For the first time, observing lithium-ion flow through a battery interface may aid in optimising material design. Exploring the world of battery nanoparticles through captivating X-ray movies has been an exciting endeavour. However, comprehending the nuanced intricacies of their functionality remained a challenge due to the overwhelming richness of information contained in these movies. Researchers at MIT, Stanford University, SLAC National Accelerator, and the Toyota Research Institute have made significant discoveries about the reactivity of lithium iron phosphate, a crucial material used in electric car batteries and other rechargeable energy storage systems, by analysing X-ray images. This approach can unveil insights into various materials, including batteries and biological procedures like embryo cell division.

A new study reveals that system deployment processes have evolved slowly, but addressing them is crucial for reducing future clean energy costs. The cost of installing a solar energy system has decreased by over 99% since 1980. However, a recent study reveals that “soft technology” aspects, like standardized permitting, supply chain strategies, and design processes for solar energy deployment, accounted for just 10 to 15% of the overall cost reduction. The majority of savings came from advancements in hardware components. Researchers at MIT have developed a numerical model to examine the cost trends of solar energy systems, considering both hardware and soft technology components. The framework indicates limited advancement in soft technology. Engineers could minimize their reliance on or optimize deployment processes to boost cost reductions.

An innovative bimanual robot demonstrates tactile precision nearly matching human skill, guided by artificial intelligence (AI) in its movements.Tactile feedback in bimanual manipulation is crucial for achieving robot skills on par with humans. Yet, this area is less investigated than single-arm scenarios, partly because of the lack of appropriate hardware and the challenges in creating efficient controllers for tasks with expansive state-action spaces. Scientists at the University of Bristol, based at the Bristol Robotics Laboratory, have designed the Bi-Touch system, which enables robots to perform manual tasks by receiving guidance from a digital assistant. The system demonstrates an AI agent utilizing tactile and proprioceptive feedback to guide robot actions for accurate sensing and practical task completion. This innovation could revolutionise sectors like fruit picking domestic aid and even emulate touch in artificial limbs.

The Industrial Internet of Things (IIoT) is exploding, resulting in the dramatic growth of remote wireless devices being powered by primary (non-rechargeable) lithium batteries. Often, these devices need to operate maintenance-free for decades without having to replace the batteries. To achieve such extended battery life, energy losses must be minimized. This is especially true for lowpower devices that draw average current measurable in micro-amps while operating mainly in an energy saving “standby” state, fully awakening only to sample or transmit data. Battery-powered wireless devices deployed in extreme environments and hard-to-access locations are generally illsuited for consumer grade alkaline cells that are extremely short-lived, primarily due to a high self-discharge rate of up to 60 percent per year. Lithium chemistries can last much longer.

A finger motion recognition system redefines human movement monitoring across various fields by tracking finger movements with 95% accuracy, even in low-light conditions. Devices capable of identifying, monitoring, and deciphering movements in their vicinity may offer invaluable possibilities in robotics, healthcare, entertainment, sports, and more. Wearable sensors are especially proficient in recognizing and logging the actions of human individuals, as they can be placed strategically and capture finger movements with heightened precision. Researchers at Ajou University, Korea University and the Korea Institute of Science and Technology (KU-KIST) have developed a new system capable of identifying the movements of a human user’s fingers in real time.

Schneider Electric announced the launch of AltivarTV Soft Starter ATS480 in the Indian market. Motors are critical assets at the core of all industrial operations. The Schneider Electric motor management system is an all-encompassing platform to unify Energy & Automation together. The system drives the complete lifecycle of a motor from design, build, operate & maintain to achieve sustainability goals in your plant, Machines & Buildings.

Siemens launches study of 1,400 executives globally revealing regional, city and industry insights regarding the infrastructure transition across energy systems, mobility and buildings. Decarbonization is a top priority within the infrastructure transition, but progress is too slow. Over 50% of executives believe decarbonization is a competitive advantage, but less than half believe their country has an effective decarbonization strategy. Technology and digitalization key levers of a successful infrastructure transition. 

FactoryTalk Twin Studio is an end-to-end automation design solution where users can design, program, simulate, emulate, and virtually commission in one cloud environment, making it easier than ever to collaborate on a common model and refine designs. On-demand digital design software enables a simpler, more efficient way to work from any web browser with software that is always up to date and flexibly scales users and compute capacity to meet project workload demands. The result is better designs that are developed quicker.

The “Lightning” system utilizes abstraction to link photons with the computer’s electronic components, establishing the inaugural photonic computing prototype capable of handling real-time machine-learning inference demands.The computing sector is at a critical juncture, with the pace of advancements as predicted by Moore’s Law diminishing, mainly due to physical and economic limitations of incorporating more transistors on affordable microchips. As the growth in computing power slows and the need for high-performance computers to support complex artificial intelligence models rises, engineers seek new avenues to boost computational capabilities. However, a straightforward solution still needs to be discovered. Photonic computing, utilizing photons generated by lasers to conduct computations in the analogue domain, emerges as a promising alternative.

Siemens introduces Industrial Operations X, an open and interoperable portfolio for automating and operating industrial production. The new portfolio is part of Siemens Xcelerator, the open digital business platform comprising a portfolio of software and connected hardware, an ecosystem of partners, and a marketplace. Industrial Operations X is the solution for production engineering, execution, and optimization in the new world of IT/OT convergence. It focuses on integrating cutting-edge IT capabilities and proven methods from software operations in the world of automation: low code, edge, cloud computing and artificial intelligence (AI) are combined with industry-leading automation technology and digital services. The result: plants and production lines become more flexible and modular so customers can react to changes at the click of a button.

Rockwell Automation introduces FactoryTalk Optix as a new addition to their visualization portfolio. Known as “visualization for visionaries,” FactoryTalk Optix is a modern, cloud-enabled human-machine interface (HMI) platform that allows users to design, test and deploy applications directly from a web browser anywhere, anytime. Take advantage of new levels of collaboration, scalability, and interoperability to achieve your vision. 

Widespread digitalization can fairly be called the driving trend for the energy industry today. The industry is facing global crises, an increase in energy consumption, and a growing need for renewable resources. To become more flexible and transparent, digital technologies, specifically the Enterprise Internet of Things (EIoT), must be used properly. One might even say that the emergence and evolvement of IoT just happened in time for the energy sector. Utilizing advanced monitoring, IoT-based automation, and predictive analytical capabilities brought by the IoT, energy operators, and utilities are likely to overcome the upcoming energy challenges more easily. In various analytical resources, EIoT solutions are increasingly presented as a must-have for energy companies. The energy sector is expected to become one of the biggest consumers of IoT edge devices, which implies a significant modernization of energy infrastructures. Moreover, industrial IoT solution for energy involves the use of asset data to enhance planning, and distribution activities, and mitigate risks of system malfunction. Thus, modernized systems are expected to become more independent regarding both energy distribution and asset maintenance. It’s precisely what can help energy companies and consumers seamlessly enter the upcoming future.

Rockwell Automation, the world’s largest company dedicated to industrial automation and digital transformation, today announced the release of a new line of Stratix switches that offer increased functionality. Stratix 5200 switches are easier to set up, configure and offer enhanced security features. New Allen‑Bradley® Stratix 5200 fully managed switches offer various hardware configurations and features. These options give machine builders more value and flexibility. Key features include. Expansion of higher port speed options with all gig SKUs. Redundant and resilient architecture options that support fully managed and high performance switch tiers. Simplified portfolio and switch selection that is streamlined for fully managed and high performance

Siemens, a leading provider of automation technology and industrial software, has launched a digital and vendor independent, cross-manufacturer App Store for industry customers. The marketplace serves as a transaction mechanism specifically for the Siemens Industrial Edge platform: an innovative IT platform which enables the scalable deployment of IT technologies and apps in the production environment. In addition to the Siemens Edge apps for the discrete and machine tool industries, third-party providers such as Braincube, Cybus, SeioTec and Tosibox have already started to list their products. Customers thus benefit from a broad range of software components, offered by numerous providers and manufacturers, which they can integrate into their manufacturing processes in a standardized manner. Today, the multifaceted offering already ranges from connectivity, data storage, visualization and analysis right up to machine monitoring, as well as energy and asset management. As an open software platform, Industrial Edge thus constitute an Edge Computing ecosystem. 

A virtual world to solve real-world problems: Siemens and NVIDIA will collaborate to enable the Industrial Metaverse and increase use of AI-driven digital twin technology that will help bring industrial automation to a new level. A real-time, immersive Industrial Metaverse connects hardware and software from the edge to the cloud and leverages the power of AI enabled, photorealistic, physics-based digital twins that drive efficiency and transform industries taking industrial automation to a new level. It also forever changes the way we collaborate – within organizations, ecosystems and between firms and their clients and customers.

Siemens Smart Infrastructure has launched Connect Box, an open and easy-to-use IoT solution designed to manage small to medium-sized buildings. The latest addition to the Siemens Xcelerator portfolio, Connect Box is a user-friendly approach for monitoring building performance, with the potential to optimize energy efficiency by up to 30 percent and to substantially improve indoor air quality in small to medium sized buildings such as schools, retail shops, apartments or small offices. Siemens Xcelerator is an open digital business platform that enables customers to accelerate their digital transformation easier, faster and at scale.

Siemens Smart Infrastructure’s smart buildings portfolio, designed to help building owners create healthy, comfortable, and sustainable buildings – whether greenfield or retrofits – has been recognized by WiredScore’s Accredited Solutions. The accreditation for smart solution providers is granted by WiredScore, a US-based company that sets a global standard for technology in the built world through certification and education.

Rockwell Automation has announced plans to collaborate with Fork Farms, a Green Bay-based agriculture technology startup company, to build a highly automated, indoor 7,300-square-foot hydroponic vertical farm within Rockwell’s Milwaukee headquarters by summer of 2024. Hydroponic farming is a technique used to grow plants without soil. Instead of planting crops in the ground, hydroponic farmers use containers filled with nutrient-rich water without the need for large plots of land.

Artificial intelligence has come a long way since scientists first wondered if machines could think. In the 20th century, the world became familiar with artificial intelligence (AI) as sci-fi robots who could think and act like humans. By the 1950s, British scientist and philosopher Alan Turing posed the question “Can machines think?” in his seminal work on computing machinery and intelligence, where he discussed creating machines that can think and make decisions the same way humans do. Although Turing’s ideas set the stage for future AI research, his ideas were ridiculed at the time. It took several decades and an immense amount of work from mathematicians and scientists to develop the field of artificial intelligence, which is formally defined as “the understanding that machines can interpret, mine, and learn from external data in a way that imitates human cognitive practices”

As industrial manufacturers pursue digital transformation, they are looking to scale digital transformation initiatives via innovations in AI, analytics, MES systems, etc., and tap real-time intelligence closer to the source of industrial data. This has created pain points around application latency, device management, and security issues – underscoring the need for a balanced edge to cloud deployment. Industrial Transformation (IX) leaders need an end-to-end, intelligent edge management solution to execute a robust edge computing strategy that can maximize overall deployment success, control devices from anywhere, and manage app deployment on devices – while improving security posture.

Schneider Electric is constantly leveraging new-age technologies to meet ever-changing customer needs. The company’s success is resonated across the world. The existing Schneider Electric Smart Factory in Hyderabad recently earned the recognition of an Advanced Lighthouse by the World Economic Forum.  Manufacturing the mission-critical products, this Hyderabad smart factory has leveraged Schneider Electric’s Fourth Industrial Revolution (4IR) based EcoStruxure Solutions backed by AI deep learning, IIOT infrastructure, and both predictive and prescriptive analytics. 

Schneider Electric, the global leader in digital transformation of energy management and next-gen automation, hosted the ground breaking ceremony of its new manufacturing plant at Prospace Industrial Park Pvt Ltd., in Kolkata. Covering an area of 9 acres, this new facility will involve an investment of INR 140 crores. Schneider Electric aims to accelerate and significantly grow its capacity in producing products such as Vacuum Interrupters (VI) and strengthen its power systems vertical through this new factory. This new facility is planned to be completed by February 2024, with the manufacturing operations expected to start in the second quarter of the next financial year.

In an unpredictable world with market demands continuously evolving, increasing productivity is a key challenge for businesses all over the world and across industries. Luckily there are ways to unlock efficiency gains and become more competitive by turning to automation. Expert guidance from industry specialists, along with insightful customer stories and product demos, can be the turning factor for businesses, both large and small, to boost productivity and thrive in an ever-changing market. That’s why Universal Robots, the world’s leading collaborative robot (cobot) company. Here, businesses will get access to exclusive insights on how automation can improve productivity, giving manufacturers the chance to explore the automation opportunities that will help unlock efficiency gains.

Honeywell will provide the largest lithium iron phosphate battery gigafactory in the U.S. with central controls and digital visualization to increase operational efficiency, enhance safety and drive more energy-efficient production. Honeywell announced that American Battery Factory (ABF), a Lithium Iron Phosphate (LFP) battery cell manufacturer, is fully integrating Honeywell's automation and process safety solutions in its new gigafactory located in Tucson, Ariz. At approximately 2 million square feet, the facility will be the largest gigafactory to produce LFP battery cells in the United States upon completion. Energy storage systems made from ABF's battery cell production will be available to households, businesses and utilities, allowing them to optimize their use of energy from the grid or from their own systems.

With an aim to empower young students, Spreading Happiness InDiya Foundation (SHIF), a non-profit collaboration between cricketing icon Sachin Ramesh Tendulkar and Schneider Electric India is on a mission to provide access to digital education in more than 150 Rural Indian schools by 2025 under the SMITA Program. Through this association, they also aim to introduce the concept of Digi Green wherein they will provide access to digital education infrastructure powered by solar energy and create awareness among the children of the country about the conservation of energy and the environment.

Machine learning and other artificial intelligence methods are gaining increasing prominence in chemistry and materials sciences, especially for materials design and discovery, and in data analysis of results generated by sensors and biosensors. In this paper, we present a perspective on this current use of machine learning, and discuss the prospects of the future impact of extending the use of machine learning to encompass knowledge discovery as an essential step towards a new paradigm of machine-generated knowledge. The reasons why results so far have been limited are given with a discussion of the limitations of machine learning in tasks requiring interpretation. Also discussed is the need to adapt the training of students and scientists in chemistry and materials sciences, to better explore the potential of artificial intelligence capabilities.

Since 2016, Universal Robots, the world’s leading collaborative robot (cobot) company, has offered robotics training to customers and others with an interest in collaborative robots. And now, after having grown increasingly popular in the past years and with 119 onsite training centers across the globe, more than 200,000 robotics enthusiasts have joined the Universal Robots Academy. “It is amazing to see that so many robotics enthusiasts from all over the world have joined us to enhance their robotic skills and benefit from the training we offer,” said Mikkel Vahl, global head of Academy and Education. “We’re a company on a mission - automation for anyone, anywhere. A key part of this is providing our customers and robotics enthusiasts with high quality training that gives them the skills they need to unleash the full potential of their cobots.”

Schneider Electric™, the global leader in the digital transformation of energy management and automation, introduces Easy UPS 3-Phase Modular. This robust uninterruptible power supply (UPS) is designed to protect critical loads while offering third-party verified Live Swap functionality. Easy UPS 3-Phase Modular available in 50-250 kW capacity with N+1 scalable configuration and supports the EcoStruxureTM architecture, which offers remote monitoring services. With scalability top of mind, Easy UPS 3-Phase Modular enables you to pay as you grow, allowing you to optimize capital investment. It is a part of Schneider Electric’s Green Premium portfolio, which ensures energy efficiency, durability, recyclability, and transparency to help reduce environmental footprint. In addition, this system features advanced technology such as a high-efficiency design, intelligent battery management, real-time monitoring, and control capabilities, making it one of the most cost-effective and energy-efficient UPS solutions available in the market.

The Industrial IoT Gateways and Routers research explores the current and future market performance and related technology and business trends and identifies leading technology suppliers. This new research is based on ARC’s industry-leading market research database, extensive primary and secondary research, and proprietary economic modelling techniques. The research includes competitive analysis, five-year market forecasts, and 5 years of historical analysis. The report segmentation includes Revenue Category, Sales Channel, World Region, Industry, Customer Type, Installation Type, Hardware by Network Type, Hardware by CPU Type, Hardware by Operating System, Hardware by Edge Computing Use, Hardware by Form Factor, Hardware by Environmental Rating, Overall Scope, Hardware by Execution Environment, Hardware by IEC / EN / UL / ATEX

A PLC can be placed in Run or Stop, or occasionally Program mode, usually through physical or virtual methods. But what do these modes mean, and when should they be used? Programming a PLC is a tricky business, no doubt about that. Setting the ladder logic, memory addressing, and communication protocols aside, there is a separate matter of actually downloading and executing the program on the CPU. While performing those steps, the PLC may toggle itself between running modes, but why do we have these different modes, and why/when would you need to manually change the mode yourself?

An overview of the theory, implementation, and reason to utilize a virtual environment in your Python scripting journey. A Python virtual environment is a compartmentalized Python environment that relies on a folder structure to deliver an isolated Python environment to develop. This is likely a similar definition you will find from many other sources on the internet. A use case really shows the benefits of this best practice when you are developing a scripting project. The recent end-to-end custom HMI development series on Control.com is a perfect example of the benefits of using a virtual environment. We required a FastAPI application, and let’s assume we used a Flask application instead of traditional HTML, CSS, and Javascript for the front-end application. Both of these Python libraries are third-party libraries, meaning that they are not installed with the base installation of Python. We would need to use the pip command line tool to install FastAPI and Flask from the Python package repository PyPi. These modules would then live in the Python/Python311/Lib/site-packages directory where you installed the root or default python binary executable python.exe on your system.

Here are some key best practices that global organizations are using to reduce cyber risks and improve operational efficiencies. An upswing of cyberattacks have occurred worldwide the past couple of years in part due to OT systems' growing connectedness to the rest of the world (and the fact that these systems have become bigger targets for bad actors.) These days, the coexistence of gauges, sensors, control systems and meters from many providers–and connected to the network–increases process efficiency yet also widens the attack surface. For an organization to succeed, it’s essential to stay ahead of developing cyber risks. CISOs at leading industrial companies must consider cybersecurity as a vital element of their plans for digital transformation. Based on what we’ve seen, there are some key best practices that global organizations are using to reduce cyber risks and improve operational efficiencies.

Everactive’s solutions will complement Rockwell Automation’s Dynamix product line, which will be ideal for Rockwell customers seeking actionable insights from their rotating equipment assets, without the concern of interruption from having to replace batteries in the process. Rockwell Automation, Inc., the world’s largest company dedicated to industrial automation and digital transformation, is pleased to announce its partnership with Santa Clara, California-based Everactive, a leader in wireless and batteryless condition-monitoring solutions for industrial customers.  

The Engineering Design Software & Building Information Modeling research explores the current and future market performance and related technology and business trends and identifies leading technology suppliers. This new research is based on ARC’s industry-leading market research database, extensive primary and secondary research, and proprietary economic modelling techniques. The research includes competitive analysis, five-year market forecasts and five years of historical analysis.  Research segmentation includes Revenue Category, Sales Channel, World Region, Customer Type, Service Revenues by Type, Deployment Model, Software Revenues by Type, Engineering Tools, Market Sector, Infrastructure & AEC/BIM, Plant Design.

Machine learning is a rapidly growing field in the oil and gas industry. It can be used to analyze seismic data, well logs and other geologic data to identify potential oil and gas reservoirs. Machine learning algorithms are also capable of analyzing production data and identifying patterns that can be used to improve well performance. Overall, machine learning has the potential to improve efficiency, increase production and reduce costs in the oil and gas industry, says GlobalData, a leading data and analytics company. GlobalData’s thematic report, “Machine Learning in Oil and Gas,” provides an overview of the machine learning technology and its growing importance in oil and gas operations. It also highlights the efforts of major oil and gas companies, such as BP, ExxonMobil, Saudi Aramco, Shell and TotalEnergies in the development and implementation of machine learning tools to address business problems.  

Zededa’s Transform Edge Computing conference highlighted why there's no place better place to compute than the cloud and how OT systems can benefit. New ways to approach operational technology (OT) digital transformation were at the heart of ZEDEDA’s third Transform Edge Computing conference in March. Some of the topics covered at Transform 2023 included new approaches to digital transformation, ways to enhance security for data and applications at the edge, and dealing with legacy software challenges, among others. Founded in 2017 to enable edge computing, ZEDEDA used this virtual event to educate its customers, partners and prospects on the Internet of Things (IoT) and cloud and edge computing.  

The CC-Link Partner Association’s (CLPA) partner, HMS Networks, announced the release of a new embedded development option for designers of industrial automation devices compatible with CC-Link IE TSN. HMS Networks’ Anybus CompactCom 40 CC-Link IE TSN embedded communication interface provides a gigabit Ethernet connection and TSN functionality in one package. With CompactCom 40 CC-Link IE TSN, device manufacturers and machine builders get a fast track to connecting their products to CC-Link IE TSN networks for future-proof applications.

The project enables compliance with cyber security regulations, extends plant lifespan, and supports the digitalization of Aboño 2 and Soto 5 power plants. ABB has won a contract for the modernization of two power plants in Spain, Aboño 2 and Soto 5, owned by EDP, a prominent player in the energy sector. The project ensures compliance with NIS2 European cyber security regulation and provides solutions for addressing potential cyber threats more effectively.

Over the past decade, AI has pushed steadily at the boundaries of the cloud computing environment in a bid to infiltrate the realm beyond. Since the introduction of Artificial Intelligence to the data center, AI has been loath to leave it. With large tracts of floorspace dedicated to servers comprising leading-edge chips that can handle the computational demands for training the latest in AI models, as well as inference via end-user connections to the cloud, data centers are the ideal environment for facilitating much of what AI has to offer. And yet, over the past decade, AI has pushed steadily at the boundaries of the cloud computing environment in a bid to infiltrate the realm beyond. The edge of the network, where users have immediate interaction with devices that do not necessarily rely on the cloud for computation, has been touted as something of the promised land for AI, where inclusion of accurate and somewhat autonomous AI - where devices are linked via Wi-Fi connectivity - would enable a true Internet of Things. This has been the expectation for the best part of the decade, with the great Edge AI takeover still forthcoming. Instead, AI has slowly trickled into certain household devices and consumer electronics goods, with other applications yet to realize the full impact that AI has promised.

Autosilicon has launched the world’s first 14-channel Battery Diagnosis IC (BDIC) for Electric Vehicles (EV) and Energy Storage Systems (ESS). The primary role of BDIC is to gauge the Electrochemical AC Impedance of battery cells. This impedance, frequency-dependent and indicative of the battery’s condition, offers precise insights into the battery’s internal state. Unlike the traditionally used expensive and space-consuming Electrochemical Impedance Spectroscopy (EIS) equipment, BDIC significantly enhances measurement accuracy, operating current, and compactness. It outperforms EIS equipment and can even replace multiple such devices with just one chip. 

The State-run Coffee Board has mooted a proposal to double India’s coffee production over the next 10 years through increasing productivity in the traditional growing states, while bringing in new areas of around 2 lakh ha under cultivation in the non-traditional regions. Coffee has traditionally been grown in Karnataka, Kerala and Tamil Nadu, while the plantation crop has been making inroads in the non-traditional regions of Andhra Pradesh, Odisha and the North East in recent years.

Coffee Board Secretary and CEO, K G Jagadeesha said the Board proposes to take a two-pronged approach to double the production by 2033. “We are looking at a National Replantation Policy for the traditional States, where coffee plants in about 60 per cent of the areas have crossed the economic age. The yields start declining in arabica plants after 40 years and in robusta after 50 years. If we don’t take up replanting in those states where it has crossed the economic age then our yields are bound to go down. We can increase almost 50 per cent of our production by replanting the old and senile plants,” Jagadeesha said.

As part of the proposal, the interest costs on the loans availed by growers to take up replanting in the traditional areas will be absorbed by the government for a period of five years, Jagadeesha said. The Board has sent the proposal to the Commerce Ministry and is awaiting for the government’s approval.

Currently, coffee is grown in about 4.72 lakh hectares and the production during 2022-23 stood at 3.60 lakh tonnes. India’s coffee output has more than doubled over the past three decades from around 1.69 lakh tonnes in the early nineties and the Board is targetting doubling the yields over the next decade through improved productivity.

Further, Jagadeesha said the proposed area expansion of about two lakh hectares in the non-traditional regions will be carried out by providing subsidies to the growers in collaboration with the respective States. The Centre proposes to share the subsidy burden equally with the States for new area expansion.

Jagadeesha said Odisha has already announced a mission to bring about 1 lakh ha under coffee by 2030. In Andhra Pradesh about 80,000 hectares would be brought under coffee, while the cultivation can be expanded to about 25,000 hectares in Tamil Nadu and another 50,000-60,000 hecatres in North East. “All these areas if we can bring under coffee, we can improve the output,” he said.

India, which exports about two thirds of the coffee produced in the country, is hosting the World Coffee Congress (WCC 2023) in Bengaluru from September 25-28 and the event is expected to showcase the Indian coffees to the global buyers.

Thanks to IoT and Industry 4.0 technologies, factories are connected workspaces, with every machine a potential data source. That’s allowing us to model, optimize, and automate workflows, respond nimbly to problems, and transform the efficiency and reliability of our facilities. The digitization of industry has allowed some facilities to ride out the pandemic relatively easily, because automated and digitized plants don’t need as much human attention. China’s silicon-chip factories were barely shaken by the crisis, for instance, because their sterile spaces require few human workers other than a handful of engineers to monitor control panels. Other manufacturers are leveraging connectivity to reduce the need for on-site customer visits. Nano-electronics specialist P2i uses machine learning to image products as they move through production lines, allowing buyers to conduct quality-control checks remotely. Manufacturing equipment can be coordinated remotely, too, so P2i’s engineers can correct errors or fine-tune processes without leaving their homes.

NDP200 Neural Decision Processor (NDP) from Syntiant Corp. has been awarded “Best Product of the Year” by the tinyML Foundation. The company claims the processor is a specialized deep-learning processor suited for constant-use scenarios in battery-powered devices. The NDP excels at the simultaneous execution of multiple applications with minimal power consumption. The processor architecture is designed for always-on-edge AI vision applications and allows complete control of multiple independent networks. It can operate concurrently at less than 2mW power with near-zero latency. This makes it suitable for tasks like person detection, object classification, motion tracking, and occupancy monitoring. The NDP200 offers precise neural processing for battery-powered consumer and commercial devices such as IP and security cameras, video doorbells, and teleconferencing equipment, among other applications. 

Broadcom has launched Film Bulk Acoustic Resonator (FBAR) integrated front-end module (FiFEM) devices for Wi-Fi access points, which the company claims ensures coexistence in the 5 GHz and 6 GHz bands while minimizing in-band insertion loss. Additionally, they significantly reduce the bill of materials (BOM) at the RF front end. The devices feature a non-linear power amplifier (PA) design, specially optimized for Broadcom’s Wi-Fi System on a Chip (SoC) Digital Predistortion (DPD) operation. This optimization allows an impressive up to 40% reduction in RF front-end power consumption. These devices are designed for Wi-Fi routers, residential gateways, and enterprise Access Points (APs). The introduction of the 6 GHz band for Wi-Fi has opened up new possibilities for businesses and consumers worldwide, offering faster speed, lower latency, and higher capacity.

Dr Santosh Pandey, naturopath and acupuncturist, Rejua Energy Centre, Mumbai said that the Sannata drink is a combination of yogurt mixed in water with asfoetida, cumin seeds, coriander, mint, and carom seeds, which resembles a thinner version of traditionally made raita. “Rock salt and black salt are also sprinkled. It should have a watery, thin consistency. This specific mixture may help calm stomach discomfort and alleviate indigestion. Since it has a cool and calming effect on the inner stomach lining, it is often consumed with and after meals in many regions”.

The global market for automation hardware is undergoing a significant change as manufacturers adopt automation solutions with more complex architectures, and software-based PLCs are an increasingly “notable vector of transformation” for the PLC market, according to a new report from global technology intelligence firm ABI Research. Makers of next-generation human-machine interfaces (HMIs), industrial personal computers (IPCs), and programmable logic controllers (PLCs) are adapting to the growing digital transformation within manufacturing. They are attempting to identify best-of-breed offerings, leverage the increasing openness of products and integrate these assets into a comprehensive overarching solution. ABI Research predicts each market will see strong growth over the next ten years with the total market forecast to increase from a current value of US$38.7 billion in 2023 to US$64 billion in 2033.

The battery monitoring IC finds versatile application examples, including voltage detection for automotive batteries, ensuring their safe and efficient operation. Moreover, by optimising their performance, the IC proves beneficial for various critical automotive components such as engines, transmissions, suspensions, and ABS. Additionally, its significance extends to Electric Vehicles (EVs), Hybrid Electric Vehicles (HEVs), and Plug-in Hybrid Electric Vehicles (PHEVs), where it plays a pivotal role in monitoring battery voltages, contributing to the overall operational integrity and safety of these vehicles.

Lucidworks, the leading search solutions provider, shared results from the largest, global generative AI benchmark study that gathered input from over 6,000 employees involved in AI technology decision-making. The study includes respondents from nearly 400 manufacturing and B2B companies worldwide. The study shows that 93% of companies across industries plan to increase their investments in AI within the next twelve months. The manufacturing industry is aligned with the same percent planning to boost generative AI investments. However, the survey found differences depending on location. 100% of Chinese and Australian manufacturing respondents said they plan to increase investment, compared to only 92% of United States participants.

Mobile robots have experienced substantial growth in the last decade due to their autonomous mobility, which has been propelled by advancements in robotics technology, autonomous navigation, and artificial intelligence. IDTechEx's market research report, titled "Mobile Robotics in Logistics, Warehousing, and Delivery 2024-2044", delves into the technical, regulatory, and market aspects influencing the emerging logistics mobile robot industry.

It gives farmers the ability to make informed decisions, maximise productivity and utilise resources efficiently. Agriculture is no exception at a time when technology is constantly redefining industries. Precision agriculture is a concept that has emerged as a result of the integration of artificial intelligence (AI) into farming techniques. This ground-breaking method uses AI to improve numerous areas of farming, increasing productivity, decreasing resource waste, and improving agricultural yields. As a result, the convergence of AI with modern agriculture provides an explanation for how precision agriculture is altering the agricultural landscape.

Achieving efficient, reliable and sustainable electric power supply requires the power industry to deploy digitalization technologies for society’s economic vitality and well-being. Spin Eng (Spin) has learned many lessons serving the generation, transmission, and distribution (GTD) electric community, creating software, collaborating with companies helping it in its digital transformation since 1992. Spin has accelerated its responsiveness to delivering solutions by standardizing on a solid software platform, empowering its subject matter experts to focus their electrical expertise on applications development.

Spin has developed a great deal of knowledge and know-how with responsive offerings for power management software solutions to manage and optimize a wide range of demanding electric power. The software must address traditional and new requirements with functions including digital hub functionality, data analytics, business intelligence, environmental, artificial intelligence, machine learning, and integrating different systems with ease. Digitalization solutions include supervisory control and data acquisition (SCADA), ADMS/FLISR (fault location isolation service restoration), renewed energy management, Industrial Internet of Things (IioT) gateways, microgrid management, control, distributed energy resource management system (DERMS), condition-based asset management, data analytics, and the like.

Digitalization enables utility competitiveness

Digitalization is essential for utilities facing growing demands and new competitors to improve efficiency flexibility and enhance their ability to react quickly to technological changes. The systems used in many companies to manage their processes result from decades of quick fixes, often poorly documented, and needing to be improved or replaced. For example, transmission and distribution utilities face difficulties launching smart grid solutions (smart meters) or integrating intermittent energy sources, such as renewables, if linked to legacy or outdated systems. Digitalization also dramatically improves the common utility industry challenge of effectively and efficiently communicating in real time with stakeholders, including the information technology (IT) department and executive board. Deploying flexible digital technologies keeps organizations ahead of competitors and improves financial health.

Core competency focus

Quickly and effectively satisfying ever-changing utility industry digitalization requirements, Spin electric utility subject matter experts (SMEs) focus on creating and adding application-specific energy software building on top of the brand labeled “Tatsoft’s FactoryStudio” software platform. This platform enables Spin Eng SMEs to develop solutions for new customer requirements with unique features and high quality. Spin’s engineering team automatically receives Tatsoft new features and updates on an ongoing basis, enhancing product functionality and simplifying software support. Spin developed its first SCADA in 1994 and was considering rewriting its solution in a new environment when it found Tatsoft’s FactoryStudio, enabling them to deliver feature-rich high-quality products to market faster.

Spin has partnered with Tatsoft since 2011 and built a product called “Action.NET,” oriented to the electrical market with solutions for GTD automation, microgrids management, ADMS/FLISR, electrical modules, and electrical protocols, among several solutions. When this partnership started, Tatsoft and Spin spent nearly one year adapting the FactoryStudio platform to energy solutions, creating new attributes and concepts, developing the communication drivers used in this vertical, standard reports, and so on.

Open platform advantage

FrameworX by Tatsoft is an open and extensible platform enabling users to create standardized, reusable, industrial applications, maximizing return on engineering, shortening project times, reducing risk, and reducing the total cost of ownership. The platform provides scalability, redundancy, and services creating a robust industrial operating system. The platform inherently supports digitalization by managing and bringing together different data sources to deliver meaningful and valuable information.

The architecture of FrameworX provides Spin with the ability to tailor offerings to customers to create custom symbols and components that encapsulated their intellectual property gained with more than 30 years in the electric utility industry. Tatsoft’s communication driver toolkit has provided the flexibility to create electric utility-specific real-time drivers, including IEC-61850, DNP 30, IEC-60870-5-104/101, and ICCP-TASE2. Using FrameworX because it is a open, robust, and flexible software gives Spin the ability to deliver an intrinsically secure supervisory control product built on the latest digitalization technology.

The Tatsoft technical team has become a collaborative resource assisting Spin to customize FactoryStudio to create Action.NET, an environment to develop electric applications satisfying customers’ specific product requirements. The collaborative development process includes brainstorming sessions, exchanging ideas, and exploring the standard FrameworX functionality and the product’s capability to be easily customized in many different ways, including:

• Custom symbols and complex reusable components.

• Auto-creation of tags, alarms, device points, historian and graphics.

• A software framework to enable their real-time industry-specific analytics.

• Driver toolkit to develop new protocols for their product.

Spin also takes advantage of all of the built-in standard functionalities native to FrameworX. These advanced functionalities provide their product leading capability and require no further development, such as:

• Mobile and HTML5 displays.

• Integrated high-performance databases like Canary, OSIsoft PI System, and all relational databases.

• Integrates built-in libraries oriented to electrical market like OpenDSS.

• Supports scripting in C#, VB.NET, JavaScript, and Python.

• Built-In robust project management and audit trails.

• Security and high availability.

Power management integrated circuit revolutionises energy harvesting for IoT and embedded systems. Compact, high-efficiency design reduces waste and expands applications, making eco-friendly electronics a reality. Nexperia has launched NEH2000BY, a high-performance power management integrated circuit (PMIC), to simplify and enhance the performance of low-power applications, including Internet of Things (IoT) devices and embedded systems. The company claims the PMIC is designed to recharge batteries or storage capacitors using harvested energy from surrounding sources, such as ambient light harnessed through photovoltaic cells. This energy harvesting solution also holds the potential to curb the environmental impact caused by the enormous number of batteries produced and discarded annually. The PMIC revolutionises energy harvesting solutions, offering a remarkable advantage over competing offerings by allowing designs up to 20 times smaller without manual optimisation for specific applications. 

Blues has introduced Scoop, a capacitor-based backup power add-on board. Unlike Lithium Polymer batteries (LiPOs), this capacitor functions across broader temperature ranges, boasts a lower self-discharge rate, and sidesteps the shipping restrictions associated with lithium batteries. The Scoop is engineered to ensure consistent power delivery over brief intervals. It features Japan Solderless Terminal (JST) battery connectors tailored for Adafruit-compatible packs and includes a J1 charge connector. The output voltage varies between 2.5 to 3.8 volts and comes with four sets of male header pins mounted through-hole. A standout advantage of Scoop is its power outage detection capability. This is vital for essential operations where any power interruption can cause significant disruptions. Using Scoop, your IoT device can transition to an alternative power source, recognize the energy shift, and promptly notify you of the power disruption.

Micron Technology has launched the industry’s first 8-high 24GB HBM3 Gen2 memory, featuring bandwidth exceeding 1.2TB/s and pin speed surpassing 9.2Gb/s. This marks a notable enhancement of up to 50% compared to presently available solutions. With a 2.5-fold increase in performance per watt compared to earlier iterations, the HBM3 sets new records for critical artificial intelligence (AI) data centre measures such as performance, capacity, and power efficiency. These enhancements expedite training extensive language models such as GPT-4, optimising AI inference infrastructure and establishing an unparalleled total cost of ownership (TCO) superiority. The high-bandwidth memory (HBM) solution is rooted in its 1β Dynamic Random-Access Memory (DRAM) process node. This allows the assembly of a 24Gb DRAM die into an 8-high cube, fitting standard package dimensions.

As per the latest development, the beaches of the Andaman and Nicobar Islands are all set to become more lively with beach shacks. Reports add that the administration is planning to give permits for more than 150 shacks to be set up on selected beaches. This move is supposedly backed by the administration that came up with the plan based on feedback from tourists, who suggested that the scenic beaches of the islands are not as lively as they expect after sunset. Currently, tourists are not allowed to venture close to the sea at night on most of the beaches of the island...

In recent years, there has been a growing push towards mindfulness to beat stress and lifestyle issues. Now a new position paper published in the Journal of Hypertension indicates that meditation for 45 minutes every day can beat stress-related high blood pressure or BP. According to the International Society of Hypertension, lifestyle management of hypertension also involves cutting down on salt and quitting smoking. Let’s delve deep into how stress and high blood pressure are interrelated. According to the International Society of Hypertension guidelines, hypertension is defined as persistently elevated systolic blood pressure (SBP) >140 mmHg and/or diastolic blood pressure (DBP) at least 90 mmHg. It affects over 1.5 billion people worldwide, it notes.

The Coffee Board has proposed a national replantation policy for the traditional coffee-growing States of Karnataka, Kerala and Tamil Nadu. The Indian Institute of Technology (IIT) Kharagpur is developing a mechanised coffee harvester, which can reduce labour dependence by about 50-60 per cent, said Coffee Board CEO and Secretary, K G Jagadeesha. Addressing the members of United Planters Association of South India (Upasi) in Coonoor, Jagadeesha said the prototype of the harvesting machine is customised to the Indian conditions.

Manufacturing experts from across the globe congregated at the Alfândega Congress Center, a unique venue nearby Douro river, for Critical Manufacturing's MES & Industry 4.0 International Summit (MESI 4.0 Summit). This highly anticipated two-day event served as a platform for professionals to exchange valuable insights and knowledge regarding the digital transformation in the manufacturing sector. With attendees hailing from various corners of the world, the MESI 4.0 Summit proved to be an epic gathering of industry leaders, fostering collaboration and driving innovation in the realm of manufacturing.

The digital transformation can only become reality with networked systems, end-to-end data and close cooperation between manufacturers. To this end, EPLAN founded the Partner Network in late 2020, which currently has 65 manufacturers as members. One of the newer additions is the international technology company Dassault Systèmes, which joined the network as a partner in April of this year. The interface between the EPLAN Platform and the 3DEXPERIENCE platform of Dassault Systèmes will thereby be systematically expanded by both parties working together.

With attendees hailing from various corners of the world, the MESI 4.0 Summit proved to be an epic gathering of industry leaders, fostering collaboration and driving innovation in the realm of manufacturing.

During an eventful week, manufacturing experts from across the globe congregated at the Alfândega Congress Center, a unique venue nearby Douro river, for Critical Manufacturing's MES & Industry 4.0 International Summit (MESI 4.0 Summit).

This highly anticipated two-day event served as a platform for professionals to exchange valuable insights and knowledge regarding the digital transformation in the manufacturing sector. With attendees hailing from various corners of the world, the MESI 4.0 Summit proved to be an epic gathering of industry leaders, fostering collaboration and driving innovation in the realm of manufacturing.

The MESI 4.0 Summit was an exemplary event that sought to unlock the vast potential of digital transformation and propel the trajectory of growth. With its focus on the convergence of manufacturing execution systems and the fourth industrial revolution, this summit served as a platform for the dissemination of cutting-edge knowledge and the exploration of innovative strategies. Through the lens of this summit, participants were able to harness the power of digitalization and embrace the paradigm shift that is reshaping the industrial landscape.

“The industry is eagerly embracing the concept of industry 4.0 and demonstrating an insatiable appetite for knowledge regarding the ways in which digital transformation can be leveraged for their benefit. We found great satisfaction in the fact that a multitude of individuals chose to accompany us at this event,” said Critical Manufacturing CEO Francisco Almada Lobo.  “Our company specializes in driving business value through the convergence of intelligence, operations and automation technologies for Industry 4.0.  By harnessing these cutting-edge technologies, we help businesses optimize their operations, increase efficiency and stay ahead in the ever-evolving digital landscape. Our expertise lies in integrating intelligent systems, leveraging data analytics and implementing automation solutions to streamline processes, improve decision-making and drive innovation. With our comprehensive approach, businesses can unlock the full potential of Industry 4.0, driving growth, competitiveness and success in today's fast-paced and interconnected world.” 

Mário Campolargo, the esteemed Secretary of State for Digitalization and Administrative Modernization in Portugal, commenced the two-day affair by eloquently expounding upon Portugal's illustrious digital odyssey and the profound global implications of the renowned Industry 4.0.

Keynote speaker Jeff Winter shared his experiences as Sr. Director of Industry Strategy, Manufacturing for Hitachi Solutions, and Strategic Advisor for IIoT World, “Industry 4.0 has advanced well beyond the buzzword stage. We are seeing real value for sure, but everyone’s digital transformation journey is unique. That’s why it is so important for the people from the shopfloor to the C-suite to come together in conferences such as this,” said Winter.

The majority of the presenters at the summit consisted of MES end users, exemplified by the likes of Michael Kaiser, who holds the position of Lead Smart Manufacturing for B. Braun.  Mr. Kaiser's captivating discourse revolved around the topic of "Pretending to be Agile - Implementation of MES in a Regulated Environment."

“We are very excited about what we have been able to accomplish in deploying MES. We enjoyed presenting our experiences to colleagues in medical device manufacturing as well as other industries. We learn so much from each other.” said Michael Kaiser.

To become more flexible and transparent, digital technologies, specifically the Enterprise Internet of Things (EIoT), must be used properly. Widespread digitalization can fairly be called the driving trend for the energy industry today. The industry is facing global crises, an increase in energy consumption, and a growing need for renewable resources. To become more flexible and transparent, digital technologies, specifically the Enterprise Internet of Things (EIoT), must be used properly. One might even say that the emergence and evolvement of IoT just happened in time for the energy sector. Utilizing advanced monitoring, IoT-based automation, and predictive analytical capabilities brought by the IoT, energy operators, and utilities are likely to overcome the upcoming energy challenges more easily.

In various analytical resources, EIoT solutions are increasingly presented as a must-have for energy companies. The energy sector is expected to become one of the biggest consumers of IoT edge devices, which implies a significant modernization of energy infrastructures. Moreover, industrial IoT solution for energy involves the use of asset data to enhance planning, and distribution activities, and mitigate risks of system malfunction. Thus, modernized systems are expected to become more independent regarding both energy distribution and asset maintenance. It’s precisely what can help energy companies and consumers seamlessly enter the upcoming future.

Why enterprise Internet of Things is a hot topic for the energy industry

The Internet of Things is a concept centered on gaining real-time data from assets, processing and analyzing it to forecast the system’s behavior. In energy, where outages are critical, IoT provides reliable management in the following way:

IoT sensors collect data on the consumption of electricity, water, heat, etc.; measure pressure, temperature and other parameters to control the status of assets, as well as detect leakages.

Actuators connected to IoT sensors automatically shut off and resume the supply of gas, water, heat and so on.

Cloud connectivity simplifies the monitoring of energy infrastructure objects, and management of energy distribution and increases overall transparency.

AI applications for predictive analytics to implement condition-based and predictive maintenance of pipes, electrical installations, wind turbines and other energy equipment.

Implementation of IoT solutions requires modernization and expansion of the existing infrastructure, as well as a new approach to its maintenance. The ultimate challenge for energy operators and utilities is to build a reliable network in compliance with conditions of use, and KPIs they set. It requires sensors to be resilient enough to withstand the environmental conditions, deciding the processing center to be local or virtual, reliable IoT communication protocols and network topology, and custom apps that consider an optimal set of factors to create precise analytical models. Additionally, IoT-based energy infrastructure should have the potential to be expanded.

Thus, when implementing IoT solutions, the energy network turns into an intelligent energy network, which can bring enhanced efficiency for consumption management, energy distribution, maintenance processes, and immediate reaction to issues. Fortunately, developments in edge computing, embedded systems and AI fields allow enterprises to create affordable custom EIoT solutions with ROI expected soon.

How Enterprise Internet of Things promotes trends in energy sector

Enhanced management of renewable energy

The green energy vector has strengthened so much that the concept of “smart” energy became frequently associated with the concept of “renewable” energy. This can be explained by the fact that the future of renewable energy seems in close conjunction with Enterprise IoT solutions. First, advanced IoT monitoring is indispensable when considering infrastructure for renewable energy stations to be expanded; and second, it can provide highly efficient solutions for energy storage and management, eliminating the need for renewable energy to be immediately utilized.  

Enterprise Internet of Things promotes the surge in the effectiveness of green energy at all stages of its lifecycle–from generation to final utilization. For instance, by equipping solar panels with IoT sensors, the smart system can determine optimal conditions for energy generation. After transferring data to the edge or cloud processing server, the system can automatically reorient to generate energy most effectively. Using the same principle, it can detect unexpected changes in wind patterns and can automatically adjust the orientation of wind turbines.

Regarding energy management, an efficient solution can be found in IoT-enabled energy storage. They allow for the collection, storage and distribution of energy locally or from a central node. Energy in such storage can be utilized as a main or reserve energy source since IoT software platforms allow for comprehensive monitoring of various connected assets. Energy providers can search for the optimal combination of renewables with fossils soon. Thus, the IoT ecosystem can provide reliable support over the transition period allowing companies to utilize several energy types simultaneously.

Decentralized electricity generation

This item naturally follows the previous one since the shift to renewables invariably leads to an increase of loads to electricity networks. In the next 10 years, we expect a natural growth of electricity demand, and therefore the need for electricity grid expansion. However, this is not always physically possible and economically justified. Decentralized generation, storage and distribution of energy seem to become a workable solution here, which can be smoothly implemented with the help of Enterprise Internet of Things solutions. It is important to have accurate information about the amount of energy being produced and consumed to effectively manage and balance energy supply and demand. Also, decentralization can be supported when implementing IoT automation solutions for some energy management processes.

To minimize risks of outages, increase reliability and rationally balance supply and demand for electricity, IoT-enabled energy storage systems are also helpful. IoT components, including reliable cellular networks, allow for flawless access to the cloud for remote tracking control and troubleshooting of such systems. Second, it allows control conditions for the storage system itself. For example, by controlling the temperature and humidity of the storage system and collecting data on battery status and its charging mode, the system lifecycle can be significantly extended.

Thus, it makes renewable energy a controlled resource.

Predictive analytics through Enterprise Internet of Things

Predictive analytics is exactly the tool that helps energy companies be proactive in the face of problems such as power outages, outdated equipment, unusual spikes in energy use, etc. Being proactive allows for more efficient and fast decision-making in emergencies, such as hurricanes, and forest fires, as well as greater transparency and control for the key infrastructure. Both Condition-based maintenance and IoT predictive maintenance approaches are applicable here thanks to real-time data collection. To illustrate, pressure sensors can identify potential leaks, while vibration monitoring can detect equipment misalignment or imbalance that can result in energy waste.

In Predictive Maintenance (PdM) applications, Artificial Intelligence (AI) and Machine Learning (ML) algorithms should study the behavior of the asset considering various exploitation conditions. To create an AI-based model, historical maintenance data is a crucial component that helps identify significant maintenance patterns. Thus, predictive models can be developed for the whole systems, mission-critical and non-critical assets, including valves, pipes, motors, pumps, compressors and so on.

Enterprise Internet of Things for energy sector: Summing up

Already, the Internet of Things allows the implementation of digital solutions that can not only significantly increase efficiency but prepare the industry for future changes. [MC1] In the context of energy trends, the Internet of Things enables the following:

• IoT monitoring capabilities allow getting real-time data on every energy asset for all interested parties.

• Predictive analytics only gain momentum, offering more affordable solutions, and more precise predictions of the system behavior.

• IoT-enabled energy storage allows affordable, simple management of renewables as a primary and reserve energy source, as well as management of various energy types simultaneously to regulate supply and demand.

• IoT solutions support the trend toward decentralization of energy generation, storage and distribution thanks to IoT-based automation, enhanced management and analytics capabilities.

The technological transformation of the agriculture industry over the past decade has been monumental. It has aided in revolutionising the farming sector. Smart farming, also referred to as precision agriculture, was the phenomenon that brought the transformative transition leveraged with the application of cutting-edge technology such as AI-driven drones, IoT, and other data-driven solutions that catapulted productivity in farming and enhanced efficiency in agriculture simultaneously. The integration of technology in agriculture was the vital driver for the development of smart farming solutions that helped empower farmers to make better-informed.

Only when the challenges of data accessibility and expensive computing power were mitigated did the AI field experience exponential growth. There are now more than a dozen types of AI being advanced. This feature originally appeared in InTech magazine's August issue, a special edition from ISA's Smart Manufacturing and IIoT Division. Artificial intelligence has come a long way since scientists first wondered if machines could think.

In the 20th century, the world became familiar with artificial intelligence (AI) as sci-fi robots who could think and act like humans. By the 1950s, British scientist and philosopher Alan Turing posed the question “Can machines think?” in his seminal work on computing machinery and intelligence, where he discussed creating machines that can think and make decisions the same way humans do (Reference 1). Although Turing’s ideas set the stage for future AI research, his ideas were ridiculed at the time. It took several decades and an immense amount of work from mathematicians and scientists to develop the field of artificial intelligence, which is formally defined as “the understanding that machines can interpret, mine, and learn from external data in a way that imitates human cognitive practices” (Reference 2).

Even though scientists were becoming more accustomed to the idea of AI, data accessibility and expensive computing power hindered its growth. Only when these challenges were mitigated after several “AI winters” (with limited advances in the field) did the AI field experience exponential growth. There are now more than a dozen types of AI being advanced

Due to the accelerated popularity of AI in the 2010s, venture capital funding flooded into a large number of startups focused on machine learning (ML). This technology centers on continuously learning algorithms that make decisions or identify patterns. For example, the YouTube algorithm may recommend less relevant videos at first, but over time it learns to recommend better targeted videos based on the user’s previously watched videos.

The three main types of ML are supervised, unsupervised, and reinforcement learning. Supervised learning refers to an algorithm finding the relationship between a set of input variables and known labeled output variable(s), so it can make predictions about new input data. Unsupervised learning refers to the task of intelligently identifying patterns and categories from unlabeled data and organizing it in a way that makes it easier to discover insights. Lastly, reinforcement learning refers to intelligent agents that take actions in a defined environment based on a certain set of reward functions.

Deep learning, a subset of ML, had numerous ground-breaking advances throughout the 2010s. Similar to the connections between the nervous system cells in the brain, neural networks consist of several thousand to a million hidden nodes and connections. Each node acts as a mathematical function, which, when combined, can solve extremely complex problems like image classification, translation, and text generation.

Impact of artificial intelligence

Human lifestyle and productivity have drastically improved with the advances in artificial intelligence. Health care, for example, has seen immense AI adoption with robotic surgeries, vaccine development, genome sequencing, etc. (Reference 5). So far, the adoption in manufacturing and agriculture has been slow, but these industries have immense untapped AI possibilities (Reference 6). According to a recent article published by Deloitte, the manufacturing industry has high hopes for AI because the annual data generated in this industry is thought to be around 1,800 petabytes (Reference 7). 

This proliferation in data, if properly managed, essentially acts as a “fuel” that drives advanced analytical solutions that can be used for the following (Reference 8):

• becoming more agile and disruptive by learning trends about customers and the industry ahead of competitors

• saving costs through process automation

• improving efficiency by identifying processes’ bottlenecks

• enhancing customer experience by analyzing human behavior

• making informed business decisions, such as targeted advertising and communication (Reference 9).

Ultimately, AI and advanced analytics can augment humans to help mitigate repetitive and sometimes even dangerous tasks while increasing focus on endeavors that drive high value. AI is not a far-fetched concept; it is already here, and it is having a substantial impact in a wide range of industries. Finance, national security, health care, criminal justice, transportation, and smart cities are examples of this.

AI adoption has been steadily increasing. Companies are reporting 56 percent adoption in 2021, an uptick of 6 percent compared to 2020 (Reference 10). With the technology becoming more mainstream, the trends of achieving solutions that emphasize “explainability,” accessibility, data quality, and privacy are amplified.

“Explainability” drives trust: To keep up with the continuous demand of more accurate AI models, hard-to-explain (black-box) models are used. Not being able to explain these models makes it difficult to achieve user trust and to pinpoint problems (bias, parameters, etc.), which can result in unreliable models that are difficult to scale. Due to these concerns, the industry is adopting more explainable artificial intelligence (XAI). 

According to IBM, XAI is a set of processes and methods that allows human users to comprehend and trust the ML algorithm’s outputs (Reference 11). Additionally, explainability can increase accountability and governance.

Increasing AI accessibility: The “productization” of cloud computing for ML has taken the large compute resources and models, once reserved only for big tech companies, and put them in the hands of individual consumers and smaller organizations. This drastic shift in accessibility has fueled further innovation in the field. Now, consumers and enterprises of all sizes can reap the benefits of:

• pretrained models (GPT3, YOLO, CoCa [finetuned])

• building models that are no-code/low-code solutions (Azure’s ML Studio)

• serverless architecture (hosting company manages the server upkeep)

• instantly spinning up more memory or compute power when needed 

• improved elasticity and scalability.

Data mindset shift: Historically, model-centric ML development, i.e., “keeping the data fixed and iterating over the model and its parameters to improve performances” (Reference 12), has been the typical approach. Unfortunately, the performance of a model is only as good as the data used to train it. Although there is no scarcity of data, high-performing models require accurate, properly labeled, and representative datasets. This concept has shifted the mindset from model-centric development toward data-centric development—“when you systematically change or enhance your datasets to improve the performance of the model” (Reference 12).

An example of how to improve data quality is to create descriptive labeling guidelines to mitigate recall bias when using data labeling companies like AWS’ Mechanical Turk. Additionally, responsible AI frameworks should be in place to ensure data governance, security and privacy, fairness, and inclusiveness.

Data privacy through federated learning: The importance of data privacy has not only forged the path to new laws (e.g., GDPR and CCPA), but also new technologies. Federated learning enables ML models to be trained using decentralized datasets without exchanging the training data. Personal data remains in local sites, reducing the possibility of personal data breaches.

Additionally, the raw data does not need to be transferred, which helps make predictions in real time. For example “Google uses federated learning to improve on-device machine learning models like ‘Hey Google’ in Google Assistant, which allows users to issue voice commands” (Reference 13).

AI in smart factories

Maintenance, demand forecasting, and quality control are processes that can be optimized through the use of artificial intelligence. To achieve these use cases, data is ingested from smart interconnected devices and/or systems such as SCADA, MES, ERP, QMS, and CMMS. This data is brought into machine learning algorithms on the cloud or on the edge to deliver actionable insights. According to IoT Analytics (Reference 14), the top AI applications are:

• predictive maintenance (22.2%)

• quality inspection and assurance (19.7%)

• manufacturing process optimization (13%)

• supply chain optimization (11.5%)

• AI-driven cybersecurity and privacy (6.6%)

• automated physical security (6.5%)

• resource optimization (4.8%)

• autonomous resource exploration (3.8%)

• automated data management (2.9%)

• AI-driven research and development (2.1%)

• smart assistant (1.6%)

• other (5.2%).

Vision-based AI systems and robotics have helped develop automated inspection solutions for machines. These automated systems have not only been proven to save human lives but have radically reduced inspection times. There have been significant examples where AI has outperformed humans, and it is a safe bet to conclude that several AI applications enable humans to make informed and quick decisions (Reference 15). 

Given the myriad additional AI applications in manufacturing, we cannot cover them all. But a good example to delve deeper into is predictive maintenance, because it has such a large effect on industry. 

Generally, maintenance follows one of four approaches: reactive, or fix what is broken; planned, or scheduled maintenance activities; proactive, or defect elimination to improve performance; and predictive, which uses advanced analytics and sensing data to predict machine reliability.

Predictive maintenance can help flag anomalies, anticipate remaining useful life, and provide mitigations or maintenance (Reference 17). Compared to the simple corrective or condition-based nature of the first three maintenance approaches, predictive maintenance is preventive and takes into account more complex, dynamic patterns. It can also adapt its predictions over time as the environment changes. Once accurate failure models are built, companies can build mathematical models to reduce costs and choose the best maintenance schedules based on production timelines, team bandwidth, replacement piece availability and other factors.

Bombardier, an aircraft manufacturer, has adopted AI techniques to predict the demand of its aircraft parts based on input features (i.e., flight activity ) to optimize its inventory management (Reference 18). 

This example and others show how advances in AI depend on advances associated with other Industry 4.0 technologies, including cloud and edge computing, advanced sensing and data gathering, and wired and wireless networking.

Way back in 1978, when talking about a library was rare in Punjab’s villages, the residents of Jeeda, a village in Bathinda district, decided to construct a small library on a 100 square yard plot. This plot was a part of the village’s common land, and the library was created with voluntary funds. The library comprised one big hall and an adjoining room. “We started it with few books on the Communist movement, novels by Punjabi writers and four-five newspapers so that people could gather at one place and develop a love for reading. The library was inaugurated on January 8, 1978, with many villagers being a part of the endeavour and a few books being added from time to time. Recently demolished and reconstructed to have more space for public.

Laura Barajas, a 40-year-old woman from California, lost all four of her limbs after consuming undercooked tilapia fish that was contaminated with a deadly bacteria. She recently underwent life-saving surgery after a month-long stay in the hospital. “It’s just been really heavy on all of us. It’s terrible. This could’ve happened to any of us,” Barajas’ friend Anna Messina was quoted as saying by KRON. “She almost lost her life. She was on a respirator.” Messina revealed that Barajas became sick days after eating the fish that she had purchased at a local market in San Jose and prepared it at home. “They put her into a medically induced coma. Her fingers were black, her feet were black her bottom lip was black. She had complete sepsis and her kidneys were failing,” she added.

Siemens AG and DMG MORI, global leaders in automation and precision machine tools, are offering an industry-defining digital twin for machine tool processing on the Siemens Xcelerator Marketplace. Based on the Digital Native CNC (Computerized Numerical Control) Sinumerik One, this offering encompasses a digital twin of the control system, the machine tool and the workpiece, enabling breakthrough improvements in sustainability, flexibility and time-to-market. CNC SINUMERIK ONE is the leading-edge control system responsible for machining with machine tools. DMG MORI and Siemens are presenting the first end-to-end digital twin for machine tool machining on Siemens Xcelerator.

The 3rd Industrial Revolution has left a rich legacy of highly automated industrial machinery that forms the basis of any significant production. However, today digital has taken a dominant position, and many factories are puzzled about what to do with all this "wealth?" Due to the fact that manufacturing and IT systems have historically developed in parallel, they cannot communicate by default, which forces companies to struggle with compatibility issues when performing digital transformation. As a result of these struggles, integration costs in such cases might be up to 50% of the total cost of enterprise IoT solutions. In the wrong hands, once prosperous factories become a burden that only prevents the transition of production to digital rails. Thus, is it really worth it?

Energy-efficient technology enables businesses to save money and reduce emissions–without compromising on quality or output. Motor and drive technology has improved rapidly, meaning that businesses can save money and cut emissions by replacing existing units before they reach the end of their life. Electric motors are essential to industry, powering fans, pumps, compressors, conveyors and other machinery – but they consume a huge amount of electricity. Around 45% of all electricity generated goes to powering electric motors and the systems they drive–and the number of industrial electric motors in use is expected to double by 2040.