Top 5 disruptive trends in Industrial Automation.
In the last two decades, technology has massively evolved, thereby transforming the nature of industrial and manufacturing operations. Indeed, industries are no longer entirely dependent on a human workforce for simple or complex tasks. With digitalization and IoT, Industrial Automation has become a competitive advantage in today’s industrial landscape.
When it comes to focusing on technology, we are constantly searching for what are called disruptive innovations. These are the kind of trends that will create entire new markets and, in most cases, displace others in the process. The kinds of trends which will boost the economy and help people understand where the future lies. Only when the value of a technology surpasses the value of a competing market, it officially becomes a disruptive trend. With rapid development in AI and robotics technology, automation is at a tipping point. Automation is a field which is predicted to do very well in the future. This is the reason as to why the world is moving into automation. Today, robots can perform a great deal of functions without considerable human intervention. Automated technologies are not only executing iterative tasks, but also enhancing workforce capabilities significantly. In fact, automated machines are expected and are on course to replace almost half of the global workforce. Multiple industries, ranging from manufacturing to banking, are adopting automation to drive productivity, safety, profitability, and quality. Automation will bolster connectivity and reliability during a hyper-competitive ecosystem. The future of automation thus looks promising where everything will be made accessible and easily available. Given below are a few disruptive trends that could come up in Industrial Automation:
Here are the top 5 future trends in industrial automation :
1) Improving accuracy with machine learning
Machine learning has revolutionized some fields of technology in the past few years and is going to touch many others in the near future. One of these could be industrial automation, though traditionally a reluctant environment to adopt new technologies. There are indeed a few areas where machine learning could bring improvements in the world of control systems. Here we try to identify and describe a couple, and see how likely it is for them to break through industrial automation.
A. Machine Vision
Vision is the jewel of machine learning: it is the area where the most stunning applications have found place. Vision in industrial automation is not nearly as widespread as it is in the mass consumer market, probably because traditional approaches were not robust enough for the industrial requirements. Convolutional networks are a lot more flexible and robust. In addition, what’s best is that these networks can be trained offline on simulated and augmented data, with no fine-tuning required at the machine. You can easily generate a massive database to train an FCN(Fully Convolutional Network), so that a manipulator can pick different objects off a conveyor. The uniform background colors of a conveyor or a pallet make it all much easier than, say, road-detection for self-driving cars. While training an entire deep network can seem intimidating, transfer learning reduces training time dramatically. When training CNNs we are left only with the fully connected heads (either for classification or regression or both) to be tuned. When working with FCNs we only need to worry about the decoder part. Machine vision will quickly find its way in machine and factory automation in the fields of packaging, logistics, sorting, AGVs control and much more.
B. Model Optimization
Optimization is another area where the introduction of ML techniques can seriously revolutionize the field. All industrial machines and processes are driven by controllers: from the simplest PID controller, to the addition of feed-forward models, to MPC techniques, to other more exotic non-linear models. All these controllers need to be parameterized, either manually or automatically through auto-tuning identification techniques. And here comes the problem: most of the controllers deployed in the field are sub-optimally tuned. Machine learning solves the optimization of a control strategy using reinforcement learning techniques. The nomenclature is slightly different (the model becomes a policy, the input is an observation, the output an action, the feedback a reward), but the concept is absolutely the same.
C. Control
Industrial robots are very easy to simulate, they can also run without wasting any byproduct, and they provide a great wealth of data. Motion trajectory generation is a potential case. But the same could be said for a wind turbine park, where a specific strategy to reduce the mutual disturbances between adjacent turbines could be studied. We could discover a way to align the individual yaw angles in order to maximize the overall park’s output power generation, instead of simply maximizing the individual turbine’s margin without considering the interactions between them. The outcome would be very profitable. Actually, trying to learn a new control strategy from scratch is not necessarily a bad idea also for those tasks for which we already have some sort of working controller. Previous knowledge during a learning process is often a double-edged sword. It gives a strong initial kick, but not always necessarily in the right direction. And once learned, certain habits are hard to forget. So learning from scratch, while coming at extra initial expenses, might provide the freedom to discover entirely new strategies far superior to the original controller.
2) Digital twins and industrial automation
Industrial automation is undergoing a digital revolution as ‘Industry 4.0’ and Digital Twin technologies introduce new and innovative ways to execute manufacturing processes. Among these technologies, Digital Twin is changing how industrial manufacturers approach product design, operations, and post-sale services. “Digital Twin solutions open new opportunities for industrial firms to speed up their Big Data- and AI-driven innovation transformation efforts,” Forrester reports. A combination of vendor marketing and education — along with real-world benefits realized in Digital Twin use cases — are driving Digital Twin adoption among industrial manufacturers as they become an integral part of companies’ IoT and digital strategy investments.
Digital Twins have proven especially useful when integrated with IoT systems in industrial manufacturing. “However, manufacturers of IoT-connected products are the most progressive, as the opportunity to differentiate their product and establish new service and revenue streams is a clear business driver.” This is especially critical as IoT becomes more commonplace in industrial manufacturing environments. IoT sensors are already collecting data on real-world machines for analysis in cloud environments — building a congruous Digital Twin with each machine is a cost-effective addition to this already connected model, and can only help manufacturers improve machines and processes in the supply chain.
“The Digital Twin is not a new idea: Designers and makers of machines have long recognized the value of being able to simulate those machines on a computer screen. But growing enthusiasm for connecting machines to the Internet of Things (IoT), and for delivering services instead of only selling products, means that the Digital Twin is becoming relevant to a broader audience.”
In this way, Digital Twins significantly reduce the cost and work required to maintain and enhance physical things. AI, machine learning, and deep learning allow for increased automation in these processes as well. Manufacturers can introduce new concepts in digital environments, then count on digital tools to both optimize them and execute them among their physical counterparts.
Optimizing Digital Twin with manufacturing intelligence
“Rapidly expanding Internet of Things and analytics programs powered by machine learning and artificial intelligence — as well as improving models and simulations — mean that Digital Twins are coming to fill a number of roles. And they’re going to make life better.”
Industrial manufacturers have already identified dozens of successful use cases for automated and BI tools like machine learning, AI, and deep learning systems.
3) Advances in industrial cybersecurity
Over the past decade, the rise in cyber-attacks on critical infrastructure has resulted in cyber security becoming a key concern amongst the users and vendors of industrial automation control systems. Fortunately, advances in industrial cybersecurity management is helping to address the crucial requirements of industrial automation applications, equipment and plants as these relate to stringent constraints on network communications and system updates.
Cyber Security Market: Key Drivers and Trends
- With the increasing adoption of mobile devices, such as mobile phones, laptops, and tablets, the need for cyber security solutions is increasing.
- One of the major reasons for this is the increasing access to the Internet through such devices, which is increasing the chances of cyber threats.
- The use of mobile devices for personal and professional use is increasing access to critical data and information, which will increase the chance of unauthorized access in case of a stolen mobile device.
- With technological advances, the acceptance of mobile devices for m-commerce, bill payment, and GPS is also increasing.
- The confidential information accessed by mobile devices requires high-level security against hacking.
- There is a high demand for advanced cyber security solutions.
- With advances in technology, vendors are introducing firewalls with advanced capabilities such as intrusion prevention, blacklists, reputation feeds, and URL filtering.
- This helps in leveraging threat detection and provides an enhanced opportunity to protect the network.
- The deception responses can be generated by a firewall or by leveraging integration with deception providers that specialize in emulating services, or by providing deception hosts designed specifically to be attacked.
- The key to successful deceptions is believability. Therefore, firewall policies would need to be constructed in a way to align the real services with deceptive services within the deception providers’ emulated services (for instance, deception mappings).
The advanced industrial cybersecurity solutions available today take a very effective hybrid approach. Ergo, this includes both behavior-based anomaly detection that helps tos identify would-be cyber threats using conventional cybersecurity approaches, and rules-based analysis that allows manufacturers to leverage deep inspection in order to uncover malware cyberattacks on the network.
With the Digital revolution around all businesses, small or large, corporates, organizations and even governments are relying on computerized systems to manage their day-to-day activities and thus making cybersecurity a primary goal to safeguard data from various online attacks or any unauthorized access. Continuous change in technologies also implies a parallel shift in cybersecurity trends as news of data breach, ransomware and hacks become the norms.
Here are the top cybersecurity trends for 2022:
- Potential Of artificial intelligence (AI)
- Rise of automotive hacking
- IOT with 5G network: The new era of technology and risks
- Data Breaches: Prime target
4) Virtual reality and augmented reality
Today, Augmented Reality (AR) and Virtual Reality (VR) are used in a few contexts from consumer applications to production. However, at the end, when AR offers large amounts in thousands of forms, it is combined with various other technologies. Indeed, VR and AR technologies are transforming complex production processes and product development.
These terms are understood in the fields of sports, medicine, entertainment, and education. A simple example of a child playing a video game that appears in a shopping mall. All his actions imitate the game. AR and VR are the ideas of combining computer-generated visual images with real-time world data. Both are similar in theory but with significant differences. In the case of industrial automation and production, VR can help producers simulate a product or environment digitally. Therefore, to allow them to participate and immerse themselves in it. AR assists industrial users to produce digital products or information in the real world. This is more productive than projecting in a digital simulation environment like VR.
Virtual Reality in Industrial Automation
VR takes operators around the world using wearable devices. They allow the user to interact with computer-generated graphics. In particular, VR is found in gaming consoles where the simulation impresses the user more deeply on the game and makes him feel like he is playing in a real setting.
Augmented Reality in Industrial Automation
Augmented Reality (AR) is a high level of VR. Computer-generated graphics are scattered throughout the real-world environment with the help of cell phones, smart camera screens, and tablets. To add means to add or improve. So, it’s just an extension of VR and adds an easy-to-use feel to this technology.
AR and VR Concepts
Today, AR and VR have found a good place in production and manufacturing. The Industrial Internet of Things (IIoT) combines intelligent production tools with automated technology. Therefore, AR and VR have already added to what is being done in IIoT. The visual effects created in CAD software are combined with real-time data displayed on mobile devices, smart glasses, headsets, and laptops. This supports a variety of human-centered tasks and makes the technician feel like you are a real-time machine with all the visual graphics.
Consider an operator who wants to operate and control a large industrial area with few errors and high accuracy. Thanks to AR and VR devices, you will be able to go through a specific program with a detailed guide and care. Cameras and headsets will guide the operator in understanding the system well with real-time data and appropriately, will take the necessary action. The Operator just sits in the central control room and operates the whole plant easily and with great precision. These elements will highlight the system with in-depth elements and their real-time data with appropriate animation, and thus assist the operator in controlling the system. AR and VR will easily identify industry resources and provide relevant information about them. This reduces people’s efforts to identify and maintain automated tools that would be time-consuming and busy. It also helps to reduce personal travel or the need to be present at a dangerous place or in situations where timely arrival is not possible. Devices will simply simulate everything in front of him and just wait for his actions to deliver.
It is also possible to estimate system costs more accurately and to organize work; as the user knows a large number of details by experiencing it. The design stage is easy and reliable to operate. All of these strategies combined with other IIoT technologies make manufacturing, logistics, and output all smarter and more efficient. Also, with the latest developments in 5G technology, AR and VR will become stronger to use and operate. It is still a matter of time before this method becomes fully acquired in the ever-changing industrial industry.
5) The rise of smart industrial robots
The growing presence of intelligent industrial robots on the factory floor is the victory of the fourth industrial revolution (Industry 4.0). Although manufacturers have been using robots for decades, the ongoing development of robotic technology has undoubtedly increased the potential use of intelligent industrial robots. So, today, robots driven by cutting-edge software and vision systems can be programmed to perform a series of tasks, tailored to the need for flexible production.
As the global manufacturing industry enters its fourth phase, new technologies such as robotics, automation and, artificial intelligence (AI) will take over. The number of active industrial robots worldwide is growing at about 14% year on year, and the changing ones are continuing to develop new types of robots with improved function and performance. Future industries are likely to introduce robots and co-workers to meet consumer demand — a new world for business owners to prepare.
Robotics Can Improve Productivity
Based on current assumptions, AI is expected to have the potential to increase labor productivity by up to 40% by 2035. While some may see robotics and AI as tools to replace human workers, the International Federation of Robotics believes that less than 10% of jobs can be automated; robots are usually designed to take repetitive tasks and allow employees to focus on more complex tasks. The great advantage of automation in large-scale manufacturing operations is that some jobs can be successfully completed 24/7, thus increasing productivity without additional labor costs. Successfully completing some robotic tasks can be especially helpful for small business owners. Small businesses often cannot hire as many employees as productive bullies; automation can help to create a playground.
In a recent survey, 57% of employers expressed interest in improving performance and productivity through automation and robotics. Some studies have shown that increased use of AI in the workplace may create new job opportunities, allowing employers to hire more employees in the future.
Automation Can Lower Overhead Costs
While the initial cost of automated software or robots may be significant, the return on investment can be immediate. Business owners may find that certain roles are no longer needed once AI is in use, saving costs quickly. For example, some restaurant industry leaders use service delivery robots, which reduces their need for human workers. As fewer employees participate in hazardous activities, businesses can also save on health and safety costs, with fewer injuries or leisure time for their employees.
Most robots need only a small amount of space to work, and they can work safely near humans in the assembly lines. A possible reduction in the required space means that companies may be reduced in cheap workplaces and industries. According to a recent survey, 24% of employers are currently considering doing certain jobs in order to reduce operating costs.
Smart Technology Can Reduce Human Error
Human error is a factor that every business should plan for, and both time and effort are spent in correcting problems when they occur. Especially when it comes to very repetitive or mathematical tasks, automation can deal with this with much lower errors than human workers. Since automation can be expensive to use, it is important for entrepreneurs to evaluate their end-of-year processes, and decide where they can get the biggest impact on AI. If your business has significant invoice functions with a small error limit, this may be a good place to start. If you have a connecting line where small screws are often left out of the finished product, the robot can complete the task successfully for you.
The fourth industrial revolution is expected to have a major impact on businesses and individuals around the world. If you are a business owner of any size, it is worthwhile to analyze your current activities to review where you can find opportunities to use automation or robots. The future of production is likely to involve people who work outside seamlessly around a variety of practical skills; preparing for the launch of the robot can help put your business ahead of the competition, and increase your main goal.
Authors: Aniket Kesarkar , Pranav Paigude , Siddharth Deshetti , Ashutosh Lahoti.
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