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Chandran Nair is the vice president for Asia Pacific at National Instruments, a provider of automated test systems and virtual instrumentation software. In an interview during their recent National Instruments Technical Symposium, Nair explains how the Industry 4.0 vision will change electronics manufacturing. He explains key technology enablers that would drive the evolution of manufacturing processes, and how a platform-based approach can help improve test and measurement.
Las Marias: How will Industry 4.0 take electronics manufacturing processes to the next level?
Chandran Nair: Industry 4.0 is a massively used term, and people have different views for that. But one of the common themes in Industry 4.0 as we move forward is that at the very least, it will help asset management because people will be able to build machines that can communicate with each other in the factory floor. It will help in things like utilization and predictive maintenance, that will in turn reduce downtime, increase efficiency, and reduced wastage. At the next level, it will be the connectivity to the enterprise that will enable things like data analytics, higher level integration with the IT systems—the convergence of operational technology and information technology—and people will be able to get real-time information on things like productivity. You can even think as far as being able to make customizable products based on customer needs.
Las Marias: What are the key technology enablers for this Industry 4.0 vision?
Nair: Each level will be really having smart machines—machines that can communicate with each other and communicate with the enterprise. It is powered by the increase in processing power, in the ability to use heterogenous processing systems, increase in the capability of system level software to be able to talk to multiple processing systems that are requiring data, and then finally, the ability to do analysis and the ability to provide insights based on all these data that are being collected.
Las Marias: Do you see manufacturers now adopting these technologies? If not yet, what remains to be the challenge?
Nair: The places where I see quick adoption are in high-value systems, like aerospace systems. For example, people that make high-precision turbines—they are already a step ahead with regards to integrating their manufacturing to the IT level to do predictive maintenance. That seems to be carrying on as economies of scales take place, and people understand exactly how to use these smart machines, smart devices, the ability to connect to the cloud and to do analytics,and to have artificial intelligence (AI) algorithms that can provide insights. As that happens more and more, I can see the adoption go downstream also.
Las Marias: Industry 4.0 involves automation, connectivity, and data.
Nair: Yes, and connectivity to sensors, not only connectivity among processing units. There is one more thing that I would like to add: it’s also the ability to do data reduction at the edge, close to where the sensors are.
Las Marias: Where does National Instruments come in to help manufacturers in their Industry 4.0 journey?
Nair: National Instruments come in to the test side, providing the ability to do data management and integration of these data management to the enterprise level. We also come in very heavily in terms of predictive maintenance. We are also helping companies in the verification and test, because all the data collected can then be fed into artificial intelligence boxes, if you like, where there will be analytics that do these AI and give people a better understanding of how to do predictive maintenance, better utilization, and so on. So, where National Instruments comes in is sharing our platform expertise and tapping on the expertise of our customers and partners to be able to help companies get insights from the large amount of data they collect.
Las Marias: Nowadays, equipment manufacturers are developing some sort of AI technology or machine learning into their systems. Why do you think that is so?
Nair: Instead of just basing computer programming on a set of codes that the computer will perform—and it is limited to the instructions the computer is given—what happens is data is fed and models are created based on that data, so that decisions can then be made by the computer based on the wide varieties of data and scenario that are fed in. It’s used in things like understanding how autonomous vehicles would react in different conditions. Basically, by getting these large amounts of inputs and creating scenarios around them, the computer learns how to create decisions based on real-time information.
When it comes to the manufacturing side, it could be where people understand, based on inputs, of what kind of test requirements are needed to increase, for example, the yield. On the production side, it could be determining the yield conditions as products are being manufactured, and how one can improve the yield. These could all be use-cases for machine learning and AI. Also, robots used in the manufacturing line will increasingly learn how to work along with humans to be able to continually optimize. Because, really, the future is not just purely robotic lines, but how robots work along with humans in to increase efficiencies—because there are some things that only humans can do.
Las Marias: Many manufacturers have legacy systems in their factory lines. How can they transition to a smart manufacturing model while still being able to utilize their existing systems?
Nair: Transition is the right word. It’s evolution, not revolution. The reason I say that is you are not going to throw away millions of dollars’ worth of equipment that you have already invested in especially if it’s working well. So, the existing machines will be made much smarter. How? You can add some sensors, so that these machines can have increased amount of data input during the manufacturing process. That information can then be passed up the stream so that you have intelligent manufacturing, for example, and you could scale up in parts. As you go to new product lines, you may bring in new equipment. The existing product lines can be made smarter based on your needs, through adding features, which can be added through people like National Instruments who are working with multivendor systems.
Las Marias: What does the future look like for the test and measurement industry?
Nair: For the test and measurement market, of course, the ability to use FPGAs for increasingly complex testing is going to continue. The ability to do machine learning so that test times can be reduced, and tests can be optimized—that will continue; and, the ability to integrate with the cloud so people can do these analytics on the cloud and get them, especially for large companies with multiple manufacturing sites. One of the major things is, as test systems and control systems become more and more complex and increasingly controlled and monitored through software, the need for configuration and remote management of the assets will increase so that manufacturers will be able to do updates from central stations.