Page 4 - Machines Italia Vol. XIII Next Generation Manufacturing

CURRENT ADVANCES
Smart Data Enables Smart Manufacturing
At the time of this writing, the COVID-19 pandemic continues to disrupt the world. Technical trends that were apparent before the pandemic may accelerate.
At some point, perhaps as you are reading this, it is likely that the COVID-19 pandemic will be controlled—while the tech- nical trends that were affecting the manufacturing world will have continued. Independent of any pandemic, there are
four trends that will affect the future of manufacturing: Digital twin tech- nology, knowledge capture, increasing connectivity, and ubiquitous sensing that feeds big data sets to artificial intelligence and machine learning. These four important trends are linked by what we might label Smart Data, and I will discuss how this enables Smart Manufacturing.
Digital twin is the digital representation of your process, product or part. It is easy to think of a digital twin as a CAD or CAE; however, it is substantially more. The digital twin does not have to be only a computer model. In fact, computer simulation can only go so far. Using 3D printing, physical models created from digital data can be used to derive empirical results, while its CAD model can be used for analytical modeling and computer simulations. Using both helps truly certify a part for use. Once a part is certified, the ability to track it in the field and certify that it is genuine via digital twin is vital. There is significant concern today about counterfeit parts, or tampering with production processes, which can be alleviated by this expanded definition of a digital twin. The approach employing the digital twin as a digital passport to certify a part as genuine and produced correctly, is known as born qualified or born certified and is a key element to cybersecurity in smart manufacturing operations.
The second trend I see is the ability today to capture knowledge and expertise and present it to new operators. Augmented and virtual reality are important tools in this ability to train a new generation as the old one retires and expanding new expertise as it develops. While
it is important to note that new technology is vital to the continued revitalization of manufacturing, it cannot replace human expertise. Automation, for example, is much more about augmenting the human and making them more productive, not replacing them. In places where labor costs more, investing in technology that helps workers become more valuable is especially important. A good example is using augmented reality to guide a seasoned three-axis CNC programmer and set-up person through a difficult five-axis CNC programming and set-up task. Such guidance seamlessly augments the capabilities of a member of the workforce, providing training and significantly adding value to that individual’s skillset.
Connection through the cloud with secure data transmission is important and available. About one third of all cyber-attacks in the U. S. are in the manufacturing sector, typically to acquire intellectual property and proprietary information. The upside of connectivity is the easy movement of a vast variety of data, from machine health to productivity. Edge computing, cloud computing, embedded comput- ing platforms, low cost storage, and broad band connectivity are all contributing to collecting and moving data.
It is this expanding connectivity that enables the final trend, the movement to data-driven analytics. Gathering sensor data from every point in the manufacturing process is important to enable the use of data analytics, artificial intelligence and machine learning to better understand manufacturing processes and operations, ultimately driv- ing critical decisions. Using field data can help generative design and manufacturing; process models can be improved for better and more profitable manufacturing.
All of these developments enable Smart Manufacturing, a trend that many of the Italian companies highlighted in this publication are enabling, as you will read.
Thomas R. Kurfess, Ph.D., P.E.
Chief Manufacturing Officer Oak Ridge National Laboratory
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