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Smart Manufacturing

March 10, 2017 • INNOVATION, TECHNOLOGY, Digital Transformation, Emerging Ideas, Internet of Things

By Seeram Ramakrishna, Chen-Khong Tham and Teo Kie Leong

Manufacturing is vital to a country’s economic growth and ability to innovate. Manufacturing is changing from large monolithic production floors to geographically distributed, internet-connected, medium scale, smart factories. Engineering education needs to be updated in order to train engineers with the needed knowledge and skills.

 

Manufacturing accounts for a quarter of worldwide employment. Employees of this sector earn higher than the median income of a country.1 Manufacturing contributes up to 20% of Gross Domestic Product, GDP of countries. Moreover it has a multiplier effect on business services jobs. Manufacturing is vital to a country’s economic growth and ability to innovate. Hence countries such as Singapore, China, USA, Germany, India, UK, Korea, and Japan have embarked on substantially funded national programs to strengthen manufacturing. Singapore’s $19 billion Research, Innovation and Enterprise plan known as RIE2020 has a major emphasis on Advanced Manufacturing and Engineering, AME. Automation and robots have been part of manufacturing innovation.

 

Digital Manufacturing

The terms “digital manufacturing” or “smart manufacturing” or “intelligent manufacturing” refer to communication and computing technologies which enable all players in the value chain of products at the supply chain, enterprise and shop floor levels to be digitally connected and data analytics-driven, thus achieving intelligent coordination for demand and supply matching, faster time to market, mass customisation and cost benefits. Engineers are developing Manufacturing Control Tower (MCT) for this purpose.  Maintenance of machinery is an area of focus.  For example, General Electric (GE), reported that at one of its plants, the use of Predix software platform connected to sensors led to the detection of gas leakage and preventive measures led to savings of $350,000 per year.  Another example, consider cutter tool wear in a CNC milling machine. After each cut, photos from a high fidelity stereomicroscope can be taken to measure the wear of the cutting tool. Measurements from sensors such as vibration sensor and force dynamometer are sampled using data acquisition cards and the data are stored on computers. After data acquisition, the sensor readings are used for feature extraction and selection. These selected features can be used to train and cross-validate advanced neural network (NN) or Hidden Markov Model (HMM) models of the tool wear, e.g. via an offline computing platform away from the machine. When the models are trained and cross-validated, they can be used for: (a) diagnostics where the degree of tool wear in the current time step can be determined from the model given the current and past sensor readings, and (b) prognostics so that the tool wear is predicted. These predictions enable maintenance to be scheduled when necessary, thus reducing downtime while increasing reliability.



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About the Authors

Dr Seeram Ramakrishna, FREng, is a Professor of Mechanical Engineering at the National University of Singapore (NUS). He chairs the Future of Manufacturing technical committee at the Institution of Engineers Singapore. He co-leads Smart Manufacturing Expert Group, SPRING, Singapore Government. He is a Highly Cited Researcher in Materials Science (www.highlycited.com). Thomson Reuters identified him among the World’s Most Influential Scientific Minds; he has co-authored around 1,000 articles which attracted around 62,000 citations and 116 H-index. He mentors three start-up companies and his research has been translated into marketed products. He authored the book entitled The Changing Face of Innovation.

Chen-Khong Tham is an Associate Professor at the Department of Electrical and Computer Engineering (ECE) of the National University of Singapore (NUS). His current research focusses on cyber-physical systems, advanced sensing, edge analytics and mobile cloud computing. He had served as Principal Scientist and Department Head of the Networking Protocols Dept, and Programme Manager of the Digital Services Programme, at A*STAR Institute for Infocomm Research (I2R) Singapore, and Programme Manager of a multi-institution research programme on UWB-enabled Sentient Computing (UWB-SC). He obtained his PhD and MA degrees in Electrical and Information Sciences Engineering from the University of Cambridge, United Kingdom. He is a Senior Member of the IEEE.

Teo Kie Leong is currently the Vice-Dean (Research & Technology) of the Faculty of Engineering and Professor in the Department of Electrical & Computer Engineering (ECE) at the National University of Singapore (NUS). His current research interest is in the area of Spintronics with a focus on the synthesis and fabrication of novel magnetic materials for STTRAM applications.

References
“Revitalizing American Manufacturing”, National Economic Council, October 2016.
www.hyperion-core.com
Seeram Ramakrishna et al, Materials Informatics, Journal Materials Today, 2017.

 

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