Smart Machining Systems: Issues and Research Trends
Laurent Deshayes, Lawrence A. Welsch, M A. Donmez, Robert W. Ivester, David E. Gilsinn, Richard L. Rhorer, Eric P. Whitenton, Florian A. Potra
Smart Machining Systems (SMS) are an important part of Life Cycle Engineer-ing (LCE) since its capabilities include: producing the first and every product correct; improving the response of the production system to changes in demand (just in time); realizing rapid manufacturing; and, providing data on an as needed basis. Thereby, SMS improve the performance of production systems and reduce production costs. In addition, an SMS not only has to improve a particular ma-chining process, but it also has to determine the best optimized solution to pro-duce the part faster, better, at lower cost, and with a minimum impact on the en-vironment. In addition, new software tools are required to facilitate the improvement of a machining system, characterized by a high level of expertise or heuristic methods. A global approach requires integrating knowl-edge/information about the product design, production equipment, and machin-ing process. This paper first discusses the main characteristics and components that are envisioned to be part of SMS. Then, uncertainties associated with mod-els and data and the optimization tasks in SMS are discussed. Robust Optimiza-tion is an approach for coping with such uncertainties in SMS. Current use of machining models by production engineers and associated problems are dis-cussed. Finally, the paper discusses interoperability needs for integrating SMS into the product life cycle, as well as the need for knowledge-based systems. The paper ends with a description of future research trends and work plans.
Life Cycle Engineering and Sustainable Development
SPRINGER, Edt. D. Brissaud, S. Tichkiewitch, P.Zwolinski,
Knowledge bases, Life Cycle Engineering, Ontologies, Robust Optimization, Smart Machining Systems
, Welsch, L.
, Donmez, M.
, Ivester, R.
, Gilsinn, D.
, Rhorer, R.
, Whitenton, E.
and Potra, F.
Smart Machining Systems: Issues and Research Trends, SPRINGER, Edt. D. Brissaud, S. Tichkiewitch, P.Zwolinski, , [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=822333
(Accessed May 18, 2021)