Intelligent Manufacturing Systems: A Review

Steven Liang 1, Manik Rajora 1, Xianli Liu 2, Caixu Yue 2, Pan Zou 3, and Lihui Wang 4
1. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, USA
2. Mechanical Engineering, Harbin University of Science and Technology, Harbin 150080, China
3. Mechanical Engineering, Donghua University, Shanghai 201620, China
4. Mechanical Engineering, KTH Royal Institute of Technology, SE 10044, Sweden
Abstract—Manufacturing factories, having continuous pursuit of productivity and quality, often meet challenges in coping with high production complexities and uncertainties. These are the areas in which traditional manufacturing paradigms underperform due to the limitation of human operators’ ability to cope with these complexities, uncertainties, understanding/memorizing big data, and also their inability to make time demanding decisions. Intelligent manufacturing systems, on the other hand, can yield superior results compared to traditional manufacturing systems as they are capable of analyzing, self-learning, apprehending complexities and are also able to store and analyze large amounts of data to obtain increased quality of the product and lower production cost while shortening the time-to-market. The aim of this paper is to outline the recent accomplishments and developments in intelligent scheduling, process optimization, control, and maintenance. For each aspect, concepts, requirements, application implemented, and methodologies deployed are also presented. 

Index Terms—component, Intelligent Manufacturing Systems, Intelligent Scheduling, Intelligent Prediction and Optimization, Intelligent Control, Intelligent Maintenance

Cite: Steven Liang, Manik Rajora, Xianli Liu, Caixu Yue, Pan Zou, and Lihui Wang, "Intelligent Manufacturing Systems: A Review," International Journal of Mechanical Engineering and Robotics Research, Vol. 7, No. 3, pp. 324-330, May 2018. DOI: 10.18178/ijmerr.7.3.324-330
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