Performance of machine tool tends to deteriorate in the production process. This deterioration increases the processing energy consumption and leads to more defectives and corresponding energy waste. Maintenance can be taken to restore the performance of machine tool and improve the energy efficiency, which has a significant impact on the total energy consumption and productivity. This paper proposes an approach to improve the energy efficiency of the production process through scheduling the maintenance actions of the machine tool, taking into account productivity, product quality, and energy consumption. The deteriorating machine tool is modeled as a discrete-time, discrete-state Markov process. Partially observable Markov decision process (POMDP) framework is applied to develop the maintenance decision-making model, where the joint observation of processing energy consumption and quality of manufactured workpiece is used to infer the status of the machine tool. An optimal maintenance policy maximizing the total expected reward about energy efficiency over a finite horizon is obtained, which consists of a sequence of decision rules corresponding to the optimal action for each belief vector. The characteristics of the optimal policy are illustrated through a numerical example and the effects of parameters on the policy are analyzed. . . . Keywords Energy
The International Journal of Advanced Manufacturing Technology – Springer Journals
Published: Jun 2, 2018
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