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IJAT Vol.10 No.6 pp. 985-992
doi: 10.20965/ijat.2016.p0985
(2016)

Paper:

An Optimization of Energy-Efficiency in Machining Manufacturing Systems Based on a Framework of Multi-Mode RCPSP

Tetsuo Samukawa and Haruhiko Suwa

Setsunan University
17-8 Ikeda-naka-machi, Neyagawa, Osaka 572-8508, Japan

Corresponding author,

Received:
March 30, 2016
Accepted:
September 20, 2016
Published:
November 4, 2016
Keywords:
energy efficiency, machine tools, energy load profile, optimization, multi-mode resource constrained project scheduling problem
Abstract
It has become important to consider energy-efficient optimization not only in a process design but also in the operations of manufacturing systems to promote sustainable and green manufacturing. This paper extends authors’ previous work to a more practical situation to demonstrate the applicability of the proposed framework of energy-efficient manufacturing operations based on a resource-constrained project scheduling problem (RCPSP). Both have varying resource requirements and multi processing modes, which can produce a suitable energy-load profiles for complete manufacturing systems. This study proposes a mathematical model for producing optimal energy-load profiles, and based on these profiles, each given operation is allocated to a machine tool with a specific processing mode. A processing mode refers to machining conditions for the corresponding operation, conditions that provide a predictive processing time and estimated electrical energy consumption. Through some cutting experiments on aluminum alloy performed on a three-axis machining center, we provide several possible processing modes for workpieces (operations), and we generate energy-load profiles by applying multi start local searches. We then discuss the applicability and capability of the energy-load profiles as an energy-aware production control.
Cite this article as:
T. Samukawa and H. Suwa, “An Optimization of Energy-Efficiency in Machining Manufacturing Systems Based on a Framework of Multi-Mode RCPSP,” Int. J. Automation Technol., Vol.10 No.6, pp. 985-992, 2016.
Data files:
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