An Optimization of Energy-Efficiency in Machining Manufacturing Systems Based on a Framework of Multi-Mode RCPSP
Author(s) -
Tetsuo Samukawa,
Haruhiko Suwa
Publication year - 2016
Publication title -
international journal of automation technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.513
H-Index - 18
eISSN - 1883-8022
pISSN - 1881-7629
DOI - 10.20965/ijat.2016.p0985
Subject(s) - machining , energy consumption , scheduling (production processes) , computer science , energy (signal processing) , efficient energy use , machine tool , resource (disambiguation) , mode (computer interface) , industrial engineering , mechanical engineering , mathematical optimization , manufacturing engineering , automotive engineering , reliability engineering , engineering , operations management , computer network , statistics , electrical engineering , mathematics , operating system
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.
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