Going 'energy efficient' has been one of the missions for manufacturers to stay globally competitive. Considering machining as a major manufacturing activity, how to effectively model and control its energy consumption becomes critical. Although researchers have analyzed the energy consumption of machining from the machine, process planning, or shop-floor perspective individually, very litter work has comprehensively studied the concurrent interactions among energy-aware decisions for machine parameter settings, process planning, and shop-floor control. Hence, the work presented in this article undertakes this challenge in the context of a resource-constrained machining system. In particular, the energy characteristics of machining are first analyzed with the consideration of various machine tools, cutting tools, cutting parameters, operation sequences, as well as machine availability. A multiobjective optimization model is then developed to minimize both energy consumption and makespan. The solution is provided through honey bee mating optimization algorithm (HBMOA) combined with shop-floor simulation. In addition, the significance of the proposed approach is exemplified and elucidated by a case study. Note to Practitioners-A well-informed decision made in cutting parameter optimization or process planning relies heavily on the accurate data delivered from the shop floor. In other words, the decision made without the consideration of the shop-floor situation might not achieve the original goal or even fail to materialize. As these factors exist in a real manufacturing cycle, how to integrate shop-floor scheduling with process planning and machining parameter optimization becomes essential for energy-efficient manufacturing. This article undertakes this challenge and develops a multiobjective optimization model, where energy consumption of machining, for the first time to the best of our knowledge, is comprehensively analyzed at both the machine level and shop-floor level. Such broader integration makes this approach more practical and applicable to real industry settings, particularly when the shop-floor resource is limited.
|Original language||English (US)|
|Number of pages||18|
|Journal||IEEE Transactions on Automation Science and Engineering|
|State||Published - Jul 2020|
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering