Multi-component energy modeling and optimization for sustainable dry gear hobbing

Qinge Xiao, Congbo Li, Ying Tang, Jian Pan, Jun Yu and Xingzheng Chen

Energy, 2019, vol. 187, issue C

Abstract: Sustainable machining becomes a key priority for manufacturing industries due to the ever growing energy costs, associated environmental impacts and carbon emissions. As one of the frequent activities in metal machining, dry gear hobbing contributes to a significant portion of energy consumption. Process parameter optimization is an effective method of decreasing energy from process control perspective. However, hobbing parameter optimization is rarely involved in previous studies. To this end, a multi-component energy model is first developed on a basis of energy characteristics analysis of dry gear hobbing machines. Then, the optimization of hobbing parameters for the minimizing energy consumption and production cost is formulated as mathematical programming problem with a systematic consideration of machining constraints. Finally, the optimization problem is solved by a modified multi-objective imperialist competitive algorithm (MOICA). The results demonstrate that the energy-efficient gear hobbing can be achieved through a collaborative effort of predictive modeling and parameter optimization.

Keywords: Dry gear hobbing; Parameter optimization; Energy modeling; Sustainable machining (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:187:y:2019:i:c:s0360544219315890

DOI: 10.1016/j.energy.2019.115911

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