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

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

    Research output: Contribution to journalArticlepeer-review

    10 Scopus citations

    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.

    Original languageEnglish (US)
    Article number115911
    JournalEnergy
    Volume187
    DOIs
    StatePublished - Nov 15 2019

    All Science Journal Classification (ASJC) codes

    • Civil and Structural Engineering
    • Building and Construction
    • Pollution
    • Mechanical Engineering
    • Industrial and Manufacturing Engineering
    • Electrical and Electronic Engineering

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