Transactions of Nanjing University of Aeronautics & Astronautics
A Novel Multi-objective Optimization Approach for Gear Hobbing Parameters Considering Hidden Management Constraints
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Abstract:
In the multi-variety and small-batch production mode， the effective historical gear hobbing cases of gear hobbing machine tools represent the best practices under the combined constraints of gear hobbing machine tool， gear， and hob characteristics， as well as gear hobbing management requirements. The gear hobbing parameters of samples in historical gear hobbing cases inherently embed hidden management constraints of gear hobbing. This study established the two-stage multi-objective optimization models for gear hobbing parameters considering hidden management constraints， achieving a trade-off among gear hobbing quality， gear hobbing time， and gear hobbing cost under hidden management constraints. A multi-objective hippopotamus optimization （MHO） algorithm is proposed in this study. The diversity， convergence， and coverage of its Pareto optimal solutions are verified via standard test functions， and the running efficiency of MHO is significantly superior to that of the non-dominated sorting genetic algorithm Ⅱ （NSGA-Ⅱ） and the multi-objective particle swarm optimization algorithm （MOPSO）. By solving the proposed model with MHO， a set of gear hobbing parameters subjected to hidden management constraints is obtained. By designing different selection strategies， multiple optional gear hobbing parameter combinations are provided for technicians. Experimental results show that the effectiveness of gear hobbing using the proposed method meets the requirements of quality， time， and cost subjected to hidden management constraints. The research results are of significant technical and engineering value for technicians and managers to optimize gear hobbing parameters subjected to hidden management constraints.
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TH164
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