New biomimetic approach for multi-objective optimization decision-making of collaborative gear hobbing and grinding

Abstract

Multiple process variable parameters such as cutting parameters, tool parameters, and machine tool parameters in gear hobbing and subsequent gear grinding processes directly affect gear machining accuracy and efficiency, as confirmed through historical processing experience or manual decision-making. To determine effective parameters quickly, this study proposes a new biomimetic approach for optimization and decision-making based on the improved multi-objective grasshopper optimization algorithm (MOGOA) and the information entropy technique for order preference by similarity to ideal solution (information entropy-TOPSIS) for gear hobbing and gear grinding collaborative machining. Specifically, the parameter optimization problem under collaborative machining of gear hobbing and gear grinding is presented. Then, a multi-objective model oriented to the optimization of gear accuracy and processing efficiency is constructed through optimization variables, i.e., hobbing and grinding process parameters. Furthermore, the improved MOGOA and information entropy-TOPSIS are used for optimal decision-making on the process parameter sets. Eventually, the effectiveness and practicality of the proposed multi-objective optimization decision-making method are verified via small module gear precision machining. Results and comparison demonstrate that the optimization and decision of multiple parameters for the collaborative machining of gear hobbing and gear grinding can be solved by the proposed method, whose efficiency and superiority are confirmed.

Graphical abstract

Keywords

gear hobbing / gear grinding / multi-objective grasshopper optimization / decision-makin / information entropy-TOPSIS

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