Six sigma robust multi-objective optimization modification of machine-tool settings for hypoid gears by considering both geometric and physical performances

With the increasing demands of low noise and high strength from gear transmission system in industry applications, a collaborative optimization considering both geometric and physical performances has been increasingly significant for high-performance complex manufacturing of the hypoid gears. More recently, the machine-tool setting modification has provided an important access to this optimization design. However, its data-driven robustness or reliability is of a great difficulty. To deal with this problem, this paper presents a six sigma (6σ) robust multi-objective optimization (MOO) modification of machine-tool settings. Firstly, the 6σ robust optimization formulation is applied in the numerical result evaluations. Then, a novel data-driven model for MOO modification of machine-tool settings is established by establishing the functional relationships between the machine-tool settings and the performance evaluations, respectively. They can be integrated into a 6σ robust MOO machine-tool setting modification for hypoid gears having higher quality requirements. Finally, with the decision and optimization process, an achievement function approach was applied to solve MOO modification for the Pareto front, and the sensitivity-based variability estimation is used to identify the robust solution. The numerical applications are given to verify the proposed methodology.