Data-driven process control considering both geometric and loaded contact performance evaluations has been an increasingly important stage in field of spiral bevel and hypoid gears. A new data-driven manufacturing process control strategy is proposed for a high performance spiral bevel and hypoid gears. Here, to distinguish with the conventional simulated loaded tooth contact analysis (SLTCA) using economical finite element software package, the numerical loaded tooth contact analysis (NLTCA) is of more flexibility and practicality. In light of the advantages of the improved design for six sigma (DFSS), it is integrated with NLTCA for establishing a novel data-driven process control of gear manufacturing. Firstly, in improved DFSS framework, quality function deployment (QFD) is used to determine four sub-objective high-performance evaluation items. Then, their data-driven relationships between machine settings are respectively determined by using NLTCA. In particular, the manufacturing process control is further converted into multi-objective optimization (MOO) modification of the hypoid generator settings. Finally, an interactive preference point approach is applied for data-driven control of its iterative step and it can obtain a robust solution from Pareto optimal front. A case study is provided to verify the proposed methodology.

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Machine setting modification has been an increasingly important access to the accurate flank manufacturing geometric accuracy control for spiral bevel and hypoid gears. More recently, machine setting driven integration of the theoretical design and the actual gear manufacturing is gaining more and more attention. In this paper, the traditional machine setting modification is extended to the case when higher-order component of the prescribed ease-off flank topography is investigated in form of high-order polynomial expression. Moreover, the actual gear manufacturing and general measurement are integrated into an adaptive data-driven high-order machine setting modification. In particular, this modification method is used to perform adaptive modular control for computer aided process planning (CAPP). Here, a data-driven operation and optimization is developed for adaptive high-order modification. It mainly includes: (i) Polynomial fitting and its optimization by using overall interpolation based on energy method, (ii) Data-driven ease-off flank parametrization based on the fastest descent Newton iteration method, (iii) adaptive control strategy by considering the sensitivity analysis, and (iv) Levenberg-Marquardt (L-M) based approximation for high-order machine setting modification. Given numerical test can verify the proposed method.

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