Enhancing Gear Performance: Discrete Response Optimization through OptiSLang

Original equipment manufacturers have already begun to transition their vehicles from traditional internal combustion engines (ICEs) to electric drives (EVs). As the industry continues to move towards electrification, the entire industry, and especially Valeo, is focusing on lean product development (LPD) with the help of numerical simulation. Optimization techniques help industry achieve the most accurate product at the lowest cost without sacrificing performance. Generally gears are mainly used for power transmission in the advanced technologies of electric vehicles. There are many factors that must be taken into account when designing a gear transmission system. Finding the most appropriate design parameters for a gear transmission system can be a challenge, and optimization parameters will help to find the best compromise between them. The main objective of this study is to increase the contact safety factor of the gear system by fulfilling 14 constraints, which are continuous (5 constraints) and discrete (0 or 1 for 9 constraints). Building a meta-model for discrete output parameters is difficult, combined with the large number of input and output parameters makes it a challenging problem to solve. Traditional optimization algorithms based on meta- models, and also algorithms based on direct optimizations struggle to find optimum design, and in many cases doesn’t give a valid design even after a huge number of design evaluations. An algorithm called One Click Optimization (OCO) in ANSYS OptiSLang was tried for this application. The OCO method is a hybrid and dynamic optimization approach developed by ANSYS. It efficiently combines direct (high-fidelity model) and MOP (response surface) assisted search strategies. As a result, OCO was able to find a better design (satisfying all the design criteria and constraints) when compared to other algorithms. Also, it was able to achieve this with ~50% less design evaluations. A New methodology is demonstrated here, which helps solve complex design problems in an efficient way, thereby generating better design and reducing overall product development time.