This paper introduces a solution for the current engineering challenges facing powertrain designers. This solution leverages the power of AI/ML technologies in the framework of transmission design.
The main objective of this paper is to introduce a solution for the current engineering challenges facing powertrain designers. This solution leverages the power of AI/ML technologies in the framework of transmission design. For this purpose, an evidence-based approach is adopted by presenting practical examples and use cases.
This study contains two main examples. The first is a small study conducted to validate the approach, focused on gear micro-geometry optimisation to reduce transmission error. This first study proves the accuracy and effectiveness of the method, after which a second, more comprehensive study is shown. This second study optimises a transmission design by varying a large number of parameters and evaluating the design’s performance against a wide range of criteria. This second case study shows the potential power that can be leveraged using this solution, which combines physics-based CAE with AI/ML methods.
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