Application of Topology Optimization and Artificial Intelligence based Evolutionary Algorithm to Minimize the Contribution of the Caliper in Brake Squeal Noise

<div class="section abstract"><div class="htmlview paragraph">The squeal noise is one of the critical factors to qualify a disc brake design from the Noise Vibration and Harshness (NVH) perspective. It is imperative to be watchful of the unstable natural modes of the brake assembly which trigger squeal. Any design modification for reducing a part’s contribution to targeted squeal mode can adversely affect and give rise to new squeal modes. Also, controlling conflicting requirements like mass, strength, and casting manufacturability, further adds up complexity, which increases design iterations and product cost. In view of these challenges, the application of the topological optimizations embedded under an artificial intelligence (AI) driven optimization workflow is explored. The scope of optimization is kept limited only to the caliper. Complex eigenvalue (EV) finite element analysis (FEA) of baseline design brake assembly is performed which predicts critical squeal mode having 34% strain energy contribution from caliper. To improve the squeal performance, surface morphing-based shape optimization with mode tracking is explored, which can be useful in the finetuning stages of the design. However, at the initial stages, topological optimizations play an important role in obtaining suitable concepts. Since commercial topological optimization tools do not support complex EV analysis, an integrated &amp; automated workflow is developed. In this, the caliper geometry is first topologically optimized for mass, stiffness, casting manufacturing constraints, followed by complex EV analysis of the optimized geometry. A dummy thermal analysis is included in topology optimization, which equivalently simulates irrotational inviscid fluid flow to improve casting filling performance. Furthermore, a computational fluid dynamics (CFD) solver is added to the workflow to simulate the viscous flow effects during filling process. AI based evolutionary multi-objective optimization algorithm is used to perform multi-disciplinary optimization on the caliper geometry. The best candidate obtained from virtual design iterations exhibited significant reduction in caliper’s strain energy contribution to 9% or less in squeal all modes, reduction in number of squeal modes while having better strength as compared to baseline design with controlled weight addition.</div></div>