Generative Design Optimization for Advance Manufacturing Process
Authors:-Mrs. K. Aravinda, Komali Naga Ramakrishna, Robba Rithwik, Sahukari Vijay Kumar, Komarthi Gagan Venkat Jayanth
Abstract-Brake pedals are critical components in automotive applications, requiring a balance between high stiffness, low weight, and manufacturability. Traditional design approaches often result in suboptimal structures with excessive material usage. This study explores the generative design optimization of a brake pedal using Fusion 360, targeting maximum stiffness and minimum mass while considering different manufacturing constraints for milling and additive manufacturing (AM). Generative design algorithms were employed to generate multiple optimized pedal designs by defining material properties, boundary conditions, and load cases. The milling-based design focused on constraints like tool access, machining orientations, and material removal feasibility, whereas the AM-based design leveraged organic lattice structures and topology optimization to achieve minimal material usage while maintaining structural integrity. The optimized models were analyzed using finite element analysis (FEA) to compare stress distribution, deformation, and weight reduction for both manufacturing methods. Results indicate that additive manufacturing allows for a more complex, lightweight design with internal lattice structures, resulting in a higher stiffness-to-weight ratio compared to the milling approach. However, the milled design exhibits superior fatigue resistance and is better suited for high-load conditions due to the absence of microstructural porosity. A comparative evaluation of material usage, manufacturing feasibility, and mechanical performance highlights the trade-offs between AM and milling-based designs. This research demonstrates how generative design tools can optimize brake pedal geometry for different manufacturing processes, leading to weight savings and enhanced performance while ensuring manufacturability. The findings provide valuable insights into process-dependent design optimizations and serve as a reference for future lightweight automotive component development.
