Optimization of clutch fork design for automotive applications using reverse engineering, static analysis, and additive manufacturing

This study focuses on optimizing the design of an automotive clutch fork using reverse engineering, finite element analysis (FEA), and shape optimization. This was followed by additive manufacturing for prototype visualization. Cyclic loading on clutch forks in automotive transmission system can lead to reduced durability and premature failure due to stress concentration effects. Reverse engineering was done to obtain a 3D model Digital Twin of the original clutch fork. Static analysis was then performed to evaluate stress distribution, displacement, and the factor of safety under applied loading conditions. The analysis identified high-stress concentration areas, highlighting design improvements. Shape optimization was applied to refine the geometry, efficiently redistributing material while ensuring structural integrity and manufacturability. The optimized design demonstrated a 50.6% increase in the factor of safety, 33.7% reduction in von Mises stress, and 7% weight reduction. This makes it structurally more efficient and maintains mechanical integrity. Additionally, the maximum displacement increased by 10.6%, which remains within an acceptable range for operational performance. The strain values decreased by 50%, indicating a significant reduction in material deformation and improved load distribution. Fatigue analysis using ANSYS 2025R1 showed enhancement in life of optimized shape by around 36% as compared to the existing design. This study also highlights the role of additive manufacturing in fabricating complex, optimized geometries. By integrating computational design tools with advanced manufacturing techniques, this research demonstrates how shape optimization enhances mechanical performance, durability, and material efficiency in next-generation automotive transmissions by employing concept of Digital Twin.