Multi-Objective Optimization of Sheet Metal Forming Parameters for Enhanced Strength-to-Weight Ratio in Automotive Structures

The increasing demand for lightweight yet structurally robust automotive components has intensified the need for advanced optimization strategies in sheet metal forming processes. This study presents a comprehensive multi-objective optimization framework aimed at enhancing the strength-to-weight ratio of automotive structures by systematically tuning key sheet metal forming parameters. Critical process variables, including blank holder force, punch velocity, die radius, lubrication conditions, and material anisotropy, are analyzed to understand their combined influence on mechanical strength, thickness distribution, and formability limits. A hybrid optimization approach integrating finite element modeling (FEM), design of experiments (DoE), and advanced evolutionary algorithms such as Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to achieve optimal trade-offs among conflicting objectives. The proposed methodology effectively minimizes thinning and springback while maximizing structural integrity and material utilization. Additionally, response surface methodology (RSM) is utilized to develop predictive models, enabling efficient exploration of the design space with reduced computational cost. The results demonstrate significant improvements in strength-to-weight performance compared to conventional single-objective optimization methods. The optimized parameter sets provide enhanced formability, reduced material waste, and improved mechanical properties, thereby contributing to sustainable and cost-effective manufacturing in the automotive industry. The study offers valuable insights into the development of intelligent forming strategies aligned with modern lightweight design requirements and Industry 4.0 paradigms.