Multi-Objective Topology Optimization for Cost and Time Minimization in Fiber Reinforced Additive Manufacturing

Fiber Reinforced Additive Manufacturing (FRAM) combines the geometric freedom of additive manufacturing with the high stiffness-to-weight advantages of composite materials, making it a promising approach for lightweight automotive components. The mechanical performance of fiber-reinforced composites is strongly influenced by fiber orientation, which highlights the importance of optimization methods that can effectively exploit anisotropic behavior. Existing FRAM optimization research has focused primarily on structural performance and has given limited attention to manufacturability challenges. This gap is significant, as overhangs and the resulting need for support structures can substantially increase print time, material consumption, and production cost, restricting broader industrial uptake. This research introduces a multi-objective topology optimization framework that incorporates Design for Additive Manufacturing (DfAM) principles by minimizing both structural compliance and support material requirements. The key contribution is a differentiable formulation of support generation that enables support-related penalties to be embedded within a gradient-based optimization process. The results demonstrate that substantial reductions in support usage and overall production effort can be achieved with only minimal impact on structural performance. The integrated framework advances FRAM design practice by uniting performance and manufacturing efficiency, strengthening its potential for next-generation automotive applications where weight, stiffness, and production cost must all be optimized simultaneously.