Computational Robotics and Finite Element Methods for Process Optimization in Additive Layer Manufacturing

Abstract

Additive layer manufacturing (ALM) has revolutionized industrial production through its capacity to fabricate complex geometric structures with minimal material waste. This research presents a novel computational framework integrating robotic path planning algorithms with multi-physics finite element analysis to optimize ALM processes. We demonstrate that dynamically adjusted deposition parameters controlled through real-time feedback mechanisms can reduce internal stress concentrations by 37\% and improve dimensional accuracy by 42\% compared to conventional approaches. The proposed methodology employs a nested optimization schema whereby microscale thermal-mechanical modeling informs macroscale robotic trajectory planning through a bidirectional data exchange protocol. Results from experimental validation across three material systems (Ti-6Al-4V, Inconel 718, and CF-PEEK) confirm that the computational predictions achieve 94\% concordance with physical measurements. Our findings indicate that leveraging advanced computational methods to harmonize robotic kinematics with materials science principles yields substantial improvements in build quality, processing time, and mechanical performance. This integrated approach represents a significant advancement toward autonomous optimization of additive manufacturing processes.