Integrated Optimization Framework for Composite Manufacturing: Minimizing Spring-In Effects and Improving Cure Cycles

Abstract

This study presents a structured methodology for the optimization of composite manufacturing processes, focusing specifically on autoclave techniques to mitigate issues such as the spring-in effect. The initial phase employs Sequential Quadratic Programming (SQP) in a weighted-sum approach to optimize the cure cycle, using Radford's equation for spring-in angle estimation and a multi-physics, multi-scale MATLAB model to investigate the cure and temperature-dependent laminate response. This phase underscores the efficacy of the selected optimization algorithm, demonstrating a significant reduction in spring-in while ensuring a high degree-of-cure. Subsequently, the study incorporates an integrated Finite Element Analysis (FEA) optimization framework linking ABAQUS and MATLAB. This framework utilizes the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization with an integrated composite manufacturing processing model. This second phase illustrates the framework's robust capabilities in composite cure cycle optimization, providing a well-distributed set of optimal solutions in an efficient timeframe. The study highlights the potential of the approaches and frameworks investigated to improve the efficiency, performance, and quality of composite parts.