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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.
DOI
10.12783/asc38/36621
10.12783/asc38/36621
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