Energy efficiency analysis of autoclave curing of thermoset composites using simulation

Fiber-reinforced composite polymers (FRPs) have revolutionized aerospace with their exceptional specific mechanical properties. Yet, transforming raw fibers and resins into high-performance composite structures is an intricate multi-step process. It is best analyzed as a systems problem, and any variation in one cell of a factory can propagate through the entire process chain and affect the quality or property outcomes. In this work, a previously developed structured approach that defines Material, Shape, Tooling, Equipment, and Process (MSTEP) at every stage was used to analyze what is arguably the most critical step, namely cure, which is the transformation of the resin from liquid to solid. Cure is nearly always a thermal process, with autoclave (and oven) curing the most common. To facilitate this analysis, the autoclave system was broken down into its thermal, mechanical, and electrical subsystems. The electrical and mechanical components, such as computers, vacuum systems, and cooling pumps, are characterized by their basic and operational power consumption measured from experimental baseline data. Meanwhile, the thermal subsystem is quantified by combining commercial thermochemical simulation software, which provides temperature and material property evolutions of composite laminates and tooling, with energy balance equations and Newtons’ law of cooling. While this systems approach provides a logical framework for understanding the interactions during cure, the decision-making process of operational management often requires trade-offs between quality, rate, cost and sustainability. To navigate these trade-offs, industries are increasingly adopting Model-based System Engineering (MBSE). This work adopts that methodology and demonstrates the utility of the developed framework by quantifying the energy efficiencies of the CRN autoclave across various curing cycles. Finally, the scalability of the framework is evaluated by estimating larger industrial autoclaves, such as the Boeing 777X Autoclave.