Session: 03-05-02: Integrated Computational Materials Engineering

Paper Number: 137934

137934 - Integrated Process Simulation for Advanced Composites Manufacturing 

Composite materials exhibit exceptional properties and performance, yet their manufacturing processes often introduce complications such as cure-induced distortion, porosity, wrinkling, residual stress, micro-cracking, and variability. These challenges necessitate the development of physics-based processing models to accurately understand and predict these effects and defects. Such models encompass draping, infusion, flow-compaction, wrinkling, and curing simulations. The utilization of these models is pivotal in enhancing manufacturing processes. However, the question arises: what is the optimal approach to employing these models to yield measurable improvements? Merely possessing an accurate model does not guarantee the conversion of theoretical perfection into practical outcomes. Virtual experimentation and trial-and-error methods offer some cost and time savings, but they often fall short in delivering optimal results or substantial insights. The integration of physics-based processing models with data-driven methodologies—such as statistical analysis, uncertainty quantification, and optimization—is essential to harness their full potential. Model integration can be thought of as linking various aspects of materials processing, structure, properties, and performance, or implementation within a framework producing informed decision making.

Depending on the materials and processing conditions, viscoelastic behavior of a curing composite material may become significant to accurately predict the outcome of the manufacturing process. It is therefore critical to develop the capability to efficiently capture the viscoelastic curing behavior to enable full exploration of the design/solution space for model integration with data-driven methods such as optimization. Typically, viscoelastic behavior is approximated to elastic behavior in composite process modeling because of the high computational expense, which is especially limiting to data-driven methods, and it is therefore critical to develop more efficient methods for viscoelastic process modeling. Using the correspondence principle with multi-scale modeling techniques, we obtain the homogenized thermo-viscoelastic response of the composite material, and verified with direct numerical simulation. A reduced order model is utilized to incorporate the cure-dependency of the thermo-viscoelastic behavior. The simulation is very efficient and has been compared with experimental results of spring angle of 1.9° vs 1.95° from the experiment.

We have developed an integrated Finite Element Analysis (FEA) optimization framework, combining ABAQUS and MATLAB, and employs the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. This results in a robust and efficient methodology for process optimization, with a well-distributed set of optimal solutions. The study demonstrates significant potential for improving the efficiency, performance, and quality of composite parts, highlighting the value of these approaches in advancing composite manufacturing processes.

A straightforward approach to linking the processing model to an integrated framework like this is to convert the ABAQUS input file into a parametric f-string to make the model a function of the design variables. However, this method does not leverage the capabilities of ABAQUS/CAE as it is limited to changes that can be made by modifying existing input files. For a more general approach, we implement our process modeling approach through ABAQUS Python to enable comprehensive parameterization of a given model. A typical integrated composite processing model is multi-scale and multi-physics, consisting of sequentially couple heat-transfer and stress analyses. To automate the model creation, the geometry is first generated parametrically and the conditions valid for all analysis types are defined according to the input parameters. The code loops through the desired analysis types (for example, heat-transfer and stress-deformation), creating the steps and defining relevant conditions. Then, looping through the analysis types again, the model is modified as needed (including material properties and section and element types), and the job is created. A batch file is automatically generated for submitting and linking the jobs.

This study highlights the integration of composite processing simulations to enhance manufacturing accuracy. By combining physics-based models with data-driven analytics, the research offers methods to predict and optimize manufacturing outcomes. The application of multi-scale modeling, parametric simulation, and experimental validation provides a comprehensive framework that improves the efficiency of composite material processing. This integrated approach advances the design, manufacturing, and performance evaluation of composite materials.

Presenting Author: Ryan Enos Purdue University

Presenting Author Biography: Ryan Enos, currently a Ph.D. student in Aeronautical and Astronautical Engineering at Purdue University, is engaged in the study and application of computational modeling and simulation, particularly focusing on advanced composites. His educational journey began with a bachelor’s degree in Mechanical Engineering from Virginia Commonwealth University, followed by a master’s degree from the University of Connecticut, where he started to explore his interest in aerospace and mechanical engineering.

Ryan's professional experience includes internships and research positions at various esteemed institutions. He has been fortunate to work on projects at the University of Connecticut, NASA's Glenn Research Center, and Langley Research Center. These opportunities allowed him to delve into the complexities of composite materials and structural engineering. At Purdue, his research endeavors continue, mainly focusing on manufacturing process modeling for Organic Matrix Composites and contributing to collaborative projects with the Air Force Research Laboratory.

His academic contributions, while modest, include publications in recognized journals and presentations at conferences, reflecting his ongoing commitment to the field of aerospace engineering. Alongside his academic and professional pursuits, Ryan values his experiences as a Division 1 athlete and his involvement in community service.