Date of Award
2026
Document Type
Open Access Master's Thesis
Degree Name
Master of Science in Mechanical Engineering (MS)
Administrative Home Department
Department of Mechanical and Aerospace Engineering
Advisor 1
Vinh Nguyen
Committee Member 1
Geordan Gutow
Committee Member 2
Cameron Hadden
Abstract
Composite materials have become a critical component of modern manufacturing, especially in the automotive and aerospace industries. The curing process for these composites has been modeled using a variety of partial differential equations representing the heat transfer and composite curing kinetics. Optimizing the applied temperature profile is critical for maximizing the efficiency and capacity of composite part manufacturers. Constraints must be placed on the inputs and outputs of the model, including but not limited to, the applied temperature profile, part temperature, and final degree of cure. Conflicting sets of constraints are easy to unknowingly impose due to the highly coupled and non-linear partial differential equations characterizing the optimization problem. This work presents an approach to managing conflicting constraints in the optimization process through a new method of co-training Physics Informed Neural Network (PINN) models. The proposed training algorithms, Loss Categorization and Trenching Algorithm (LoCaTA) and Automatic Loss-Based Constraint Relaxation (ALBaCoRe) allow for the detection of conflicting constraint sets through the categorization and modification of traditional loss terms. If conflicts are detected, ALBaCoRe will automatically adjust the constraints to a non-conflicting state. Both algorithms are tested using sets of constraints with known degrees of conflict and compared to finite element (FE) models to evaluate accuracy. LoCaTA was shown to accurately distinguish between conflicting and non-conflicting sets. For the conflicting sets, ALBaCoRe was able to relax conflicts to a new set of non-conflicting constraints.
Recommended Citation
Evans, Cooper J., "Detecting and Repairing Conflicting Constraints in Co-Trained Physics-Informed Neural Networks for Composite Curing Processes", Open Access Master's Thesis, Michigan Technological University, 2026.
Included in
Manufacturing Commons, Materials Science and Engineering Commons, Numerical Analysis and Scientific Computing Commons, Partial Differential Equations Commons, Structures and Materials Commons