Optimal Structure Determination for Composite Laminates Using Particle Swarm Optimization and Machine Learning

This work addresses optimality aspects related to composite laminates having layers with different orientations. Regression Neural Networks can model the mechanical behavior of these laminates, specifically the stress-strain relationship. If this model has strong generalization ability, it can be coupled with a metaheuristic algorithm–the PSO algorithm used in this article–to address an optimization problem (OP) related to the orientations of composite laminates. To solve OPs, this paper proposes an optimization framework (OFW) that connects the two components, the optimal solution search mechanism and the RNN model. The OFW has two modules: the search mechanism (Adaptive Hybrid Topology PSO) and the Prediction and Computation Module (PCM). The PCM undertakes all the activities concerning the OP at hand: the stress-strain model, constraints checking, and computation of the objective function. Two case studies about the layers’ orientations of laminated specimens are conducted to validate the proposed framework. The specimens belong to “Off-axis oriented specimens” and are subjects of two OPs. The algorithms for AHTPSO and for the two PCMs (one for each problem) are proposed and implemented by MATLAB scripts and functions. Simulations are carried out for different initial conditions. The solutions demonstrated that the OFW is effective and has a highly acceptable computational complexity. The limitation of using the OFW is the generalization ability of the RNN model or any other regression models. To harness the RNN model efficiently, it must have a very good generalization power. If this condition is met, the OFW can be integrated into any design process to make optimal choices of the layers’ orientations.