AN ANALYTICAL TOOL FOR STUDYING THE IMPACT OF PROCESS PARAMETERS ON THE MECHANICAL RESPONSE OF COMPOSITES

The present work presents a numerical framework able to predict the impact of the manufacturing process on the mechanical performance of the composite component. A simple one-dimensional thermochemical model has been used to predict the evolution of the degree of cure of the resin for a given thermal cycle. The homogenized properties at the lamina level have been obtained through a classical mixtures law and employed to predict the process-induced deformations. A refined one-dimensional model, derived in the framework of the Carrera Unified Formulation, has been used to provide accurate results with reduced computational costs. The virtual manufacturing framework has been used to investigate the impact of the process parameters on process-induced defects of a simple composite part. Different curing cycles have been considered and their outcomes discussed. The results demonstrate the capability of the present numerical tool to correlate the manufacturing process parameters with the mechanical performances of the final component

An efficient numerical approach to evaluate process-induced free-edge stresses in laminated composites

This paper presents an advanced modeling approach to predict process-induced residual stresses at the free-edge of laminated structures. The numerical model is based on the Carrera Unified Formulation, a numerical tool that allows any kinematic model to be considered without an ad hoc implementation. A layer-wise kinematic model has been adopted to detect the through-the-thickness distributions of transversal stresses. The process's evolution of the material properties is obtained by the RAVEN ® software. A cure hardening instantaneously linear elastic (CHILE) constitutive model was adopted. Peeling and transverse shear stress distributions along free-edges were computed and proved to be very high and localized

A theory-guided probabilistic machine learning framework for accelerated prediction of process-induced deformations in advanced composites

This paper introduces an innovative framework for efficient analysis of composites manufacturing processes and phenomena. The method combines sparse probabilistic characterizations, multi-fidelity simulation schemes, and limited experiments to train surrogate machine learning (ML) models. Guided by a probabilistic technique, Spatially Weighted Gaussian Process Regression (SWGPR), predictive models are constructed from multi-fidelity data to perform rapid and accurate manufacturing assessments. This study demonstrates the effectiveness of the framework in accurately predicting process-induced deformations (PIDs) for L-shaped composite parts using minimal experimental efforts. The method introduced in this work aims to offer a cost-efficient and broadly applicable framework for potentially mitigating PIDs and solving other composites manufacturing problems