Machine learning-based inverse predictive model for AFP based thermoplastic composites

Abstract

Manufacturing of thermoplastic composites using automated fibre placement (AFP) machine with specific characteristics is a challenging task due to the interdependence of various processing conditions and variables. It is of interest to know the accurate value of different input variables which would give the desired characteristics (outputs) of the laminates. This problem comes under the framework of inverse identification and is often ill-posed and its solution becomes increasingly difficult when the available data samples are very less. The present study develops a neural network-based inverse predictive model for AFP based manufacturing process using virtual sample generation (VSG) techniques. The efficacy of the developed predictive inverse model has been established considering varieties of experimental data. The proposed approach can be applied to a large class of manufacturing processes to determine the input conditions to a get product with desired characteristics.