Abstract:
Manufacturing of composites using Automated Fibre Placement (AFP) is a complex process which involves large number of processing conditions and variables. Improper selection of these parameters adversely affect the quality and integrity of the manufactured laminates. Thus, it is important to develop a predictive model which can assess how changes in critical process conditions alter the outputs of the manufacturing process. The goal of this investigation is to learn the complex behaviour of composites by developing an intelligent model which can subsequently be used for the prediction of various characteristics of the composites. However, manufacturing of AFP composites is both expensive and time-consuming and therefore the available data samples are less, from the prospective of machine learning, which leads to the small data learning problem. This study first solves this problem through Virtual Sample Generation (VSG), then a Neural Network based predictive model is developed to accurately learn the complex relationships between various processing parameters in AFP.
