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.

由于各种加工条件和变量之间的相互依赖性，使用具有特定特性的自动纤维铺放（AFP）机器制造热塑性复合材料是一项艰巨的任务。令人感兴趣的是知道不同输入变量的准确值，其将给出层压件的期望特性（输出）。这个问题属于逆向识别的框架，通常是不适当的，当可用数据样本非常少时，其解决方案将变得越来越困难。本研究使用虚拟样本生成（VSG）技术为基于AFP的制造过程开发了基于神经网络的逆向预测模型。考虑到各种实验数据，已经建立了开发的预测逆模型的功效。