Predicting Tensile Properties of Technical textiles with Artificial Neural Networks in the Product Development Process A case study on the requirements for an ANN implementation, the process improvements, and resulting competitive advantages

Abstract

The end-user environments of technical textiles are often characterized by extreme weather conditions, leading to aging which implies the need for testing in the product development process (PDP). This thesis aims to suggest an Artificial Neural networks (ANN) model predicting the aging of technical textiles and an AI strategy for companies in the technical textile industry. The resulting improvements and competitive advantages of such an implementation will also be studied. A case study of an aging measurement analysis in the testing and validation phase of the PDP in the technical textile industry was conducted. Through semi-structured interviews, focus group and workshop, the PDP, aging measurement analysis, requirements for an ANN implementation, and the resulting improvements and competitive advantages were mapped. This was analyzed using theory on ANNs, the stage-gate process, requirements for AI implementation, improvements from AI, and competitive advantages. It was found that the aging measurement depends on an in-depth understanding of the complex relationships between material components. As a result, several iterations in the PDP to ensure that the prototype meets the aging requirements are often required and lead to a time-consuming and costly process. Another finding was that the industry is characterized by low digital maturity. It was concluded that two single hidden layer backpropagation feedforward ANN models were suitable for predicting the tensile properties. It is recommended to initially use eight categories of input parameters and then employ regularization. Sigmoid and ReLU are suggested as activation functions and Levenberg-Marquardt as optimization functions. A combination of error measures are suggested. This thesis concluded that an AI strategy for the technical textile industry to implement ANN successfully should include plans to fulfill requirements regarding, internal and external data, data anonymization, automated data collection, computing power infrastructure, knowledge, and employee AI trust. Further, it was concluded that the implementation of ANN could improve the knowledge in the PDP, leading to improved speed, quality, and cost. In the technical textile industry, the corresponding competitive advantages are, time to market, superior products, innovation capacity and profitability.