Improving Quality Inspection of Textiles by an Augmented RGB-IR-HS-AI Approach

Abstract

The aim of this research topic and paper is to investigate the application possibilities of vision technology in the textile industry. These include RGB, active thermography and hyperspectral imaging techniques. In the future, this approach will be supplemented by a machine learning algorithm (e.g., in Matlab or Python) to enable the detection of defects in textiles and to correctly categorize these defects. In the first place, the various options for building such a convolutional neural network are discussed. The focus was on the models used in the literature. Based on the effectiveness of these ML models and the feasibility to build them, choices can be made to determine the most suitable models. Sufficient samples are an important link to properly train a model. Because there is a shortage of open data, it is also discussed how samples obtained from the textile industry, were measured in the lab. At first, we will limit ourselves to the five most common defects. In a later phase of research, the results with this dataset and the open datasets are benchmarked against the results from the literature.

Cooling Simulation on Woven Polyester by Finite Elements for Improving Quality Inspection

“…The thermal simulations were used for a machine learning system, allowing for the detection and prediction of errors from the moment the processing parameters started deviating from the parameter window set for the specific application. This system could then be embedded within a broader predictive maintenance framework [1].…”