Quality Control in Plastic Fiber Production: Overcoming Scarce Data with Explainable AI

The production of plastic fibers is a complex process, where the quality of the produced fibers is crucial for their performance in applications like fire-resistant concrete. Their quality is determined through visual inspections of dispersion tests, which are chemically treated samples of the produced fibers. However, this process is prone to more conservative ratings by lab personnel, as to avoid missing existing defects. This leads to unnecessary blade replacements and increased costs. To support the lab personnel in their rating process, a machine learning based pipeline is proposed. It consists of a classification model and explainable artificial intelligence (XAI) methods to visualize the presence of agglomerations, which are the sole determinant of the dispersion test’s rating. The goals are to provide lab personnel with a second opinion on the quality of the dispersion tests, whilst having only scarce labeled data available. Additionally, the suitability of different XAI methods and their pre- and post-processing steps is evaluated. The proposed pipeline is able to classify the dispersion tests with an accuracy of 85.92 %. The results highlight Layer-wise Relevance Propagation and HiResCAM as the most suitable XAI methods for this use case.