Overcoming Data Scarcity in the Quality Control of Safety-Critical Fibre-Reinforced Composites by means of Transfer and Curriculum Learning

Fibre-reinforced composites are one promising material class to provide a response to the increasing environmental awareness within society. Due to their excellent lightweight potential, fibre-reinforced composites are preferably employed in safety-critical applications, requiring extensive quality control (QC). However, commercially available QC systems are only able to measure fibre deviations, not directly detecting the error itself. In consequence, a worker is required to perform a manual inspection. Artificial intelligence and especially convolutional neural networks (CNN) offer the opportunity to directly detect and classify defects. However, to train the corresponding algorithms large amounts of data are required, which are often inaccessible in production. Artificial augmentation of the available data is a popular approach to tackle this problem, yet, resulting most of the time in undesired overfitting of the CNN. Therefore, in this contribution we examine the transfer of human learning behaviour elements to algorithms in form of transfer learning (TL) and curriculum learning (CL). The overall aim is to research, whether CL and TL are appropriate approaches to address data scarcity in e.g. production environments. Therefore, we perform our research on the error detection of three-dimensional shaped fibre-reinforced textiles

Scalable Concept Extraction in Industry 4.0

The industry 4.0 is leveraging digital technologies and machine learning techniques to connect and optimize manufacturing processes. Central to this idea is the ability to transform raw data into human understandable knowledge for reliable data-driven decision-making. Convolutional Neural Networks (CNNs) have been instrumental in processing image data, yet, their ``black box'' nature complicates the understanding of their prediction process. In this context, recent advances in the field of eXplainable Artificial Intelligence (XAI) have proposed the extraction and localization of concepts, or which visual cues intervene on the prediction process of CNNs. This paper tackles the application of concept extraction (CE) methods to industry 4.0 scenarios. To this end, we modify a recently developed technique, ``Extracting Concepts with Local Aggregated Descriptors'' (ECLAD), improving its scalability. Specifically, we propose a novel procedure for calculating concept importance, utilizing a wrapper function designed for CNNs. This process is aimed at decreasing the number of times each image needs to be evaluated. Subsequently, we demonstrate the potential of CE methods, by applying them in three industrial use cases. We selected three representative use cases in the context of quality control for material design (tailored textiles), manufacturing (carbon fiber reinforcement), and maintenance (photovoltaic module inspection). In these examples, CE was able to successfully extract and locate concepts directly related to each task. This is, the visual cues related to each concept, coincided with what human experts would use to perform the task themselves, even when the visual cues were entangled between multiple classes. Through empirical results, we show that CE can be applied for understanding CNNs in an industrial context, giving useful insights that can relate to domain knowledge