This study addresses the relevance of high-quality pharmaceutical products and proposes a cyber-physical system-based quality control approach that differentiates between usable and non-usable capsules. With a balanced accuracy of 95.71 percent, we set a new benchmark in detecting defective capsules (e.g., scratches) in the pharmaceutical domain automatically using image classification with convolutional neural networks. Following the growing need for efficiency and quality in manufactured products, we contribute to the technical evolution within the industrial sector. Our model does not require further data processing, allowing implementation in different production environments. While our approach advances the efficiency of industrial processes such as quality control to identify product defects in real-time, our approach represents a low-cost alternative to comparatively expensive and technical complex inspections (e.g., X-ray) which is of high practical relevance.
Breitenbach, Johannes; Gross, Jan; Buettner, Ricardo; Baumgartl, Hermann; Bayerlein, Samuel; Flathau, Dennis; and Sauter, Daniel, "A Deep Learning-Based Cyber-Physical System for the Detection of Defective Capsules Using a Transfer Learning Strategy" (2022). PACIS 2022 Proceedings. 287.
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