TY - JOUR A1 - Ziegler, Cedric C. A1 - Dobhan, Alexander A1 - Heusinger, Moritz T1 - Applications of Neural Networks in Engineer-to-order Environment JF - Procedia CIRP N2 - Machine tool selection and quotation costing have a low level of automation in today's engineer-to-order environments. The decision-making process is based on imprecise human judgment even if all final product characteristics are known. To improve precision and save time, we propose to apply artificial neural networks that are trained on data of already produced products. In particular, we address the selection of several grinding wheels to produce a milling cutter, which is a multiple-criteria decision, using multi-label classification. The quotation costing is a single output regression problem. In both tasks, our results on real-world data show high accuracy. Y1 - 2022 U6 - https://doi.org/10.1016/j.procir.2022.09.052 VL - 112 SP - 140 EP - 145 PB - Elsevier ER - TY - JOUR A1 - Wahyudi, Vincent A1 - Ziegler, Cedric C. A1 - Frieß, Matthias A1 - Schramm, Stefan A1 - Lang, Constantin A1 - Eberhardt, Lars A1 - Freund, Fabian A1 - Dobhan, Alexander A1 - Storath, Martin T1 - A computer vision system for recognition and defect detection for reusable containers JF - Machine Vision and Applications N2 - Abstract Small load carriers (SLCs) are standardized reusable containers used to transport and protect customer goods in many manufacturers. Throughout the life cycle of the SLCs, they will be collected, manually checked for defects (wear, cracks, and residue on the surface), and cleaned by specialized logistic companies. Human operators in small to medium-sized companies manually evaluate the defects due to the variety and degree of possible defects and varying customer needs. This manual evaluation is not scalable and prone to errors. This work aims to fill this gap by proposing a computer vision system that can recognize the SLC type for inventory management and perform defect detection automatically. First, we develop a camera portal, consisting of standard components, that capture the relevant surfaces of the SLC. A labeled dataset of 17,530 images of 34 different SLCs with their defect status was recorded using this camera portal. We trained a classification model (ConvNeXt) using our dataset to predict the different types of SLCs achieving 100% class prediction accuracy. For defect detection, we explore eight state-of-the-art (SOTA) anomaly detection models that achieved high rankings in the MVTec industrial anomaly detection benchmark. These models are trained using default hyperparameters and the two highest-scoring models were chosen and fine-tuned. The best-fine-tuned models based on “Area under the Receiver Operating Characteristic Curve (AUROC)” are PatchCore (0.811) and DRAEM (0.748). These results indicate that there is still potential for improvement in the automation of defect detection of SLCs. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:863-opus-57430 SN - 0932-8092 VL - 36 IS - 2 PB - Springer Science and Business Media LLC ER - TY - CHAP A1 - Ziegler, Cedric C. A1 - Ising, Julia A1 - Dobhan, Alexander A1 - Storath, Martin T1 - Computer Vision in Reusable Container Management - Requirements, Conception, and Data Acquisition T2 - Logistik und Supply Chain Management N2 - In container management, the reuse of small load carriers is a business alternative to disposal carriers. Reusable container management is furthermore a solution to improve the environmental impact of the logistic industry. The sorting and stock management of small load carriers are today primarily manual work and have consequently a low level of automation. In order to increase the automation of returnable containers, it is crucial to establish a computer vision system that (i) classifies the containers and (ii) detects potential defects or stains. This paper provides an overview and a discussion of the applications that are already in use. Object detection is necessary for many actions in the container management business processes, such as inventory and stock management. Detection of defects on the small load carrier is required for scrapping the carriers to ensure a smooth process in any business process involving the carrier and to decide whether additional process steps, e.g., cleaning, are required. The literature review in this paper establishes the demand for computer vision detection and shows the project setup necessary to conduct research in this area. The comparison with other applications of defect and anomaly detection supports the applicability and shows the need for further research in this specific academic field. This leads to a project outline and the research provides the technical implementation of the detections in container management. Accordingly, the research provides a work- flow guide from data acquisition to a high-quality dataset of labeled anomalies of small load carriers. Y1 - 2023 SN - 9783863099411 U6 - https://doi.org/10.20378/irb-92408 SN - 2750-8277 SP - 107 EP - 122 PB - University of Bamberg Press ER -