2022/02/07 by Mahmoud Kheir-Eddine, Kheir-Eddine, Mahmoud, Michael Banf +3
Computer Science · Engineering · #Advanced Machining and Optimization Techniques #Advanced machining processes and optimization #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Fault Diagnosis Techniques #Machine Learning (cs.LG) #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.2202.03068
arxiv created 2022/02/07 · openalex publication_date 2022/02/07 · arxiv updated 2022/02/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Milling machines form an integral part of many industrial processing chains. As a consequence, several machine learning based approaches for tool wear detection have been proposed in recent years, yet these methods mostly deal with standard milling machines, while machinery designed for more specialized tasks has gained only limited attention so far. This paper demonstrates the application of an acceleration sensor to allow for convenient condition monitoring of such a special purpose machine, i.e. round seam milling machine. We examine a variety of conditions including blade wear and blade breakage as well as improper machine mounting or insufficient transmission belt tension. In addition, we presents different approaches to supervised failure recognition with limited amounts of training data. Hence, aside theoretical insights, our analysis is of high, practical importance, since retrofitting older machines with acceleration sensors and an on-edge classification setup comes at low cost and effort, yet provides valuable insights into the state of the machine and tools in particular and the production process in general.