{"ID":2879536,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.16553","arxiv_id":"2508.16553","title":"TinyML Towards Industry 4.0: Resource-Efficient Process Monitoring of a Milling Machine","abstract":"In the context of industry 4.0, long-serving industrial machines can be retrofitted with process monitoring capabilities for future use in a smart factory. One possible approach is the deployment of wireless monitoring systems, which can benefit substantially from the TinyML paradigm. This work presents a complete TinyML flow from dataset generation, to machine learning model development, up to implementation and evaluation of a full preprocessing and classification pipeline on a microcontroller. After a short review on TinyML in industrial process monitoring, the creation of the novel MillingVibes dataset is described. The feasibility of a TinyML system for structure-integrated process quality monitoring could be shown by the development of an 8-bit-quantized convolutional neural network (CNN) model with 12.59kiB parameter storage. A test accuracy of 100.0% could be reached at 15.4ms inference time and 1.462mJ per quantized CNN inference on an ARM Cortex M4F microcontroller, serving as a reference for future TinyML process monitoring solutions.","short_abstract":"In the context of industry 4.0, long-serving industrial machines can be retrofitted with process monitoring capabilities for future use in a smart factory. One possible approach is the deployment of wireless monitoring systems, which can benefit substantially from the TinyML paradigm. This work presents a complete Tiny...","url_abs":"https://arxiv.org/abs/2508.16553","url_pdf":"https://arxiv.org/pdf/2508.16553v1","authors":"[\"Tim Langer\",\"Matthias Widra\",\"Volkhard Beyer\"]","published":"2025-08-22T17:21:56Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CV\",\"cs.ET\",\"eess.SP\",\"eess.SY\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}