Abstract
Due to the emergence of standardized infrastructure for data acquisition and processing, the manufacturing industry collects time series data from machine sensors and numerical controls at large scale. While the necessity to enrich data with domain knowledge to derive tangible insights into the process is universally accepted, little effort has been spent on mining the data at hand. Domain agnostic methods developed by the data mining community often require setting many parameters or do not cover the essential characteristics of manufacturing data. In this paper, a novel approach is presented, which allows for an unsupervised, semantically meaningful segmentation of milling process data. Identifying such segments lays the foundation for cross-process comparisons and in-depth data analyses as the local differentiation of process features is facilitated. Thus, the algorithm aims to support time-consuming data-driven optimization methods uncovering productivity and quality potentials of milling processes.
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Ochel, J., Fey, M., Brecher, C. (2022). Semantically Meaningful Segmentation of Milling Process Data. In: Behrens, BA., Brosius, A., Drossel, WG., Hintze, W., Ihlenfeldt, S., Nyhuis, P. (eds) Production at the Leading Edge of Technology. WGP 2021. Lecture Notes in Production Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-78424-9_36
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DOI: https://doi.org/10.1007/978-3-030-78424-9_36
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