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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References
Brecher, C., Klocke, F., Wellmann, F., et al.: Lernende Produktionssysteme. In: Schuh, G., Brecher, C., Klocke, F., Schmitt, R. (eds.) Engineering Valley –Internet of Production auf dem RWTH Aachen Campus. Apprimus, Aachen (2017)
Möhring, H.C., Wiederkehr, P., Erkorkmaz, K., et al.: Self-optimizing machining systems. CIRP Ann. 69(2), 740–763 (2020). https://doi.org/10.1016/j.cirp.2020.05.007
Pfeiffer, G.: Positions- und richtungsabhängige Abdrängungskompensation von 3-Achs-Werkzeugmaschinen bei der Schruppbearbeitung. TU Darmstadt (2019)
Jiang, Q., Yan, S., Yan, X., et al.: Data-driven individual-joint learning framework for nonlinear process monitoring. Control Eng. Pract. 95, 104235 (2020). https://doi.org/10.1016/j.conengprac.2019.104235
MARPOSS Monitoring Solutions GmbH: Artis. https://artis.de/ger. Accessed 08 Apr 2021
Wellmann, F.: Datengetriebene, kontextadaptive Produktivitätssteigerung von NC-Zerspanprozessen. RWTH Aachen University (2019)
Aminikhanghahi, S., Cook, D.J.: A survey of methods for time series change point detection. Knowl. Inf. Syst. 51(2), 339–367 (2016). https://doi.org/10.1007/s10115-016-0987-z
Spiegel, S., Gaebler, J., Lommatzsch, A., et al.: Pattern recognition and classification for multivariate time series. In: Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data (SensorKDD), New York, NY, USA, pp. 34–42. Association for Computing Machinery (2011). https://doi.org/10.1145/2003653.2003657
Cassisi, C., Prestifilippo, M., Cannata, A., et al.: Probabilistic reasoning over seismic time series: volcano monitoring by hidden Markov models at Mt Etna. Pure Appl. Geophys. 173, 2365–2386 (2016). https://doi.org/10.1007/s00024-016-1284-1
Gharghabi, S., Ding, Y., Yeh, C.M., et al.: Matrix profile VIII: domain agnostic online semantic segmentation at superhuman performance levels. In: IEEE International Conference on Data Mining (ICDM), New Orleans, LA, USA, pp. 117–126. IEEE Computer Society (2017). https://doi.org/10.1109/ICDM.2017.21
Yeh, C.M., Kavantzas, N., Keogh, E.: Matrix profile VI: meaningful multidimensional motif discovery. In: IEEE International Conference on Data Mining (ICDM), New Orleans, LA, USA, pp. 565–574. IEEE Computer Society (2017). https://doi.org/10.1109/ICDM.2017.66
SciPy Documentation: Peak Prominences. https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.peak_prominences.html. Accessed 17 May 2021
Siemens, A.G.: SINUMERIK 840D sl Commissioning Manual. 10/2020 (2020).
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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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