| Time series data continuously generated during CNC machining processes contain essential information on their performance and efficiency, but also on produced parts. Using industrial edge devices, among low frequency and event data also high frequency data at 1 kHz can be generated. Within these large, multi-dimensional datasets, interrelationships and semantics are usually not available without additional processing. Among others, pattern recognition algorithms have been used to find repeated patterns within the data or to find areas with constant technological parameters. However, the approaches often lack reference to parts and part characteristics, leading to individual analytics solutions. To face this issue, a data-driven, multi-level segmentation is developed. The first part of the algorithm extracts production processes of parts of the same product type; the second part segments these production process data by each individual part. For the second method, a discrete rule-based pattern recognition algorithm is proposed to perform the segmentation. Before, the available data are contextualized to gain additional process knowledge, enabling the effective use of the implicitly available information. This part-focused segmentation is verified on milling processes and not only allows for more effective analyses of CNC machining processes, but also enables the analysis of historical data which increases their value significantly. |
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