Smart sheet metal forming: importance of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine for predicting wear states during blanking

In consequence of high cost pressure and the progressive globalization of markets, blanking, which represents the most economical process in the value chain of manufacturing companies, is particularly dependent on reducing machine downtimes and increasing the degree of utilization. For this purpose,...

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Bibliographic Details
Published in:	Journal of intelligent manufacturing Vol. 33; no. 1; pp. 259 - 282
Main Authors:	Kubik, Christian, Knauer, Sebastian Michael, Groche, Peter
Format:	Journal Article
Language:	English
Published:	New York, NY Springer US 01-01-2022 Springer Nature B.V
Subjects:	Abrasive wear Blanking Business and Management Classification Control Data acquisition Data driven process optimization Feature extraction Globalization Machine learning in blanking Machines Manufacturing Mechatronics Metal forming Metal sheets Model accuracy Performance enhancement Position sensing Preprocessing Processes Production Resilient manufacturing Robotics Support vector machines Transformations (mathematics) Value engineering Machine learning in blanking Resilient manufacturing Data driven process optimization
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Summary:	In consequence of high cost pressure and the progressive globalization of markets, blanking, which represents the most economical process in the value chain of manufacturing companies, is particularly dependent on reducing machine downtimes and increasing the degree of utilization. For this purpose, it is necessary to be able to make a real-time prediction about the current and future process conditions even at high production rates. Therefore, this study investigates the influence of data acquisition, preprocessing and transformation on the performance of a multiclass support vector machine to classify abrasive wear states during blanking based on force signals. The performance of the model was quantitatively evaluated based on the model accuracy and the separability of the classes. As a result, it was shown, that the deviation of time series represents the key parameter for the resulting performance of the classification model and strongly depends on the sensor type and position, the preprocessing procedure as well as the feature extraction and selection. Furthermore, it is shown that the consideration of domain knowledge in the phases of data acquisition, preprocessing and transformation improves the performance of the classification model and is essential to successfully implement AI projects. Summarizing the findings of this study, trustworthy data sets play a crucial role for implementing an automated process monitoring as a basis for resilient manufacturing systems.
ISSN:	1572-8145 0956-5515 1572-8145
DOI:	10.1007/s10845-021-01789-w