TY - JOUR A1 - Boersch, Ingo A1 - Füssel, Uwe A1 - Gresch, Christoph A1 - Großmann, Christoph A1 - Hoffmann, Benjamin ED - London, Springer T1 - Data mining in resistance spot welding - A non-destructive method to predict the welding spot diameter by monitoring process parameters JF - The International Journal of Advanced Manufacturing Technology N2 - Resistance spot welding is the dominant process in the present mass production of steel constructions without sealing requirements with single sheet thicknesses up to 3 mm. Two of the main applications of resistance spot welding are the automobile and the railway vehicle manufacturing industry. The majority of these connections has safety-related character and therefore they must not fall below a certain weld diameter. Since resistance spot welding has been established, this weld diameter has been usually used as the gold standard. Despite intensive efforts, there has not been found yet a reliable method to detect this connection quality non-destructively. Considerable amounts of money and steel sheets are wasted on making sure that the process does not result in faulty joints. The indication of the weld diameter by in-process monitoring in a reliable way would allow the quality documentation of joints during the welding process and additionally lead through demand-actuated milling cycles to a substantial decrease of electrode consumption. An annual, estimated reduction in the seven- to nine-figure range could be achieved. It has an important impact, because the economics of the process is essentially characterized by the electrode caps (Klages 24). We propose a simple and straightforward approach using data mining techniques to accurately predict the weld diameter from recorded data during the welding process. In this paper, we describe the methods used during data preprocessing and segmentation, feature extraction and selection, and model creation and validation. We achieve promising results during an analysis of more than 3000 classified welds using a model tree as a predictor with a success rate of 93 %. In the future, we hope to validate our model with unseen welding data and implement it in a real world application. KW - Resistance spot welding KW - Electrode life prognosis KW - Data mining KW - Feature extraction KW - Model selection Y1 - 2016 UR - http://link.springer.com/article/10.1007/s00170-016-9847-y U6 - https://doi.org/10.1007/s00170-016-9847-y ER - TY - GEN A1 - Andrzejewski, Dennis A1 - Haeusler, N. A1 - Ledwon, P. A1 - Boersch, Ingo A1 - Beck, Eberhard T1 - Die Analyse des Entscheidungsprozesses in der Senologie zur Konzeption eines entscheidungsunterstützenden Systems Y1 - 2013 N1 - Poster unsd Abstract, 33. Jahrestagung der Deutschen Gesellschaft für Senologiem 27. - 29. Juni 2013, München ER - TY - GEN A1 - Andrzejewski, Dennis A1 - Boersch, Ingo A1 - Schrader, Thomas A1 - Haeusler, N. A1 - Ledwon, P. A1 - Beck, Eberhard T1 - Potentials of Computer-aided Decision Support Systems in Medicine Y1 - 2014 N1 - Abstract und Poster, 31. Deutsche Krebskongress, 19. - 22.02.2014, Berlin ER - TY - CHAP A1 - Vogel, Vanessa A1 - Boersch, Ingo ED - Thiem, Gerhard T1 - Prognose des freien Willens - Machbarkeit und erste Ergebnisse T2 - 18. Nachwuchswissenschaftlerkonferenz (NWK) : 31. Mai 2017 an der Hochschule Mittweida Y1 - 2017 SP - 341 EP - 345 PB - Hochschule Mittweida CY - Mittweida ER -