@article{RoseZaehrSchnicketal.2011, author = {Rose, Sascha and Z{\"a}hr, Julia and Schnick, Michael and F{\"u}ssel, Uwe and Goecke, Sven-Frithjof and H{\"u}bner, Mark}, title = {Arc attachments on aluminium during tungsten electrode positive polarity in TIG welding of aluminium}, series = {In: Welding in the World 55 (2011) 9-10, pp 91-99}, volume = {55}, journal = {In: Welding in the World 55 (2011) 9-10, pp 91-99}, number = {9-10}, issn = {1878-6669}, doi = {10.1007/bf03321325}, pages = {91 -- 99}, year = {2011}, abstract = {In modem AC power sources, both balance and current of EP (tungsten electrode positive) and EN (tungsten electrode negative) can be readily adjusted. According to conventional assumptions the surface of aluminium workpieces is cleaned during EP but the tungsten electrode is more heated due to electron work function. During EN the tungsten electrode cools down and the workpiece is melted. Therefore, optimal settings of the power source mostly aim for maximal penetration and minimal electrode wear. This paper presents a study of arc attachments in TIG welding on aluminium. Two different arc attachments to the cathodic workpiece could be attested with only one of them actually cleaning the surface. The first one is a spot mode (highly dynamic) that attaches to the edges of oxide layers and cracks them. The second one is also a spot mode, but it attaches to weld pools. It does not clean the surface but heats the base material intensively. Both arc attachment modes compete against each other. As a result, the cleaning area narrows with wider weld pools due to an increasing attachment to the weld pool during EP. The influences on the arc attachment to the aluminium cathode as well as the contradictions in present literature are discussed. Possible adjustments of the AC balance for optimized processes are proposed.}, language = {en} } @article{BoerschFuesselGreschetal.2016, author = {Boersch, Ingo and F{\"u}ssel, Uwe and Gresch, Christoph and Großmann, Christoph and Hoffmann, Benjamin}, title = {Data mining in resistance spot welding - A non-destructive method to predict the welding spot diameter by monitoring process parameters}, series = {The International Journal of Advanced Manufacturing Technology}, journal = {The International Journal of Advanced Manufacturing Technology}, editor = {London, Springer}, doi = {10.1007/s00170-016-9847-y}, year = {2016}, abstract = {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.}, language = {en} } @article{BoerschFuesselGreschetal.2018, author = {Boersch, Ingo and F{\"u}ssel, Uwe and Gresch, Christoph and Großmann, Christoph and Hoffmann, Benjamin}, title = {Data mining in resistance spot welding : A non-destructive method to predict the welding spot diameter by monitoring process parameters}, series = {The International Journal of Advanced Manufacturing Technology}, journal = {The International Journal of Advanced Manufacturing Technology}, number = {2016}, doi = {10.1007/s00170-016-9847-y}, url = {http://nbn-resolving.de/urn:nbn:de:1111-201701081182}, pages = {1 -- 15}, year = {2018}, language = {en} }