@article{WagnerFrankeKrauseetal., author = {Wagner, Marcus and Franke, Markus and Krause, Tom Janis and Heinle, Ingo}, title = {Data augmentation of material properties for machine learning in industrial production - a case study in an automotive press shop}, series = {Engineering Computations : International journal for computer-aided engineering and software}, journal = {Engineering Computations : International journal for computer-aided engineering and software}, publisher = {Emerald Publishing}, address = {Leeds}, doi = {10.1108/EC-08-2024-0787}, pages = {18}, abstract = {In the context of industrial production, the utilisation of data recording and processing techniques is becoming increasingly prevalent across the manufacturing sector. The solutions integrate sensors, facilitate the transmission of data, and enable data-driven decision-making, thereby reducing downtime and optimising quality. However, challenges emerge due to the limited non-transferable data or models between processes. Alterations to the production process can render collected data invalid, resulting in restricted datasets and potential overfitting. To address these issues, techniques such as data augmentation are employed. This study aims to develop a data augmentation methodology applicable in dynamic, data-scarce production environments, enhancing the robustness of regressor predictions.}, subject = {Data augmentation}, language = {en} }