- MACHINE LEARNING FOR THE PREDICTION OF EDGE CRACKING IN SHEET METAL FORMING PROCESSES -
- MACHINE LEARNING FOR THE PREDICTION OF EDGE CRACKING IN SHEET METAL FORMING PROCESSES -
Machine Learning for the Prediction of Edge Cracking in Sheet Metal Forming Processes
The chapter "Machine Learning for the Prediction of Edge Cracking in Sheet Metal Forming Processes" resulted from the SAFEFORMING project, led by Toolpresse between 2017 and 2020.
- The book "Machine Learning and Artificial Intelligence with Industrial Applications" was published this year and consists of a compendium of information and case studies that provide a comprehensive view on computer learning and artificial intelligence and their industrial applications.
- The chapter "Machine Learning for the Prediction of Edge Cracking in Sheet Metal Forming Processes" resulted from the SAFEFORMING project, led by Toolpresse between 2017 and 2020.
- The objective of the project was to evaluate the performance of several computational learning algorithms in the prediction of defects in the metal stamping process, namely the occurrence of cracking.
- Seven different unique classifiers and two types of ensemble models (majority voting and stacking) were used to make predictions, based on a dataset generated from the results of two types of mechanical tests: the uniaxial tensile test and the hole expansion. The performance evaluation was based on four metrics: accuracy, recall, precision and F-score, with the F-score considered the most relevant. The best performances were achieved by the ensemble majority voting models. The ROC curve of a majority model was also evaluated in order to confirm the predictive capabilities of the model. Overall, ML algorithms are able to predict the occurrence of edge cracking satisfactorily.
Access full article on: https://www.researchgate.net/publication/357336230_Machine_Learning_for_the_Prediction_of_Edge_Cracking_in_Sheet_Metal_Forming_Processes https://link.springer.com/book/10.1007/978-3-030-91006-8
- 20/04/2022