Grinding Burn Prediction with Artificial Neural Networks based on Grinding Parameters
Konferenz: Smart SysTech 2019 - European Conference on Smart Objects, Systems and Technologies
04.06.2019 - 05.06.2019 in Munich, Germany
Tagungsband: ITG-Fb. 289: Smart SysTech 2019
Seiten: 5Sprache: EnglischTyp: PDFPersönliche VDE-Mitglieder erhalten auf diesen Artikel 10% Rabatt
Reser, Christian; Reich, Christoph (Institute for Cloud Computing and IT Security, Furtwangen University of Applied Science, Furtwangen, Germany)
Cylindrical grinding is an important process in the manufacturing industry. During this process, the problem of grinding burn may appear, which can cause the workpiece to be worthless. In this work, a machine learning neural network approach based on a three layer perceptron is used to predict grinding burn based on the process parameters to prevent damage. A small dataset of 21 samples was gathered at a specific machine, grinding always the same element type with different process parameters. Each workpiece got a label from 0 to 3 after the process, indicating the severity of grinding burn. To get a robust neural network model, the dataset has been scaled by augmentation controlled by grinding experts, to generate more samples for training a neural network model. As a result, the model is able to predict the severity of grinding burn in a multiclass classification and it turned out that even with little data, the model performed well.