Predicting tensile strength of material extrusion parts during the pre-process using neural networks

• Published in: International journal of advanced manufacturing technology Vol. 128; no. 11-12; pp. 5129 - 5138
• Main Authors: Schmidt, Carsten, Berchtold, Florian, Griesbaum, Rainer, Sehrt, Jan T., Finsterwalder, Florian
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2023; Springer Nature B.V
• Subjects: Algorithms; CAE) and Design; Computer-Aided Engineering (CAD; Data reduction; Engineering; Extrusion; Industrial and Production Engineering; Mechanical Engineering; Media Management; Neural networks; Original Article; Prediction models; Process parameters; Redevelopment; Tensile strength; Thickness; Training; Material extrusion; Parameter optimization; Neural networks; Quality; Prediction
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-023-12256-6

Quantitative quality characteristics of additive manufactured parts are influenced by parameters selected in the preparation process (pre-process), especially in the material extrusion process. As a result, a prediction of the tensile strength of manufactured parts is hardly possible, which significantly reduces the usability of the process. In this paper a neural network approach is used to predict the tensile strength during the pre-process. The parameters investigated are print speed, number of shells, layer thickness, nozzle temperature and infill density. A prediction with a mean absolute percentage error ( MAPE ) of 2.54% could be achieved for randomly generated process parameters using a training data set of 243 samples. This exceeds the best prediction accuracies of the current literature which is between 2.56 and 3.34%. However, this research is particularly different in that, unlike the existing literature, the developed prediction models were tested with untrained random parameter values in a properly conducted test. With a data reduction to a data volume of 32 samples the used approach achieved already a MAPE of 4.15%. The neural network approach outperformed a multiple linear regression even at low training data volume. This publication differs from previously published research activities due to the achieved prediction accuracies on random parameter sets, the number of investigated parameters and the sample size. Users are provided with an algorithm and its procedure to predict the tensile strength which can be adapted to the respective application with the help of company data.