TY - GEN
T1 - Approaches to identify relevant process variables in injection moulding using beta regression and SVM
AU - Tripathi, Shailesh
AU - Straßer, Sonja
AU - Mittermayr, Christian
AU - Dehmer, Matthias
AU - Jodlbauer, Herbert
N1 - Publisher Copyright:
Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
PY - 2019
Y1 - 2019
N2 - In this paper, we analyze data from an injection moulding process to identify key process variables which influence the quality of the production output. The available data from the injection moulding machines provide information about the run-time, setup parameters of the machines and the measurements of different process variables through sensors. Additionally, we have data about the total output produced and the number of scrap parts. In the first step of the analysis, we preprocessed the data by combining the different sets of data for a whole process. Then we extracted different features, which we used as input variables for modeling the scrap rate. For the predictive modeling, we employed three different models, beta regression with the backward selection, beta boosting with regularization and SVM regression with the radial kernel. All these models provide a set of common key features which affect the scrap rates.
AB - In this paper, we analyze data from an injection moulding process to identify key process variables which influence the quality of the production output. The available data from the injection moulding machines provide information about the run-time, setup parameters of the machines and the measurements of different process variables through sensors. Additionally, we have data about the total output produced and the number of scrap parts. In the first step of the analysis, we preprocessed the data by combining the different sets of data for a whole process. Then we extracted different features, which we used as input variables for modeling the scrap rate. For the predictive modeling, we employed three different models, beta regression with the backward selection, beta boosting with regularization and SVM regression with the radial kernel. All these models provide a set of common key features which affect the scrap rates.
KW - Beta Regression
KW - Injection Moulding
KW - Scrap Rate Prediction
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=85072958533&partnerID=8YFLogxK
U2 - 10.5220/0007926502330242
DO - 10.5220/0007926502330242
M3 - Conference contribution
AN - SCOPUS:85072958533
T3 - DATA 2019 - Proceedings of the 8th International Conference on Data Science, Technology and Applications
SP - 233
EP - 242
BT - DATA 2019 - Proceedings of the 8th International Conference on Data Science, Technology and Applications
A2 - Hammoudi, Slimane
A2 - Quix, Christoph
A2 - Bernardino, Jorge
PB - SciTePress
T2 - 8th International Conference on Data Science, Technology and Applications, DATA 2019
Y2 - 26 July 2019 through 28 July 2019
ER -