Approaches to Identify Relevant Process Variables in Injection Moulding using Beta Regression and SVM

Shailesh Tripathi, Sonja Strasser, Christian Mittermayr, Matthias Dehmer, Herbert Jodlbauer

2019

Abstract

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.

Paper Citation

in Harvard Style

Tripathi S., Strasser S., Mittermayr C., Dehmer M. and Jodlbauer H. (2019). Approaches to Identify Relevant Process Variables in Injection Moulding using Beta Regression and SVM.In Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-377-3, pages 233-242. DOI: 10.5220/0007926502330242

in Bibtex Style

@conference{data19,
author={Shailesh Tripathi and Sonja Strasser and Christian Mittermayr and Matthias Dehmer and Herbert Jodlbauer},
title={Approaches to Identify Relevant Process Variables in Injection Moulding using Beta Regression and SVM},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2019},
pages={233-242},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007926502330242},
isbn={978-989-758-377-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Approaches to Identify Relevant Process Variables in Injection Moulding using Beta Regression and SVM
SN - 978-989-758-377-3
AU - Tripathi S.
AU - Strasser S.
AU - Mittermayr C.
AU - Dehmer M.
AU - Jodlbauer H.
PY - 2019
SP - 233
EP - 242
DO - 10.5220/0007926502330242