Authors:
Shailesh Tripathi
1
;
Sonja Strasser
1
;
Christian Mittermayr
2
;
Matthias Dehmer
1
and
Herbert Jodlbauer
1
Affiliations:
1
Production and Operations Management, University of Applied Sciences Upper Austria, Wehrgrabengasse 1-3, Steyr and Austria
;
2
Greiner Packaging International GmbH, Greinerstrasse 70, 4550 Kremsmuenster and Austria
Keyword(s):
Injection Moulding, Beta Regression, SVM, Scrap Rate Prediction.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Business Analytics
;
Computational Intelligence
;
Data Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Predictive Modeling
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Statistics Exploratory Data Analysis
;
Support Vector Machines and Applications
;
Symbolic Systems
;
Theory and Methods
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.