Authors:
Sonja Strasser
;
Shailesh Tripathi
and
Richard Kerschbaumer
Affiliation:
Production and Operations Management, University of Applied Sciences Upper Austria, Wehrgrabengasse 1-3, Steyr and Austria
Keyword(s):
Process Parameter Setting, Manufacturing, Model Selection, Regression Analysis, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Predictive Modeling
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
In traditional manufacturing processes the selection of appropriate process parameters can be a difficult task which relies on rule-based schemes, expertise and domain knowledge of highly skilled workers. Usually the parameter settings remain the same for one production lot, if an acceptable quality is reached. However, each part processed has its own history and slightly different properties. Individual parameter settings for each part can further increase the quality and reduce scrap. Machine learning methods offer the opportunity to generate models based on experimental data, which predict optimal parameters depending on the state of the produced part and its manufacturing conditions. In this paper, we present an approach for selecting variables, building and evaluating models for adaptive parameter settings in manufacturing processes and the application to a real-world use case.