TY - GEN
T1 - Linear vs. Symbolic Regression for Adaptive Parameter Setting in Manufacturing Processes
AU - Strasser, Sonja
AU - Zenisek, Jan
AU - Tripathi, Shailesh
AU - Schimpelsberger, Lukas
AU - Jodlbauer, Herbert
N1 - Funding Information:
The authors gratefully acknowledge financial support with the projects ADAPT and BAPDEC, which are funded by the country of Upper Austria in their program ?Innovative Upper Austria 2020? and the project ?Smart Factory Lab?, which is funded by the European Fund for regional development (EFRE) and the country of Upper Austria as part of the program ?Investing in Growth and Jobs 2014?2020?.
Funding Information:
Acknowledgements. The authors gratefully acknowledge financial support with the projects ADAPT and BAPDEC, which are funded by the country of Upper Austria in their program “Innovative Upper Austria 2020” and the project “Smart Factory Lab”, which is funded by the European Fund for regional development (EFRE) and the country of Upper Austria as part of the program “Investing in Growth and Jobs 2014–2020”.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Product and process quality is playing an increasingly important role in the competitive success of manufacturing companies. To ensure a high quality level of the produced parts, the appropriate selection of parameters in manufacturing processes plays in important role. Traditional approaches for parameter setting rely on rule-based schemes, expertise and domain knowledge of highly skilled workers or trial and error. Automated and real-time adjustment of critical process parameters, based on the individual properties of a part and its previous production conditions, have the potential to reduce scrap and increase the quality. Different machine learning methods can be applied for generating parameter estimation models based on experimental data. In this paper, we present a comparison of linear and symbolic regression methods for an adaptive parameter setting approach. Based on comprehensive real-world data, collected in a long-term study, multiple models are generated, evaluated and compared with regard to their applicability in the studied approach for parameter setting in manufacturing processes.
AB - Product and process quality is playing an increasingly important role in the competitive success of manufacturing companies. To ensure a high quality level of the produced parts, the appropriate selection of parameters in manufacturing processes plays in important role. Traditional approaches for parameter setting rely on rule-based schemes, expertise and domain knowledge of highly skilled workers or trial and error. Automated and real-time adjustment of critical process parameters, based on the individual properties of a part and its previous production conditions, have the potential to reduce scrap and increase the quality. Different machine learning methods can be applied for generating parameter estimation models based on experimental data. In this paper, we present a comparison of linear and symbolic regression methods for an adaptive parameter setting approach. Based on comprehensive real-world data, collected in a long-term study, multiple models are generated, evaluated and compared with regard to their applicability in the studied approach for parameter setting in manufacturing processes.
KW - Genetic programming
KW - Linear regression
KW - Manufacturing
KW - Process parameter setting
KW - Symbolic regression
UR - http://www.scopus.com/inward/record.url?scp=85070205513&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-26636-3_3
DO - 10.1007/978-3-030-26636-3_3
M3 - Conference contribution
AN - SCOPUS:85070205513
SN - 9783030266356
T3 - Communications in Computer and Information Science
SP - 50
EP - 68
BT - Data Management Technologies and Applications - 7th International Conference, DATA 2018, Revised Selected Papers
A2 - Quix, Christoph
A2 - Bernardino, Jorge
PB - Springer
T2 - 7th International Conference on Data Science, Technology and Applications, DATA 2018
Y2 - 26 July 2018 through 28 July 2018
ER -