An integrated approach to improve effectiveness of industrial multi-factor statistical investigations
Loading...
Date
item.page.doi
item.page.thesis.degree.name
item.page.thesis.degree.level
item.page.thesis.degree.discipline
item.page.thesis.degree.department
item.page.thesis.degree.grantor
item.page.thesis.degree.advisor
item.page.thesis.degree.committeeMember
Journal Title
Journal ISSN
Volume Title
Publisher
CEUR Workshop Proceedings
Abstract
An approach was developed for computer statistical analysis of big, multi-dimensional arrays of technology parameters and industrial product qual-ity indexes. It provides fully objective, mathematically comprehensive, scien-tifically grounded and physically interpretable description of the manufacturing factor effects on the performance of an industrial product. The approach inte-grates a basic Data Mining exploratory technique, multiple regression models construction and Monte-Carlo simulations. The approach was applied to indus-trial statistical arrays investigations for the ASTM A514 steel. The results ob-tained are in a good accordance with the known Material Science data and were confirmed in industry
Description
Citation
Miroshnichenko, V., & Simkin, A. (2020). An integrated approach to improve effectiveness of industrial multi-factor statistical investigations. Computer Modeling and Intelligent Systems (CMIS-2020) : proceedings of the Third International Workshop. CEUR Workshop Proceedings, 2608, 526–535.
Miroshnichenko V., Simkin A. An integrated approach to improve effectiveness of industrial multi-factor statistical investigations. Computer Modeling and Intelligent Systems (CMIS-2020) : proceedings of the Third International Workshop. CEUR Workshop Proceedings. 2020. Vol. 2608. P. 526–535.
Miroshnichenko V., Simkin A. An integrated approach to improve effectiveness of industrial multi-factor statistical investigations. Computer Modeling and Intelligent Systems (CMIS-2020) : proceedings of the Third International Workshop. CEUR Workshop Proceedings. 2020. Vol. 2608. P. 526–535.
