Root Cause Analysis of Quality Deviations in Manufacturing
Read more at
http://www.fcc.chalmers.se/focus-areas/ict/root-cause-analysis-of-quality-deviations-in-manufacturing/
The vision of the project Root Cause Analysis of Quality Deviations in Manufacturing Using Machine Learning (RCA-ML) is to develop machine learning and big data analytics methods for identifying root causes of failures and quality deviations in manufacturing processes.
The recent developments in machine learning and big data analytics present opportunities to utilize the large amount of data that is gathered at manufacturing sites for analyzing the manufacturing processes from a quantitative and data-driven perspective.
The purpose of this project is to study and analyze machine learning methods for automating the process of identifying and back-tracking root causes in manufacturing.
Today the root causes of failures and quality deviations in manufacturing are usually identified using expert knowledge existing at each separate manufacturing site. The experience of the staff at the sites constitutes the basis for describing the causal correlations between different process steps and the output failures/quality deviations, and manual methods are then employed to identify the root causes for the failures.
By developing automated processes where the expert knowledge within the companies is digitalized through sensor solutions and used in machine learning methods, the companies ensure that the knowledge can be transferred easily between different manufacturing sites as well as secured for future use.
The developed methods within the project will be generic, module based, and easily extendable for applications in other industries where root cause analysis is crucial for maintaining high quality and throughput.
Manufacturing
Production, Research & Development
Better Quality, Saving Cost, Smarter Product or Service
Professional
Discovery, Prediction
Explainability and Interpretability, Machine Learning, Supervised
Sensor Data, Time series