Observational data-driven modeling and optimization of manufacturing processes
作者：
Highlights：
• Proposed an integrated variable selection and process parameter design methodology.
• Exploits observational data to model, control and improve process performance.
• Overcomes costs associated with intrusive controlled designed experiments.
• Proposed data-driven approach also identifies significant control variables.
• Promising results from a synthetic experimental study and a real world case study.
摘要
•Proposed an integrated variable selection and process parameter design methodology.•Exploits observational data to model, control and improve process performance.•Overcomes costs associated with intrusive controlled designed experiments.•Proposed data-driven approach also identifies significant control variables.•Promising results from a synthetic experimental study and a real world case study.
论文关键词：Parameter design,Observational data,Variable selection,Data-driven modeling,Response surface method,Meta-heuristic optimization
论文评审过程：Received 21 June 2017, Revised 5 August 2017, Accepted 12 October 2017, Available online 16 October 2017, Version of Record 5 November 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.10.028
