Investigation of Multivariate Profile Monitoring on Complex Thin-walled Components Batch Machined using a Sliding Time Window Cluster Method

ABSTRACT

Aiming at solving the problems of low precision of quality monitoring and low sensitivity of fluctuation detection in the batch machining process of complex thin-walled parts, the multivariate profile monitoring approach was proposed. The extended error stream method was used to establish profile representation of the multi-operation machining processing fluctuation, which could describe the fluctuation of multi-part production cycle and the decline of production line represented by error sources. On this basis, the monitoring statistics of the batch complex thin-wall part machining process were built based on one-step ahead forecast error and Tsquare (OSFE-T square). Then, the profile monitoring method was developed based on the OSFE-T square monitoring model of batch parts on the different operation using the sliding time window cluster-based method. Through the Monte-Carlo simulation, the fraction correctly classified, sensitivity, specificity, false positive and false negative of the quality monitoring method were analyzed respectively, which proved that the proposed method had better performance. Finally, amachining simulation instance was presented including ten operations demonstrated the effectiveness of the machining error control method. Compared with other alternative methods, the proposed method in this paper has more advantages in the sensitivity of fluctuation detection.

KEYWORDS:

• Complex thin-walled parts
• batch machining process
• sliding time window
• clustering
• profile monitoring
• t squared statistics

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is financially supported in part by the National Natural Science Foundation of China (Grant No.:51805401), in part by the project supported by Natural Science Basic Research Plan in Shannxi Province of China (Program No.: 2019JQ-549), in part  by the Humanities and Social Sciences Youth Foundation of Ministry of Education of China (Grant No.:19YJC630221),  in part by the National Natural Science Foundation of China (Grant No.:51505356) and in part by the Free Exploration Fund No. JB190405.