Tool Wear Characterization and Monitoring with Hierarchical Spatio-Temporal Models for Micro-Friction Stir Welding

Zhe Gao, Mengfei Chen, Weihong Grace Guo, Jingjing Li

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Tool condition in manufacturing plays a significant role on process dynamics and part quality. Effective modeling and monitoring of tool condition deterioration can provide the technical basis for maintaining production efficiency and quality. Inspired by the need of tool condition monitoring in joining dissimilar materials, especially the micro friction stir welding (μFSW) process, this paper aims to model and monitor the spatial and temporal patterns in the dynamic tool wear propagation in μFSW. A hybrid hierarchical spatio-temporal model is developed for the time-ordered, high-dimensional tool surface measurement images to characterize the dynamic tool wear propagation in μFSW. The model is developed in a hierarchical Bayesian structure with the first level being a data-driven regression model for the high-resolution tool pin profile images and the second level being a physics-based advection-diffusion model for the welding temperature distribution. Kalman filter is adopted to estimate the posterior distributions of the state variable (temperature distribution) and the error between the measured tool surface image and the predicted images. Regularized Mahalanobis distance is proposed to monitor tool wear progression. Numerical studies on three abnormal tool wear progression patterns demonstrate the effectiveness of the proposed spatio-temporal modeling method, as well as the timeliness, confidence, and power of detection. Results show that abnormal tool wear progression can be detected within 2 time-steps, the detecting accuracy of abnormal tool wear progressions can reach 99%, and the Type II error rates remain at 0. The method developed in this paper is expected to facilitate early detection of abnormal tool wear progressions, reduce the efforts in manual inspection, and support smart manufacturing. The proposed methods can be easily extended to other manufacturing processes with online sensing and tool measurement capabilities.

Original languageEnglish (US)
Pages (from-to)1353-1365
Number of pages13
JournalJournal of Manufacturing Processes
Volume56
DOIs
StatePublished - Aug 2020

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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