XAI-driven digital twin for cobot dynamic error compensation

Abhilash Puthanveettil Madathil, Charlie Walker, Xichun Luo*, Qi Liu, Rajeshkumar Madarkar, Yi Qin

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

27 Downloads (Pure)

Abstract

Process and product fingerprints (FP) have been approved as effective parameters to reveal the principal contributing factors towards functionality in smart manufacturing processes. Though AI-driven methods outperform other approaches for fingerprint extraction, the lack of explainability in its black-box style predictions leads to misconceptions and trust issues among stakeholders. In this study, a novel explainable-AI (XAI) approach is proposed to identify mathematical fingerprint expressions by formulating them as graphs using the QLattice algorithm, inspired by path integral formulation. Here, the Qlattice model identifies explainable and human-comprehensible FP expressions for cobot dynamic error based on accelerometer signal features. The discovered symbolic model is subsequently applied to a digital twin which successfully tracked and compensated for dynamic errors autonomously in real time.
Original languageEnglish
Pages (from-to)176-181
Number of pages6
JournalProcedia CIRP
Volume126
DOIs
Publication statusPublished - 9 Oct 2024

Keywords

  • XAI-driven
  • digital twin
  • cobot
  • dynamic error compensation

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