XAI-driven digital twin for cobot dynamic error compensation

Puthanveettil Madathil, Abhilash and Walker, Charlie and Luo, Xichun and Liu, Qi and Madarkar, Rajeshkumar and Qin, Yi (2024) XAI-driven digital twin for cobot dynamic error compensation. Procedia CIRP, 126. pp. 176-181. ISSN 2212-8271 (https://doi.org/10.1016/j.procir.2024.08.320)

[thumbnail of Madathil-etal-PCIRP-2024-XAI-driven-digital-twin-for-cobot-dynamic-error-compensation]
Preview
 Text. Filename: Madathil-etal-PCIRP-2024-XAI-driven-digital-twin-for-cobot-dynamic-error-compensation.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (842kB)| Preview

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.

ORCID iDs

Puthanveettil Madathil, Abhilash ORCID logoORCID: https://orcid.org/0000-0001-5655-6196, Walker, Charlie, Luo, Xichun ORCID logoORCID: https://orcid.org/0000-0002-5024-7058, Liu, Qi ORCID logoORCID: https://orcid.org/0000-0002-1960-7318, Madarkar, Rajeshkumar and Qin, Yi ORCID logoORCID: https://orcid.org/0000-0001-7103-4855;
CORE (COnnecting REpositories)