Louhichi, Borhen, Slamani, Mohamed, Bonev, Ilian et Stepanenko, Oleksandr.
2026.
« Data-driven modeling of industrial robot repeatability using ensemble artificial neural networks under varying operational conditions ».
Machines, vol. 14, nº 8.
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Résumé
The positional repeatability of industrial robots is a critical yet state-dependent performance metric, highly sensitive to thermal conditioning and mechanical loading. This study develops a data-driven framework for predicting repeatability of FANUC LR Mate 200iD (FANUC, Oshino-mura, Japan) and KUKA KR 6 R700 Sixx (KUKA AG, Augsburg, Germany) robots under varying operational conditions. ISO 9283-compliant experiments using a TriCal system (TRI-CAL Ltd., Montreal, QC, Canada) were conducted across three warm-up durations, three payload levels, and five poses. Ensemble artificial neural networks with 10 independently trained networks were developed for each robot. The FANUC model achieved R2 = 0.9922, RMSE = 0.004231 mm, and MAE = 0.002979 mm, while the KUKA model achieved R2 = 0.9926, RMSE = 0.002919 mm, and MAE = 0.002215 mm. Prediction interval coverage was 93.3% for FANUC and 100% for KUKA. Per-pose R2 ranged from 0.9588 to 0.9966 for KUKA. Response surface analysis identified thermal stabilization as the dominant factor affecting repeatability, with improvements of 86% for FANUC and 84% for KUKA after 4 h of warm-up. The KUKA robot demonstrated superior robustness and lower variability compared to the FANUC robot. The framework provides a practical tool for predicting repeatability, supporting process planning, uncertainty budgeting, and precision manufacturing optimization.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Chercheur(-euse): | Chercheur(-euse) Bonev, Ilian |
| Affiliation: | Génie des systèmes |
| Date de dépôt: | 04 sept. 2026 20:47 |
| Dernière modification: | 27 sept. 2026 18:08 |
| URI: | https://espace2.etsmtl.ca/id/eprint/34284 |
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