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Articulated haulers are heavy construction machines used for earthmoving tasks. To optimize factors such as weight, cost, CO2 footprint, and productivity, maintaining the optimal chassis weight is critical. To support this, a digital twin approach is developed. In this study, two digital twin concepts for articulated haulers are evaluated and compared based on their prediction accuracy for component life. Both concepts leverage vehicle simulations, but one introduces segmented simulation data to increase flexibility and improve data utilization. Cross-validation is employed to assess the core idea of the digital twins. Results show that the best-performing concept achieves a mean cross-validation accuracy of 99%, demonstrating strong potential for future design optimization.

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Cross-Validation Comparison of Digital Twin Approaches Based on Simulated and Measured Road Roughness for Predicting Component Life in Articulated Haulers

  • 会议论文

  • 发表日期：

    2026年

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  • Joel Cramsky,
  • Manoranjan Kumar,
  • Ellen Rieloff,
  • Magnus Andersson,
  • Per-Olof Danielsson,
  • Welf Löwe

摘要

Articulated haulers are heavy construction machines used for earthmoving tasks. To optimize factors such as weight, cost, CO2 footprint, and productivity, maintaining the optimal chassis weight is critical. To support this, a digital twin approach is developed. In this study, two digital twin concepts for articulated haulers are evaluated and compared based on their prediction accuracy for component life. Both concepts leverage vehicle simulations, but one introduces segmented simulation data to increase flexibility and improve data utilization. Cross-validation is employed to assess the core idea of the digital twins. Results show that the best-performing concept achieves a mean cross-validation accuracy of 99%, demonstrating strong potential for future design optimization.