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<p>Positioning accuracy constitutes a critical metric for evaluating the operational performance of Multi-axis Flexible Machining Centers (MFMC). Currently, pinpointing the origin of positioning errors under micro-stroke conditions poses significant challenges, with the mechanistic interplay among error components remaining poorly understood. To address this limitation, this paper introduces a deep error tracing methodology grounded in error distribution analysis and pattern recognition. This approach enables precise identification of key components influencing workpiece geometric errors in micro-stroke operations, thereby offering foundational support for subsequent precision optimization, error prediction, and compensation strategies. First, an analysis of geometric error propagation from mechanical components to workpieces is conducted, revealing spatial distribution patterns of critical component error terms. Second, leveraging error flow theory, a mathematical model is established to characterize these errors, allowing the derivation of positioning accuracy distribution through algorithmic mapping that bridges component-level errors with workpiece-level deviations. Finally, by applying probability density function operations, the composite workpiece error distribution undergoes decoupling to yield distinct normal distribution curves. These characteristic curves are subsequently cross-referenced with fitted component error distributions, enabling systematic identification of dominant error sources. In addition, through a series of measurement experiments, the interaction relationships among the error sources were clarified.</p>

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A Novel Deep Error Tracing Approach Based on Stream-of-Variation Theory for Multi-Axis Flexible Machining Center at Micro-Stroke

  • 期刊论文

  • 发表日期：

    2025年11月6日

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  • Zhenfeng Li,
  • Hao Tang,
  • Yu Zheng

摘要

Positioning accuracy constitutes a critical metric for evaluating the operational performance of Multi-axis Flexible Machining Centers (MFMC). Currently, pinpointing the origin of positioning errors under micro-stroke conditions poses significant challenges, with the mechanistic interplay among error components remaining poorly understood. To address this limitation, this paper introduces a deep error tracing methodology grounded in error distribution analysis and pattern recognition. This approach enables precise identification of key components influencing workpiece geometric errors in micro-stroke operations, thereby offering foundational support for subsequent precision optimization, error prediction, and compensation strategies. First, an analysis of geometric error propagation from mechanical components to workpieces is conducted, revealing spatial distribution patterns of critical component error terms. Second, leveraging error flow theory, a mathematical model is established to characterize these errors, allowing the derivation of positioning accuracy distribution through algorithmic mapping that bridges component-level errors with workpiece-level deviations. Finally, by applying probability density function operations, the composite workpiece error distribution undergoes decoupling to yield distinct normal distribution curves. These characteristic curves are subsequently cross-referenced with fitted component error distributions, enabling systematic identification of dominant error sources. In addition, through a series of measurement experiments, the interaction relationships among the error sources were clarified.