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<p>Achieving ultra-high precision in CNC machining requires robust geometric error allocation. Traditional methods, limited by localized optimization and unidirectional tolerance assumptions, frequently fail at workspace boundaries. This paper presents a novel systematic method that introduces a bidirectional error model based on multi-body system theory. Leveraging global sensitivity analysis, the method employs genetic algorithm optimization, constrained by worst-case volumetric errors evaluated across 27,000 workspace points, to guarantee full-workspace accuracy. Simulations verify that all errors are contained within ± 0.01&#xa0;mm. Compared to the traditional single-point approach, the proposed method reduces the non-conformance rate significantly (from &gt; 60.89% to 0%) while strictly maintaining the accuracy target, significantly improving assembly precision and manufacturability.</p>

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A New Method of Priority Analysis and Optimization for Accuracy Allocation of Machining Center Considering Error Polarity

  • 期刊论文

  • 发表日期：

    2026年3月30日

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  • Yongzhen Zang,
  • Guangyu Li,
  • Xiangsheng Gao,
  • Hanjun Gao,
  • Shengxi Wang

摘要

Achieving ultra-high precision in CNC machining requires robust geometric error allocation. Traditional methods, limited by localized optimization and unidirectional tolerance assumptions, frequently fail at workspace boundaries. This paper presents a novel systematic method that introduces a bidirectional error model based on multi-body system theory. Leveraging global sensitivity analysis, the method employs genetic algorithm optimization, constrained by worst-case volumetric errors evaluated across 27,000 workspace points, to guarantee full-workspace accuracy. Simulations verify that all errors are contained within ± 0.01 mm. Compared to the traditional single-point approach, the proposed method reduces the non-conformance rate significantly (from > 60.89% to 0%) while strictly maintaining the accuracy target, significantly improving assembly precision and manufacturability.