Accurate error compensation method for multi-axis parallel machine via singularized jacobi geometric parameter correction and coupling error evaluation

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Robotics and Computer-integrated Manufacturing Pub Date : 2024-04-03 DOI:10.1016/j.rcim.2024.102771
Yuheng Luo , Jian Gao , Disai Chen , Lanyu Zhang , Yachao Liu , Yongbin Zhong
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Abstract

The Jacobian model is a prevalent tool for error compensation in multi-axis parallel mechanisms. However, discrepancies between the model's nominal and actual geometrical parameters, combined with equivalent replacements and high-order rounding in the modeling process, lead to equation solving challenges and modeling errors. These inaccuracies result in residual errors in the Jacobian model compensation. To address these problems, this paper proposes an optimal Jacobian correction approach. This is based on a geometrical parameter singularized Jacobian correction model, and a module for the evaluation of coupling errors for multi-axis parallel mechanisms was incorporated. Instead of relying on iterative processes, a singularized geometrical error solution method (SESM) was developed. Through this method, precise derivation of the Jacobian correction parameters is ensured, effectively addressing the indefinite equation challenge and partial posture non-solution problem. Moreover, modeling errors resulting from equivalent infinitesimal replacements and the overlooking of high-order minor values are compensated for by the SESM. It was observed that varying singularized geometrical parameters in the Jacobian model can produce different coupling effects and compensation outcomes. Therefore, a sensitivity-based error predictive evaluation method (EPEM) was introduced. By this method, the optimal correction parameter of the Jacobian model across the entire workspace is identified, ensuring precise pose error compensation. The proposed method was validated using a three-axis parallel mechanism. Through these tests, its superior efficacy was revealed. In comparison to the traditional uncorrected Jacobian compensation, reductions in position and orientation errors by 64.93% and 55.29%, respectively, were achieved. This method provides a new approach for error modeling, equation solving, and parameter correction for multi-axis mechanism error compensation and precision equipment development.

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通过奇异化 jacobi 几何参数校正和耦合误差评估实现多轴并联机床的精确误差补偿方法
雅各布模型是多轴并联机构误差补偿的常用工具。然而,模型标称参数与实际几何参数之间的差异,加上建模过程中的等效替换和高阶四舍五入,导致方程求解难题和建模误差。这些误差会导致雅各布模型补偿的残余误差。为解决这些问题,本文提出了一种最佳雅各布修正方法。该方法以几何参数奇异化雅各布修正模型为基础,并加入了多轴并联机构耦合误差评估模块。该方法不依赖于迭代过程,而是开发了一种奇异化几何误差求解方法(SESM)。通过这种方法,确保了雅各布修正参数的精确推导,有效地解决了不定方程难题和部分姿态非求解问题。此外,SESM 还能补偿等效无穷小替换和忽略高阶次要值造成的建模误差。据观察,雅各布模型中不同的奇异化几何参数会产生不同的耦合效应和补偿结果。因此,引入了基于灵敏度的误差预测评估方法(EPEM)。通过这种方法,可以确定整个工作空间中雅各布模型的最佳修正参数,从而确保精确的姿态误差补偿。使用三轴平行机构对所提出的方法进行了验证。通过这些测试,显示了其卓越的功效。与传统的未修正雅各布补偿相比,位置和方向误差分别减少了 64.93% 和 55.29%。该方法为多轴机构误差补偿和精密设备开发的误差建模、方程求解和参数修正提供了一种新方法。
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来源期刊
Robotics and Computer-integrated Manufacturing
Robotics and Computer-integrated Manufacturing 工程技术-工程:制造
CiteScore
24.10
自引率
13.50%
发文量
160
审稿时长
50 days
期刊介绍: The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.
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