Gaussian process-based surrogate framework for efficient prediction of geometrical inaccuracy in Wire Electrical Discharge Machining of thin-wall miniature components

IF 5.4 2区 工程技术 Q2 ENGINEERING, MANUFACTURING
Aswin P., Rakesh G. Mote
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引用次数: 0

Abstract

High aspect ratio, thin-walled miniature structures are critical in applications such as microfluidics and micromechanical cooling. Wire Electrical Discharge Machining (Wire EDM) presents a commercially viable alternative to specialized micromachining setups for fabricating such features. However, as part size decreases, conventional Wire EDM faces challenges in achieving accurate profiles due to intensified thermal effects and reduced part stiffness, leading to increased geometrical errors. To address this, a reduced-order surrogate framework based on Gaussian Process Regression (GPR) is developed to predict key geometrical deviations specifically, reduced wall thickness and wall deformation as functions of process parameters. The framework integrates four GPR models trained on hybrid datasets combining experimental data and physics-based numerical results. A discrepancy model further refines numerical predictions by accounting for deviations from experimental data. The final GPR models achieve mean absolute errors of 3.39 μm and 6.08 μm for wall thickness and deformation, with R2 values of 0.96 and 0.99. K-fold cross-validation and validation experiments confirm model reliability, with prediction errors around 14.3 μm and 12.1 μm. The discrepancy model reduces the deviation of numerical predictions from actual values by 55%. Process parameter optimization is performed to fabricate thin walls with targeted deformation levels, achieving reasonable accuracy within 22.3 μm. Furthermore, sensitivity analysis is conducted to quantify both individual and interactive influences of major process parameters on geometrical errors.
基于高斯过程的薄壁微细零件线切割加工几何误差预测替代框架
高宽高比、薄壁微型结构在微流体和微机械冷却等应用中至关重要。线材电火花加工(线材EDM)提供了一种商业上可行的替代专门的微加工装置来制造这些特征。然而,随着零件尺寸的减小,由于热效应加剧和零件刚度降低,传统的线切割在实现精确轮廓方面面临挑战,从而导致几何误差增加。为了解决这一问题,开发了基于高斯过程回归(GPR)的降阶代理框架,以预测关键几何偏差,减少壁厚和壁变形作为工艺参数的函数。该框架集成了四种基于混合数据集训练的探地雷达模型,这些混合数据集结合了实验数据和基于物理的数值结果。差异模型通过考虑与实验数据的偏差进一步改进数值预测。最终GPR模型对壁厚和变形的平均绝对误差分别为3.39 μm和6.08 μm, R2分别为0.96和0.99。K-fold交叉验证和验证实验验证了模型的可靠性,预测误差分别为14.3 μm和12.1 μm。差异模型将数值预测与实际值的偏差减少了55%。通过优化工艺参数,实现了薄壁的目标变形水平,实现了22.3 μm以内的合理精度。此外,还进行了灵敏度分析,以量化主要工艺参数对几何误差的单独和相互影响。
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来源期刊
CIRP Journal of Manufacturing Science and Technology
CIRP Journal of Manufacturing Science and Technology Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
6.20%
发文量
166
审稿时长
63 days
期刊介绍: The CIRP Journal of Manufacturing Science and Technology (CIRP-JMST) publishes fundamental papers on manufacturing processes, production equipment and automation, product design, manufacturing systems and production organisations up to the level of the production networks, including all the related technical, human and economic factors. Preference is given to contributions describing research results whose feasibility has been demonstrated either in a laboratory or in the industrial praxis. Case studies and review papers on specific issues in manufacturing science and technology are equally encouraged.
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