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<p>Electrochemical micro-machining (ECMM) enables high-precision fabrication of micro-features in difficult-to-machine materials; however, its strongly nonlinear, multi-physics nature and the high cost of experimentation severely limit reliable data-driven modeling. This study presents a physics-guided machine learning framework for robust multi-response prediction of ECMM performance using polymer graphite electrodes. Controlled experiments were conducted with non-treated and cryogenically treated electrodes, and four critical responses were evaluated: material removal rate (MRR), overcut (Oc), surface roughness (Ra), and taper angle (Ta). Physics-guided descriptors incorporating interaction-driven and severity-based features were constructed to embed mechanistic structure associated with electrochemical excitation and tool-electrolyte-workpiece coupling. Ensemble learning models were trained and rigorously validated using repeated cross-validation. The optimal physics-guided XGBoost models achieved coefficients of determination of 0.817 (MRR), 0.914 (Oc), 0.866 (Ra), and 0.769 (Ta), with corresponding mean absolute errors of 0.013&#xa0;g/min, 0.026&#xa0;mm, 0.261&#xa0;μm, and 0.049°, respectively, consistently outperforming polynomial and purely data-driven baselines. Ablation analysis confirmed that physics-guided feature integration improved both predictive accuracy and cross-validated stability under limited experimental data conditions. Parity and residual diagnostics demonstrated strong generalization without systematic bias. The results establish physics-guided learning as an effective strategy for bridging electrochemical process physics and data-driven modeling, enabling accurate, robust, and interpretable prediction of ECMM responses. The proposed framework provides a scalable foundation for intelligent micro-manufacturing, with potential applications in adaptive process optimization and digital manufacturing workflows.<!--Query ID="Q1" Text="Please check and confirm the author names and initials are correct. Also, kindly confirm the details in the metadata are correct."--><!--Query ID="Q2" Text="Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary."--></p>

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Physics-guided explainable machine learning for multi-response modeling of electrochemical micro-machining using polymer graphite electrodes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月1日

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  • B. Veera Siva Reddy,
  • N. Pradeep,
  • Akula Siva Bhaskar,
  • C. Chandrasekhara Sastry,
  • Sachin Salunkhe,
  • Robert Cep

摘要

Electrochemical micro-machining (ECMM) enables high-precision fabrication of micro-features in difficult-to-machine materials; however, its strongly nonlinear, multi-physics nature and the high cost of experimentation severely limit reliable data-driven modeling. This study presents a physics-guided machine learning framework for robust multi-response prediction of ECMM performance using polymer graphite electrodes. Controlled experiments were conducted with non-treated and cryogenically treated electrodes, and four critical responses were evaluated: material removal rate (MRR), overcut (Oc), surface roughness (Ra), and taper angle (Ta). Physics-guided descriptors incorporating interaction-driven and severity-based features were constructed to embed mechanistic structure associated with electrochemical excitation and tool-electrolyte-workpiece coupling. Ensemble learning models were trained and rigorously validated using repeated cross-validation. The optimal physics-guided XGBoost models achieved coefficients of determination of 0.817 (MRR), 0.914 (Oc), 0.866 (Ra), and 0.769 (Ta), with corresponding mean absolute errors of 0.013 g/min, 0.026 mm, 0.261 μm, and 0.049°, respectively, consistently outperforming polynomial and purely data-driven baselines. Ablation analysis confirmed that physics-guided feature integration improved both predictive accuracy and cross-validated stability under limited experimental data conditions. Parity and residual diagnostics demonstrated strong generalization without systematic bias. The results establish physics-guided learning as an effective strategy for bridging electrochemical process physics and data-driven modeling, enabling accurate, robust, and interpretable prediction of ECMM responses. The proposed framework provides a scalable foundation for intelligent micro-manufacturing, with potential applications in adaptive process optimization and digital manufacturing workflows.