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<p>Electrochemical discharge machining (ECDM), which combines advantages of both Electrical discharge machining (EDM) and electrochemical machining (ECM), is effective for processing difficult-to-cut materials. The machining gap is critical for directing energy to the workpiece, yet conventional control strategies like constant feed rate lack adaptive adjustment, limiting ECDM performance. Thus, a new gap-characterizing signal is needed for closed-loop control. To address this, an intelligent control system integrating Long Short-Term Memory (LSTM) networks and fuzzy control is proposed. It adaptively adjusts the feed rate based on desired spark discharge ratio. By establishing an LSTM-based spark ratio prediction model, multi-step ahead forecasting is achieved, effectively preventing arc discharges and short circuits that may damage the workpiece surface. In the low, medium, and high spark ratio stages, the prediction accuracies reached 92.69%, 88.00%, and 98.54%, respectively. The stability and reliability of the model were further validated through multiple evaluation metrics, including Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The results demonstrate that, under the condition of meeting the highest surface quality requirements, the proposed intelligent control system increases the material removal rate of ECDM by 77% compared with conventional constant feed rate control, while achieving a surface roughness of 0.19&#xa0;μm. In comparison with high feed rate machining, electrode wear is reduced by 50%, and the short-circuit frequency decreases from 95 times per hour to zero. This approach achieves a comprehensive improvement in discharge stability, machining efficiency, electrode wear, and surface roughness.</p>

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An adaptive intelligent control system for electrochemical discharge machining based on discharge ratio prediction

  • 期刊论文

  • 发表日期：

    2026年1月3日

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  • Zhiming Wang,
  • Hang Dong,
  • Ruixiang Li,
  • Qingsong Zhang,
  • Yihao Wang,
  • Jianping Zhou

摘要

Electrochemical discharge machining (ECDM), which combines advantages of both Electrical discharge machining (EDM) and electrochemical machining (ECM), is effective for processing difficult-to-cut materials. The machining gap is critical for directing energy to the workpiece, yet conventional control strategies like constant feed rate lack adaptive adjustment, limiting ECDM performance. Thus, a new gap-characterizing signal is needed for closed-loop control. To address this, an intelligent control system integrating Long Short-Term Memory (LSTM) networks and fuzzy control is proposed. It adaptively adjusts the feed rate based on desired spark discharge ratio. By establishing an LSTM-based spark ratio prediction model, multi-step ahead forecasting is achieved, effectively preventing arc discharges and short circuits that may damage the workpiece surface. In the low, medium, and high spark ratio stages, the prediction accuracies reached 92.69%, 88.00%, and 98.54%, respectively. The stability and reliability of the model were further validated through multiple evaluation metrics, including Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The results demonstrate that, under the condition of meeting the highest surface quality requirements, the proposed intelligent control system increases the material removal rate of ECDM by 77% compared with conventional constant feed rate control, while achieving a surface roughness of 0.19 μm. In comparison with high feed rate machining, electrode wear is reduced by 50%, and the short-circuit frequency decreases from 95 times per hour to zero. This approach achieves a comprehensive improvement in discharge stability, machining efficiency, electrode wear, and surface roughness.