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<p>Regardless of the materials’ intrinsic characteristics, electrochemical discharge drilling (ECDD) effectively micro-machines various materials. The present article optimizes the ultrasonic assisted rotary ECDD (UR-ECDD) process for maximizing the material removal rate (MRR), while minimizing the hole overcut (HOC) and circularity error (CE). The micro-holes are produced using a Taguchi’s L16 array and multi-criteria optimization is carried out using grey relational based analysis (GRA). MRR, HOC and CE serve as a response parameter while tool vibration, tool feed rate, working material rotation, applied voltage and electrolyte concentration are control variables. UR-ECDD results in the improvement of 14.8% in MRR, 15.4% in HOC and 17.2% in CE when compared to the ECDD process. The optimized control variables based on GRA are derived as A4C3B4D1E4 (6&#xa0;µm, 80&#xa0;rpm, 0.9&#xa0;mm/min, 35&#xa0;V, 25&#xa0;wt%). Tool vibration emerged as the most significant control variable. The GRG’s predicted results at optimum conditions provide a satisfactory alignment with the experimental results. Machine learning-based algorithms are also used to predict the responses using Random Forest and Gradient Boost approaches. Comparative results indicated that the Random Forest predicts the responses with reduced error in comparison to the Gradient Boost method. The validation of the dataset exhibits a similar trend confirming the efficacy of prediction.</p>

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Micro hole drilling and multi criteria optimization of soda lime glass via ultrasonic assisted rotary electrochemical discharge drilling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月9日

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  • Sahil Grover,
  • Viveksheel Rajput,
  • Sanjay Kumar Mangal,
  • Sarbjit Singh,
  • Sandeep Singh,
  • Shubham Sharma,
  • Ehab El Sayed Massoud,
  • Dražan Kozak,
  • Jasmina Lozanovic

摘要

Regardless of the materials’ intrinsic characteristics, electrochemical discharge drilling (ECDD) effectively micro-machines various materials. The present article optimizes the ultrasonic assisted rotary ECDD (UR-ECDD) process for maximizing the material removal rate (MRR), while minimizing the hole overcut (HOC) and circularity error (CE). The micro-holes are produced using a Taguchi’s L16 array and multi-criteria optimization is carried out using grey relational based analysis (GRA). MRR, HOC and CE serve as a response parameter while tool vibration, tool feed rate, working material rotation, applied voltage and electrolyte concentration are control variables. UR-ECDD results in the improvement of 14.8% in MRR, 15.4% in HOC and 17.2% in CE when compared to the ECDD process. The optimized control variables based on GRA are derived as A4C3B4D1E4 (6 µm, 80 rpm, 0.9 mm/min, 35 V, 25 wt%). Tool vibration emerged as the most significant control variable. The GRG’s predicted results at optimum conditions provide a satisfactory alignment with the experimental results. Machine learning-based algorithms are also used to predict the responses using Random Forest and Gradient Boost approaches. Comparative results indicated that the Random Forest predicts the responses with reduced error in comparison to the Gradient Boost method. The validation of the dataset exhibits a similar trend confirming the efficacy of prediction.