Scholarlink

<p>This research proposes an integrated optimization framework combining artificial neural networks (ANN) and Taguchi robust design for an eco-friendly deep eutectic solvent-based electrolyte. Five key process parameters—applied voltage, processing time, temperature, electrolyte composition (choline chloride to ethylene glycol ratio) and distilled water concentration—were systematically analyzed for their effects on the surface roughness of electropolished titanium. To address data scarcity and improve the generalization performance of the model, Gaussian noise with a mean of 0 and a standard deviation of 0.05 was applied to the input variables to augment the dataset. The multilayer perceptron-based ANN model effectively learned the nonlinear interactions between process parameters and achieved a high predictive accuracy with a coefficient of determination (R<sup>2</sup>) of 0.981. The optimal process conditions derived from the ANN model were 20&#xa0;V applied voltage, 21&#xa0;min of processing time, a 1:4 electrolyte ratio, 0% distilled water and 42℃, under which a minimum surface roughness of 4.162&#xa0;nm was experimentally confirmed. This investigation provides a practical pathway for optimizing processes in environmentally sustainable manufacturing by quantitatively analyzing the nonlinear complexity of electrochemical surface treatments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine learning based optimization of titanium electropolishing using artificial neural networks and Taguchi design in eco-friendly electrolytes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月5日

查看全文
  • Hyun-Kyu Hwang,
  • Seong-Jong Kim

摘要

This research proposes an integrated optimization framework combining artificial neural networks (ANN) and Taguchi robust design for an eco-friendly deep eutectic solvent-based electrolyte. Five key process parameters—applied voltage, processing time, temperature, electrolyte composition (choline chloride to ethylene glycol ratio) and distilled water concentration—were systematically analyzed for their effects on the surface roughness of electropolished titanium. To address data scarcity and improve the generalization performance of the model, Gaussian noise with a mean of 0 and a standard deviation of 0.05 was applied to the input variables to augment the dataset. The multilayer perceptron-based ANN model effectively learned the nonlinear interactions between process parameters and achieved a high predictive accuracy with a coefficient of determination (R2) of 0.981. The optimal process conditions derived from the ANN model were 20 V applied voltage, 21 min of processing time, a 1:4 electrolyte ratio, 0% distilled water and 42℃, under which a minimum surface roughness of 4.162 nm was experimentally confirmed. This investigation provides a practical pathway for optimizing processes in environmentally sustainable manufacturing by quantitatively analyzing the nonlinear complexity of electrochemical surface treatments.