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<p>This study investigates the machinability of a novel LM25 aluminum alloy reinforced with vanadium carbide composite material (LM25Al/VC) using computer numerical control (CNC) lathe operation. By optimizing CNC lathe process parameters such as depth of cut, feed rate, and cutting speed, the aim is to maximize material removal rate, minimize surface roughness, reduce power consumption, and optimize costs. The study employs analytical modeling, deep neural networks (DNN), and grey relational grade (GRA) coupled with response surface methodology (RSM) for performance evaluation. The effectiveness of these methods was compared using four objective verification mechanisms. In this case, the DNN technique delivered superior results among the methods considered. Additionally, new analytical models and DNN programming were developed in this work to predict machining costs, power consumption, material removal rate, and surface finish quality. These findings contribute to creating energy-efficient, cost-effective machining techniques and promote sustainable practices in the manufacturing industry.</p>

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Optimizing economics of machining for LM25Al/VC composite material using analytical modeling, deep neural network and GRA coupled with RSM

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月25日

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  • Mesay Alemu Tolcha,
  • Hirpa Gelgele Lemu,
  • Yosef Wakjira Adugna

摘要

This study investigates the machinability of a novel LM25 aluminum alloy reinforced with vanadium carbide composite material (LM25Al/VC) using computer numerical control (CNC) lathe operation. By optimizing CNC lathe process parameters such as depth of cut, feed rate, and cutting speed, the aim is to maximize material removal rate, minimize surface roughness, reduce power consumption, and optimize costs. The study employs analytical modeling, deep neural networks (DNN), and grey relational grade (GRA) coupled with response surface methodology (RSM) for performance evaluation. The effectiveness of these methods was compared using four objective verification mechanisms. In this case, the DNN technique delivered superior results among the methods considered. Additionally, new analytical models and DNN programming were developed in this work to predict machining costs, power consumption, material removal rate, and surface finish quality. These findings contribute to creating energy-efficient, cost-effective machining techniques and promote sustainable practices in the manufacturing industry.