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This comprehensive study centers on optimizing the finishing milling of Aluminum Alloy A7075 material by employing the Coral Reefs Optimization (CRO) algorithm. The main aim is to minimize both surface roughness value (Ra) and cutting force value (Fc). To accomplish this, the research cutting parameters include cutting speed (Vc), feed rate (F), and depth of cut (ap) within stringent machine and cutting tool constraints. The CRO algorithm, renowned for its effectiveness in complex problem-solving, has been meticulously applied to navigate the trade-offs between these objectives. A set of Pareto-optimal solutions was obtained, with a standout combination featuring a Vc of 335.7291 m/min, an F of 1200 mm/min, and an ap of 1.3455 mm. This resulted in an Fc of 162.93 N and a Ra of 0.313 µm, signifying an optimal balance between efficiency and quality. These results underscore the potential of bio-inspired algorithms like CRO in optimizing industrial machining processes, offering valuable insights for the advancement of precision manufacturing within the constraints of practical applications. The findings pave the way for improved operational performance in the milling of high-strength aluminum alloys.

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Sustainable Dry Machining of Aluminum Alloy A7075: Utilizing Coral Reefs Optimization and Heatmap Analysis for Impact Assessment and Optimization of Cutting Parameters

  • 会议论文

  • 发表日期：

    2025年

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  • Nhu-Trang Le,
  • Van-Canh Nguyen,
  • Duong Van Duc,
  • Hoang Tien Dat,
  • Van-Tam Ngo

摘要

This comprehensive study centers on optimizing the finishing milling of Aluminum Alloy A7075 material by employing the Coral Reefs Optimization (CRO) algorithm. The main aim is to minimize both surface roughness value (Ra) and cutting force value (Fc). To accomplish this, the research cutting parameters include cutting speed (Vc), feed rate (F), and depth of cut (ap) within stringent machine and cutting tool constraints. The CRO algorithm, renowned for its effectiveness in complex problem-solving, has been meticulously applied to navigate the trade-offs between these objectives. A set of Pareto-optimal solutions was obtained, with a standout combination featuring a Vc of 335.7291 m/min, an F of 1200 mm/min, and an ap of 1.3455 mm. This resulted in an Fc of 162.93 N and a Ra of 0.313 µm, signifying an optimal balance between efficiency and quality. These results underscore the potential of bio-inspired algorithms like CRO in optimizing industrial machining processes, offering valuable insights for the advancement of precision manufacturing within the constraints of practical applications. The findings pave the way for improved operational performance in the milling of high-strength aluminum alloys.