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<p>Machining of difficult-to-cut materials such as high-temperature metals is challenging due to their low machinability resulting in reduced productivity and high manufacturing cost. This investigation develops and optimizes the hard self-propelled rotary turning (HSPRT) operation, in which an efficient self-propelled rotary tool is proposed and fabricated. The HSPRT responses (total carbon emission—<i>TC</i>, machined roughness—<i>MR</i>, and noise emission—<i>EN</i>) are minimized using optimal process variables (inclination angle—<i>A</i>, turning depth—<i>D</i>, turning speed—<i>V</i>, and rake angle—<i>R</i>). The <i>TC</i>, <i>MR</i>, and <i>EN</i> models are developed in terms of the HSPRT inputs using the radial basis function network and response surface method, while the weights were computed using the removal effects of criteria, EQUAL, and rank order centroid methods. The improved quantum-behaved particle swarm optimization algorithm and method based on the multi-attributive border approximation area comparison were applied to produce feasible solutions and select the best optimal point. The optimization findings of the <i>V</i>, <i>D</i>, <i>f</i>, and <i>R</i> were 32&#xa0;deg., 0.2&#xa0;mm, 137&#xa0;m/min, and 20&#xa0;deg., while the <i>TC</i>, <i>MR</i>, and <i>EN</i> were saved by 42.8%, 24.1%, and 20.0%, respectively. The HSPRT performances were primarily affected by the turning depth and speed, respectively. The valuable outcomes could be applied to the practice to boost HSPRT performances, while the developed HSPRT operation could be utilized for machining alloys and hardened steels. The technique could be applied to treat optimization problems for other rotary turning processes.</p>

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Radial basis function network-based optimization of the hard self-propelled rotary turning titanium

  • 期刊论文

  • 发表日期：

    2025年1月2日

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  • Trung-Thanh Nguyen,
  • Xuan-Ba Dang

摘要

Machining of difficult-to-cut materials such as high-temperature metals is challenging due to their low machinability resulting in reduced productivity and high manufacturing cost. This investigation develops and optimizes the hard self-propelled rotary turning (HSPRT) operation, in which an efficient self-propelled rotary tool is proposed and fabricated. The HSPRT responses (total carbon emission—TC, machined roughness—MR, and noise emission—EN) are minimized using optimal process variables (inclination angle—A, turning depth—D, turning speed—V, and rake angle—R). The TC, MR, and EN models are developed in terms of the HSPRT inputs using the radial basis function network and response surface method, while the weights were computed using the removal effects of criteria, EQUAL, and rank order centroid methods. The improved quantum-behaved particle swarm optimization algorithm and method based on the multi-attributive border approximation area comparison were applied to produce feasible solutions and select the best optimal point. The optimization findings of the V, D, f, and R were 32 deg., 0.2 mm, 137 m/min, and 20 deg., while the TC, MR, and EN were saved by 42.8%, 24.1%, and 20.0%, respectively. The HSPRT performances were primarily affected by the turning depth and speed, respectively. The valuable outcomes could be applied to the practice to boost HSPRT performances, while the developed HSPRT operation could be utilized for machining alloys and hardened steels. The technique could be applied to treat optimization problems for other rotary turning processes.