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<p>The continuous rise in global manufacturing demand has significantly increased electricity consumption, with Computer Numerical Control (CNC) machine tools identified as major energy consumers—yet often operating at efficiencies below 30%. Although numerous studies have investigated energy consumption in machining, clear definitions of energy sources and a comprehensive examination of the influence of mechanical properties on power consumption remain insufficient. To address these, this work analyzed the power behavior of spindle and feed axis motors and evaluated the impact of steel properties on cutting power, aiming to develop a generalized cutting power model. In particular, the predictive accuracy of such models varies significantly across different steel types, underscoring the importance of robustness and generalizability in evaluating model performance. This study systematically compares the correlations between cutting power and four material properties—yield strength, tensile strength, elongation, and hardness. Cutting power models based on each property were constructed using five types of steel: P1, SKD61, P20, P3, and NAK80. The proposed model was first validated using S50C and subsequently tested on 420J2 stainless steel, achieving a prediction accuracy of 97.6%. In addition, the model was evaluated under a variable cutting path designed to simulate realistic machining conditions, yielding an accuracy of 97.2%. These results collectively highlight the model’s strong generalizability across different steel types and cutting scenarios.</p>

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Comparative analysis of steel mechanical properties for generalized cutting energy prediction in CNC machining

  • 期刊论文

  • 发表日期：

    2025年9月19日

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  • Pang-Hsiang Hung,
  • Po-Han Chen,
  • Shang-Yu Lin,
  • Wen-Tse Hsu,
  • Bing-He Chou,
  • Pei-Zen Chang,
  • Wei-Chang Li

摘要

The continuous rise in global manufacturing demand has significantly increased electricity consumption, with Computer Numerical Control (CNC) machine tools identified as major energy consumers—yet often operating at efficiencies below 30%. Although numerous studies have investigated energy consumption in machining, clear definitions of energy sources and a comprehensive examination of the influence of mechanical properties on power consumption remain insufficient. To address these, this work analyzed the power behavior of spindle and feed axis motors and evaluated the impact of steel properties on cutting power, aiming to develop a generalized cutting power model. In particular, the predictive accuracy of such models varies significantly across different steel types, underscoring the importance of robustness and generalizability in evaluating model performance. This study systematically compares the correlations between cutting power and four material properties—yield strength, tensile strength, elongation, and hardness. Cutting power models based on each property were constructed using five types of steel: P1, SKD61, P20, P3, and NAK80. The proposed model was first validated using S50C and subsequently tested on 420J2 stainless steel, achieving a prediction accuracy of 97.6%. In addition, the model was evaluated under a variable cutting path designed to simulate realistic machining conditions, yielding an accuracy of 97.2%. These results collectively highlight the model’s strong generalizability across different steel types and cutting scenarios.