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<p>Processing difficult-to-cut materials with optimal surface integrity is essential for advanced manufacturing. Tool vibration degrades 12X18N10T stainless steel machining. The best cutting settings for this tough material require measuring vibrations. Traditional wired vibration-monitoring systems often lose signal quality due to the distance between sensors and the cutting zone. This project collects high-quality, real-time data from a Bluetooth-enabled wireless vibration-monitoring device in the tool holder. A study used a <i>2</i><sup><i>3</i></sup> factorial design and ANOVA to analyze the impact of cutting speed <i>(v)</i>, feed per tooth <i>(S)</i>, and depth of cut <i>(t)</i> on surface roughness <i>(Ra)</i> and vibration amplitude <i>(A)</i>. The model showed high reliability (<i>R</i><sup><i>2</i></sup> ≥ 90%), with cutting speed <i>(v)</i> significantly affecting vibration amplitude <i>(A)</i> and feed per tooth <i>(S)</i> significantly affecting surface roughness <i>(Ra)</i>. The optimal settings for Derringer’s desirability multi-objective optimization method were <i>(v</i> = 45&#xa0;mm/min<i>)</i>, <i>(S</i> = 0.02&#xa0;mm/tooth<i>)</i>, and <i>(t</i> = 0.2&#xa0;mm<i>)</i>, resulting in an approximate surface roughness <i>(Ra)</i> of 3.07&#xa0;µm and a vibration amplitude <i>(A)</i> of 0.01196&#xa0;mm. The study also establishes <i>“feasible machining zones”</i> with (<i>Ra</i> ≤ 0.4&#xa0;µm and <i>A</i> ≤ 0.04&#xa0;mm) for intelligent, adaptive control in Industry 4.0 compatible milling systems.</p>

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Data-Driven Modeling and Optimization of Surface Roughness and Vibration in End Milling Using a Wireless Sensor Tool Holder

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月9日

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  • Trong Dang Huu,
  • Hiep Nguyen Hoang,
  • Ngoc Dang Khoa Tran,
  • Nguyen Van Toan

摘要

Processing difficult-to-cut materials with optimal surface integrity is essential for advanced manufacturing. Tool vibration degrades 12X18N10T stainless steel machining. The best cutting settings for this tough material require measuring vibrations. Traditional wired vibration-monitoring systems often lose signal quality due to the distance between sensors and the cutting zone. This project collects high-quality, real-time data from a Bluetooth-enabled wireless vibration-monitoring device in the tool holder. A study used a 23 factorial design and ANOVA to analyze the impact of cutting speed (v), feed per tooth (S), and depth of cut (t) on surface roughness (Ra) and vibration amplitude (A). The model showed high reliability (R2 ≥ 90%), with cutting speed (v) significantly affecting vibration amplitude (A) and feed per tooth (S) significantly affecting surface roughness (Ra). The optimal settings for Derringer’s desirability multi-objective optimization method were (v = 45 mm/min), (S = 0.02 mm/tooth), and (t = 0.2 mm), resulting in an approximate surface roughness (Ra) of 3.07 µm and a vibration amplitude (A) of 0.01196 mm. The study also establishes “feasible machining zones” with (Ra ≤ 0.4 µm and A ≤ 0.04 mm) for intelligent, adaptive control in Industry 4.0 compatible milling systems.