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<p>In this study, a multi-objective optimization of AA7075-T6 machining performance was conducted using data from cutting force, vibration, power consumption, and surface roughness. A hybrid Taguchi–GRA–CRITIC optimization model was employed to evaluate and optimize multiple performance criteria objectively. Additionally, the effects of vibration on surface integrity were examined through Power Spectral Density (PSD) and Short-Time Fourier Transform (STFT) analyses. The optimal machining parameters (350&#xa0;m/min cutting speed, 3&#xa0;mm radial depth of cut, and 0.32&#xa0;mm/tooth feed rate) resulted in a 44.72% reduction in cutting force, 80.43% reduction in vibration amplitude, 20.41% reduction in power consumption, and 71.80% improvement in surface roughness compared to the baseline condition. Furthermore, the predictive accuracy of the optimized responses was experimentally validated with 99.603% consistency. Beyond optimization, time–frequency analysis of vibration signals (PSD and STFT) revealed that variations in mid-frequency vibration components directly influenced surface pattern formation, linking machining stability to surface integrity. Moreover, correlation analysis demonstrated strong interdependencies among surface roughness, vibration, and cutting force, indicating that surface quality is predominantly governed by dynamic cutting behavior. Overall, the study presents a comprehensive framework that integrates multi-sensor feature extraction with hybrid decision-based optimization, providing both quantifiable performance enhancements and mechanistic insights into vibration-induced surface formation during milling.</p>

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Experimental investigation and multi-sensor based optimization of machining parameters considering vibration effects

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月3日

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  • Bayram Sercan Bayram,
  • Mehmet Okan Kabakçı,
  • İbrahim Baki Şahin,
  • İhsan Korkut

摘要

In this study, a multi-objective optimization of AA7075-T6 machining performance was conducted using data from cutting force, vibration, power consumption, and surface roughness. A hybrid Taguchi–GRA–CRITIC optimization model was employed to evaluate and optimize multiple performance criteria objectively. Additionally, the effects of vibration on surface integrity were examined through Power Spectral Density (PSD) and Short-Time Fourier Transform (STFT) analyses. The optimal machining parameters (350 m/min cutting speed, 3 mm radial depth of cut, and 0.32 mm/tooth feed rate) resulted in a 44.72% reduction in cutting force, 80.43% reduction in vibration amplitude, 20.41% reduction in power consumption, and 71.80% improvement in surface roughness compared to the baseline condition. Furthermore, the predictive accuracy of the optimized responses was experimentally validated with 99.603% consistency. Beyond optimization, time–frequency analysis of vibration signals (PSD and STFT) revealed that variations in mid-frequency vibration components directly influenced surface pattern formation, linking machining stability to surface integrity. Moreover, correlation analysis demonstrated strong interdependencies among surface roughness, vibration, and cutting force, indicating that surface quality is predominantly governed by dynamic cutting behavior. Overall, the study presents a comprehensive framework that integrates multi-sensor feature extraction with hybrid decision-based optimization, providing both quantifiable performance enhancements and mechanistic insights into vibration-induced surface formation during milling.