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<p>Mechanical vibrations, which adversely affect the surface quality of machined components, pose critical challenges in machining processes. This study uses a predictive diagnostic performance system (PDPS) and ChatterPro software to investigate the relationship between vibration signals and surface quality. Experiments were conducted on a milling machine at various spindle speeds, with ten machining tests repeated five times under identical parameters, using consistent material and tooling for side milling. Vibration signals were processed through PDPS and analyzed via principal component analysis (PCA). The principal component distribution map and the correlation between chatter frequency and surface roughness were examined. The surface roughness measurements in the five groups with lower chatter vibrations (groups 1, 2, 3, 7, and 8) ranged from 0.36 to 0.68&#xa0;µm, while those in the five groups with higher chatter vibrations (groups 4, 5, 6, 9, and 10) ranged from 0.98 to 2.26&#xa0;µm. This demonstrates a proportional relationship between surface roughness and chatter frequency. Results indicated that increased chatter frequencies were associated with rougher surface finishes. This analysis demonstrates how specific vibration signals can detect chatter and assess cutting conditions in real-time. In future applications, software-based detection of vibration signals could allow for real-time monitoring of machining processes, enabling machine networking, mobile alerts, and reduced operator supervision.</p>

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Principal Component Analysis-Based Real-Time Diagnosis of Chatter, Vibration, and Surface Roughness for Machining Quality

  • 期刊论文

  • 发表日期：

    2025年3月26日

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  • T.C. Chan,
  • B.H. Huang,
  • R. Behera,
  • S.V.V.S. Reddy

摘要

Mechanical vibrations, which adversely affect the surface quality of machined components, pose critical challenges in machining processes. This study uses a predictive diagnostic performance system (PDPS) and ChatterPro software to investigate the relationship between vibration signals and surface quality. Experiments were conducted on a milling machine at various spindle speeds, with ten machining tests repeated five times under identical parameters, using consistent material and tooling for side milling. Vibration signals were processed through PDPS and analyzed via principal component analysis (PCA). The principal component distribution map and the correlation between chatter frequency and surface roughness were examined. The surface roughness measurements in the five groups with lower chatter vibrations (groups 1, 2, 3, 7, and 8) ranged from 0.36 to 0.68 µm, while those in the five groups with higher chatter vibrations (groups 4, 5, 6, 9, and 10) ranged from 0.98 to 2.26 µm. This demonstrates a proportional relationship between surface roughness and chatter frequency. Results indicated that increased chatter frequencies were associated with rougher surface finishes. This analysis demonstrates how specific vibration signals can detect chatter and assess cutting conditions in real-time. In future applications, software-based detection of vibration signals could allow for real-time monitoring of machining processes, enabling machine networking, mobile alerts, and reduced operator supervision.