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Quality Monitoring of Surface Roughness and Roundness Using Hidden Markov Model

机译：利用隐马尔可夫模型的表面粗糙度和圆度的质量监测

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The surface roughness and roundness (SRR) are widely used indexes of mechanical product quality. How to implement the SRR monitoring is a crucial task. In this study, the hidden Markov models (HMMs) and the cutting vibration signals are applied to monitor the SRR in variant cutting conditions. Unlike most of the prior work only to reveal one element of the geometric specifications, based on the theoretical analysis of the influence of tool vibration displacement on the SRR, the vibration energy characteristic (VEC) is determined to serve as the characteristic for monitoring surface roughness (Ra) and roundness (Rd) synchronously. Which make up the insufficiency of the comprehensive monitoring of workpiece quality. Moreover, although classical hidden Markov models (HMMs) have been successfully used for fault diagnostics of mechanical systems, this method based on recognition rate is becoming unreliable to monitor the accuracy of the workpiece. Hence, the HMM-based judgment matrix method is proposed and it is tested and validated successfully using for SRR monitoring through a series of experiments.
机译：表面粗糙度和圆度（SRR）广泛使用机械产品质量指标。如何实施SRR监控是一个至关重要的任务。在本研究中，应用隐马尔可夫模型（HMMS）和切割振动信号以监测变体切削条件中的SRR。与大多数事先工作不同，仅揭示几何规范的一个元素，基于工具振动位移对SRR的影响的理论分析，确定振动能量特性（VEC）用作监测表面粗糙度的特性（RA）和圆度（RD）同步。这弥补了综合监测工件质量的不足。此外，虽然经典隐马尔可夫模型（HMMS）已成功用于机械系统的故障诊断，但该方法基于识别率越来越不可靠，以监测工件的准确性。因此，提出了基于HMM的判断矩阵方法，并通过一系列实验使用进行SRR监控成功测试和验证。

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