The surface finish quality of a machined workpiece is commonly measured using the average roughness parameter, R_a. This parameter, however, is insensitive to the lateral changes undergone by the surface in the feed direction as a consequence of tool wear. In this work, the effectiveness of four methods of workpiece surface analysis, namely autocorrelation, cross-correlation, and two new methods, called lateral material shift (LMS) ratio and profile slope ratio (PSR) analyses are investigated. Dry machining experiments were carried out on 316 stainless steel. Images of tool nose and workpiece profiles were captured using digital camera, and the edges were extracted using sub-pixel edge detection. In the autocorrelation approach, each workpiece profile was correlated with a shifted version of the same profile. In the cross-correlation approach, the workpiece profiles at different stages of machining were correlated with a reference profile generated using the unworn tool edge. In the LMS ratio method, the material shift ratios were determined from each waveform on the workpiece profile at various stages of tool wear, while in the PSR method the slopes at the right and left part of the waveform were compared. Among the four methods, the LMS ratio method produced the best correlation with tool flank wear with the maximum R-squared value of 0.9461, while average roughness R_a showed no correlation at all with both major and nose flank wear.

通常使用平均粗糙度参数R _a来测量加工工件的表面光洁度质量。。但是，此参数对由于刀具磨损而导致的表面在进给方向上发生的横向变化不敏感。在这项工作中，研究了四种工件表面分析方法的有效性，即自相关，互相关，以及两种新方法，称为横向材料偏移（LMS）比和轮廓斜率比（PSR）分析。干加工实验是在316不锈钢上进行的。使用数码相机捕获刀尖和工件轮廓的图像，并使用亚像素边缘检测提取边缘。在自相关方法中，每个工件轮廓都与同一轮廓的移位版本相关。在互相关方法中，在不同加工阶段的工件轮廓与使用未磨损刀具边缘生成的参考轮廓相关。在LMS比率方法中，根据刀具磨损各个阶段的工件轮廓上的每个波形确定材料移动率，而在PSR方法中，比较波形左右部分的斜率。在这四种方法中，LMS比率方法与刀具侧面磨损的相关性最好，最大。R平方值为0.9461，而平均粗糙度R _a则与主要和鼻子侧面磨损完全没有相关性。