Tool wear is a key factor that dominates the surface quality and distinctly influences the generated workpiece surface texture. In order to realize accurate evaluation of the tool wear from the generated workpiece surface after machining process, a new tool wear monitoring method is developed by fractal dimension of the acquired workpiece surface digital image. A self-made simple apparatus is employed to capture the local digital images around the region of interest. In addition, a skew correction method based on local fast Fourier transformation energy is also proposed for the surface texture direction adjustment. Furthermore, the tool wear quantitative evaluation was derived based on fractal dimension utilizing its high reliability for inherent irregularity description. The proposed tool wear monitoring method has verified its feasibility as well as its effectiveness in actual milling experiments using the material of AISI 1045 in a vertical machining center. Testing results demonstrate that the proposed method was capable of tool wear condition evaluation.

中文翻译：

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一种改进的基于工件局部图像和分形维数的刀具磨损监测方法

刀具磨损是决定表面质量的关键因素，并明显影响生成的工件表面纹理。为实现加工后生成的工件表面对刀具磨损情况的准确评估，提出了一种新的刀具磨损监测方法，即通过采集的工件表面数字图像的分形维数。采用自制的简单装置捕获感兴趣区域周围的局部数字图像。此外，还提出了一种基于局部快速傅立叶变换能量的倾斜校正方法用于表面纹理方向的调整。此外，利用其固有的不规则描述的高可靠性，基于分形维数推导出刀具磨损定量评估。所提出的刀具磨损监测方法已在立式加工中心使用 AISI 1045 材料的实际铣削实验中验证了其可行性和有效性。测试结果表明，所提出的方法能够评估刀具磨损状况。