Xuefeng Zhao, Hui Li, Lin He, Meng Tao
Mechanical Engineering College, Guizhou University, Guiyang, China.
Sci Prog. 2020 Jul-Sep;103(3):36850420957903. doi: 10.1177/0036850420957903.
High-speed and high-efficient machining is the inevitable development direction of machining technology. The tool edge preparation can improve the life, cutting performance, and surface quality of a tool and help to achieve high-speed and efficient machining. Therefore, precise modeling and detection of the micron-level contour of a tool edge are crucial for edge preparation. The aim of this study is to provide the model and detect method of the prepared tool edge radius.
The mathematical model of the milling tool trajectory is established through the Matlab. The material removal model by single abrasive particle is established based on the energy conservation principle and energy absorption theory. The material removal model by multiple abrasive grains on the cutting tool edge is constructed using the statistical methods. The mathematical model of the edge radius is established through the geometrical relationship. The milling edge preparation contour detection system is setup based on the machine vision principle through LabVIEW software. Finally, the edge radius at different process parameters is determined by the mathematical model and detection system, and the results are compared with the results of the scanning electron microscopic measurement (SEM).
Through the Comparison and analysis of the edge radius measured by the SEM and calculated by the proposed model. The maximum error between the analytical results and SEM measurements is 11.18 μm, while the minimum error is 0.07 μm. Through the comparison and analysis of the edge radius measured by the SEM and the edge detection system. The maximum difference between the two methods is 2.71 μm, and the minimum difference is 0.31 μm. The maximum difference in percentage is 9.2%, and the minimum difference in percentage is 1.2%.
The edge preparation mechanisms of a single particle and multiple particles on the tool edge are explained. A mathematical model of the edge radius is established, which provides a basis for a deeper understanding of the edge preparation effect. Based on the machine vision principle, the prepared tool micron-level edge detection method is proposed. The histogram specification method, median filtering, multi-threshold segmentation method, and Canny edge detection operator are adopted to obtain the edge contour. The comparison result shows that the mathematical model of the edge radius is accurate, and the proposed tool edge detection method is feasible, which lays the foundation for edge preparation and realization of high-speed and high-efficient machining.
高速高效加工是加工技术的必然发展方向。刀具刃口预处理可提高刀具寿命、切削性能和表面质量，有助于实现高速高效加工。因此，精确建模和检测刀具刃口的微米级轮廓对于刃口预处理至关重要。本研究旨在提供预处理刀具刃口半径的模型及检测方法。
通过Matlab建立铣刀轨迹的数学模型。基于能量守恒原理和能量吸收理论建立单颗磨粒的材料去除模型。采用统计方法构建刀具刃口多颗磨粒的材料去除模型。通过几何关系建立刃口半径的数学模型。基于机器视觉原理，利用LabVIEW软件搭建铣刀刃口预处理轮廓检测系统。最后，通过数学模型和检测系统确定不同工艺参数下的刃口半径，并将结果与扫描电子显微镜测量（SEM）结果进行比较。
通过对SEM测量的刃口半径与所提模型计算结果的比较分析，分析结果与SEM测量结果的最大误差为11.18μm，最小误差为0.07μm。通过对SEM测量的刃口半径与刃口检测系统测量结果的比较分析，两种方法的最大差值为2.71μm，最小差值为0.31μm。最大百分比差值为9.2%，最小百分比差值为1.2%。
阐述了刀具刃口上单颗和多颗磨粒的刃口预处理机制。建立了刃口半径的数学模型，为深入理解刃口预处理效果提供了依据。基于机器视觉原理，提出了预处理刀具微米级刃口检测方法。采用直方图规定化方法、中值滤波、多阈值分割方法和Canny边缘检测算子获取边缘轮廓。比较结果表明，刃口半径数学模型准确，所提刀具刃口检测方法可行，为刃口预处理及高速高效加工的实现奠定了基础。
