A new in-process material removal rate monitoring approach in abrasive belt grinding

Belt grinding is a material processing operation which is capable of producing parts to high dimensional accuracy, excellent finish surface, and surface integrity. Unlike turning or other metal cutting operations that use geometrically well-defined tools, belt grinding involves tool geometry and cutting actions that are not well defined. Therefore, it is quite difficult to obtain a comprehensive theoretical model to predict the grinding depth. As well known, the strain rate is very high in grinding with abrasive tools, which results in substantial heat produced in the shear zone. Sparks are produced when the hot chips thrown out during the process, get oxidized, and burn in the atmosphere. Spark is an inherent feature for most dry grinding process. An approach on material removal rate monitoring in belt grinding by spark field measurement is proposed. The size of the spark field is a visualized reflection of the number of chips instantaneous produced in grinding. Features of spark field related to the material removal rate are analyzed and quantified. With this method, the coupling between the grinding parameters is no need to be considered. Experimental results indicate that the changing of grinding parameters has a different impact on the feature values of the spark field. Feature values of the spark field, such as area, boundary, and density, show a tight correlation with the material removal rate. The resolution accuracy of spark features on the grinding depth is studied. The correct rate of the grinding depth identification can reach more than 95% for the area, and density features of the spark field with the resolution range greater than 10 μm. The analysis indicates that the proposed method is effective and easy-to-accomplish for material removal rate monitoring in belt grinding.

[1]  Gerhard Hammann Modellierung des Abtragsverhaltens elastischer, robotergeführter Schleifwerkzeuge , 1998 .

[2]  H. Müller,et al.  Simulation and verification of belt grinding with industrial robots , 2006 .

[3]  Jian Yong Li,et al.  Modeling Material Removal Rate of Heavy Belt-Grinding in Manufacturing of U71Mn Material , 2016 .

[4]  Liang Liao,et al.  Adaptive Control of Pressure Tracking for Polishing Process , 2010 .

[5]  Xiang Zhang,et al.  An efficient method for solving the Signorini problem in the simulation of free-form surfaces produced by belt grinding , 2005 .

[6]  T. Zhao,et al.  Surface roughness prediction and parameters optimization in grinding and polishing process for IBR of aero-engine , 2014 .

[7]  Ying Zhang,et al.  Kinematic analysis and feedrate optimization in six-axis NC abrasive belt grinding of blades , 2015 .

[8]  Yun Huang,et al.  Equivalent self-adaptive belt grinding for the real-R edge of an aero-engine precision-forged blade , 2016 .

[9]  Meena Periya Samy,et al.  In-Process Surface Roughness Estimation Model for Compliant Abrasive Belt Machining Process , 2016 .

[10]  Nie Meng,et al.  Investigating the effects of contact pressure on rail material abrasive belt grinding performance , 2017 .

[11]  Yankai Wang,et al.  Model of an abrasive belt grinding surface removal contour and its application , 2016 .

[12]  V. Radhakrishnan,et al.  A study on the thermal aspects of chips in grinding , 1992 .

[13]  XiaoQi Chen,et al.  Robotic grinding and polishing for turbine-vane overhaul , 2002 .

[14]  R. Cook,et al.  Advanced Mechanics of Materials , 1985 .

[15]  Xiaohu Xu,et al.  A Robotic Belt Grinding Force Model to Characterize the Grinding Depth with Force Control Technology , 2018, ICIRA.

[16]  W. J. McDonald,et al.  The Effect of Oxygen and Water on the Dynamics of Chip Formation during Grinding , 1969 .

[17]  Wahyu Caesarendra,et al.  Predictive Modelling and Analysis of Process Parameters on Material Removal Characteristics in Abrasive Belt Grinding Process , 2017 .

[18]  H. Zahouani,et al.  The effect of abrasive grain's wear and contact conditions on surface texture in belt finishing , 2007 .

[19]  V. Radhakrishnan,et al.  On the Possibility of Process Monitoring in Grinding by Spark Intensity Measurements , 1994 .

[20]  Xiang Zhang,et al.  A local process model for simulation of robotic belt grinding , 2007 .

[21]  H. Zahouani,et al.  New criterion of grain size choice for optimal surface texture and tolerance in belt finishing production , 2009 .