A new mathematical method of modeling parts in virtual CNC lathing and its application on accurate tool path generation

In industry, many taper shafts are designed with tolerances of a few microns. To cut them in finish turning, paths of the tool in virtual machining should be accurately generated beforehand. For this purpose, the dimension errors and surface roughness of the virtually cut workpiece should be predicted. Unfortunately, the current tool path generation methods cannot accurately calculate the errors and the roughness, resulting in the taper errors larger than the part tolerance. Our research formulates equations of the effective turning edge to accurately calculate the dimension errors and the surface roughness, and then proposes a new approach to CNC programming for high-precision CNC turning. It lays a theoretical foundation of modeling parts in virtual turning and can generate tool paths to machine taper parts with high accuracy.

[1]  Dong Young Jang,et al.  Study of the correlation between surface roughness and cutting vibrations to develop an on-line roughness measuring technique in hard turning , 1996 .

[2]  Uday S. Dixit,et al.  Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process , 2003 .

[3]  T. Özel,et al.  Effects of cutting edge geometry, workpiece hardness, feed rate and cutting speed on surface roughness and forces in finish turning of hardened AISI H13 steel , 2005 .

[4]  Pedro Paulo Balestrassi,et al.  Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi's orthogonal arrays , 2012, Expert Syst. Appl..

[5]  Mehmet Çunkas,et al.  Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method , 2011, Expert Syst. Appl..

[6]  R. Du,et al.  Analysis and compensation of workpiece errors in turning , 2002 .

[7]  Joze Balic,et al.  Intelligent Programming of CNC Turning Operations using Genetic Algorithm , 2006, J. Intell. Manuf..

[8]  Can Cogun,et al.  A cutting force induced error elimination method for turning operations , 2005 .

[9]  K. Ridgway,et al.  Integration of CAD and a cutting tool selection system , 2002 .

[10]  Ioan D. Marinescu,et al.  Effect of tool wear on surface finish for a case of continuous and interrupted hard turning , 2005 .

[11]  S. S. Pande,et al.  Intelligent tool path correction for improving profile accuracy in CNC turning , 2000 .

[12]  W. Grzesik,et al.  Surface finish generated in hard turning of quenched alloy steel parts using conventional and wiper ceramic inserts , 2006 .

[13]  Tuğrul Özel,et al.  Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks , 2005 .

[14]  Peter Smid.  CNC programming handbook : a comprehensive guide to practical CNC programming , 2008 .

[15]  Kusum Deep,et al.  Parameter optimization of multi-pass turning using chaotic PSO , 2015, Int. J. Mach. Learn. Cybern..

[16]  Shuting Lei,et al.  Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations , 2004 .