Modelling and On-Line Monitoring of Machined Surface in Turning Operations

Machined surface profile and roughness are important parameters in evaluating the quality of a machining operation. They are resulted from the transformation of the complex tool-workpiece displacements involving the dynamics of the machine tool mechanical system, cutting process, and cutting motions. The focus of this study is the fundamental understanding of the surface profile formation during turning and development of regression and neural network (NN) models of surface roughness incorporating the effects of cutting parameters and tool-workpiece displacements. Also, a bifurcated opto- electrical transducer was developed for on-line monitoring of surface roughness based on the scattering of laser beams from machined surface. The feasibility of on-line monitoring was studied by comparing with actual roughness as well as the prediction results of the regression and NN models.

[1]  Ossama B. Abouelatta,et al.  Surface roughness prediction based on cutting parameters and tool vibrations in turning operations , 2001 .

[2]  Hiroyuki Hiraoka,et al.  Analysis of Surface Roughness Generation in Turning Operation and its Applications , 1985 .

[3]  Patricia Muñoz-Escalona,et al.  Influence of the critical cutting speed on the surface finish of turned steel , 1998 .

[4]  Joseph C. Chen,et al.  Multiple Regression-Based Multilevel In-Process Surface Roughness Recognition System in Milling Operations , 2022 .

[5]  Sounak Kumar Choudhury,et al.  On-line tool wear sensing and compensation in turning , 1995 .

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

[7]  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 .

[8]  M. Shiraishi In-Process Measurement of Surface Roughness in Turning by Laser Beams , 1981 .

[9]  C. S. Lee,et al.  An In-Process Measurement Technique Using Laser for Non-Contact Monitoring of Surface Roughness and Form Accuracy of Ground Surfaces , 1987 .

[10]  Marc Thomas,et al.  Effect of tool vibrations on surface roughness during lathe dry turning process , 1996 .

[11]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[12]  Shih-Chieh Lin,et al.  A study on the effects of vibrations on the surface finish using a surface topography simulation model for turning , 1998 .

[13]  D. E. Dimla,et al.  Neural network solutions to the tool condition monitoring problem in metal cutting—A critical review of methods , 1997 .

[14]  David Dornfeld,et al.  Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring , 1990 .

[15]  Imtiaz Ahmed Choudhury,et al.  Surface roughness prediction in the turning of high-strength steel by factorial design of experiments , 1997 .