Surface Roughness Control Simulation of Turning Processes

The aim of this paper is to present a surface roughness control in turning with an associated simulation block diagram. The objective of the new model based controller is to assure the desired surface roughness by adjusting the machining parameters and maintaining a constant cutting force. It modifies the feed rate on-line to keep the surface roughness constant and to make machining more efficient. The control model was developed based on simplified models of the turning process and the feed drive servo-system. The experiments were performed to find the correlation between surface roughness and cutting forces in turning and to provide functional correlation with the controllable factors. Simulation setup and results are presented to demonstrate the efficiency of the proposed control model. In terms of surface roughness fluctuations and cutting efficiency, the suggested control model is much better than a conventional CNC controller alone. Integrating the developed control model with the CNC machine controller significantly improves the quality of machined components.

[1]  Jun-Ho Oh,et al.  Model Reference Adaptive Control of the Milling Process , 1983 .

[2]  A. Galip Ulsoy,et al.  A comparison of model-based machining force control approaches , 2004 .

[3]  Ruey-Jing Lian,et al.  Intelligent control of a constant turning force system with fixed metal removal rate , 2012, Appl. Soft Comput..

[4]  L. Daneshmend,et al.  Model Reference Adaptive Control of Feed Force in Turning , 1986 .

[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]  Peter Krajnik,et al.  Creep-Feed Grinding: An Overview of Kinematics, Parameters and Effects on Process Efficiency , 2014 .

[7]  Franci Cus,et al.  Approach to optimization of cutting conditions by using artificial neural networks , 2006 .

[8]  Y. S. Tarng,et al.  A neural network controller for constant turning force , 1994 .

[9]  U. Zuperl,et al.  Tool cutting force modeling in ball-end milling using multilevel perceptron , 2004 .

[10]  Franci Cus,et al.  Optimization of cutting conditions during cutting by using neural networks , 2003 .

[11]  Robert G. Landers,et al.  Supervisory Machining Control: Design Approach and Experiments , 1998 .

[12]  I. Korkut,et al.  The influence of feed rate and cutting speed on the cutting forces, surface roughness and tool–chip contact length during face milling , 2007 .

[13]  C. L. Hwang,et al.  Adaptive turning force control with optimal robustness and constrained feed rate , 1993 .

[14]  Yoram Koren,et al.  Computer control of manufacturing systems , 1983 .

[15]  Oğuz Çolak,et al.  Optimization of Machining Performance in High-Pressure Assisted Turning of Ti6Al4V Alloy , 2014 .

[16]  Pitstra,et al.  Controller designs for constant cutting force turning machine control , 2000, ISA transactions.

[17]  A. Galip Ulsoy,et al.  Model Reference Adaptive Force Control in Milling , 1989 .

[18]  Danko Brezak,et al.  Tool wear estimation using an analytic fuzzy classifier and support vector machines , 2012, J. Intell. Manuf..

[19]  G. Stute,et al.  Adaptive Control System for Variable Gain in Acc Systems , 1976 .