Online model-based milling process condition monitoring

In line with demanding the higher efficiency of milling operation, the requests for reliable cutting tool condition monitoring techniques are encountered with considerable interest. Cutting forces are considered the most significant process parameters for developing the condition monitoring techniques. In this article, a real time condition monitoring of milling process utilising recursive least square (RLS) algorithm is proposed. The developed model provides an immediate continuous supervision on the quality of cutting process through monitoring the performance of each tool teeth. The proposed mechanical-analytical approach facilitates the calculation of the cutting force components with any number of cutting edges, which are used not only for cutting tool condition monitoring purposes, but also provide the ground for investigation of the effects of the most influencing parameters of the cutting process such as geometry of cutting, work piece material, feed rate, and tool turning speed and so on. Inherently, due to correlation of the roughness with cutting forces through stiffness of the tool, the reliable estimation of the surface condition (roughness) is also available once the cutting forces are provided.

[1]  Rolf Isermann,et al.  Fault-Diagnosis Applications , 2011 .

[2]  Deyi Xue,et al.  A cutting force model considering influence of radius of curvature for sculptured surface machining , 2011 .

[3]  XiaoQi Chen,et al.  An experimental study of tool wear and cutting force variation in the end milling of Inconel 718 with coated carbide inserts , 2006 .

[4]  Evren Burcu Kivanc,et al.  Structural modeling of end mills for form error and stability analysis , 2004 .

[5]  Rolf Isermann,et al.  Identification of Dynamic Systems , 2011 .

[6]  Ship-Peng Lo,et al.  An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling , 2003 .

[7]  Robert M. Pap,et al.  Fault Diagnosis , 1990, Bayesian Networks in Fault Diagnosis.

[8]  J. Tlusty,et al.  Dynamics of High-Speed Milling , 1986 .

[9]  Ahmed A. D. Sarhan,et al.  Monitoring of Tool Wear and Surface Roughness in End-Milling for intelligent machining , 2011 .

[10]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems: theory and application , 1989 .

[11]  Mustafa Gölcü,et al.  Modeling of cutting forces as function of cutting parameters for face milling of satellite 6 using an artificial neural network , 2007 .

[12]  Yusuf Altintas,et al.  Mechanics and Dynamics of Ball End Milling , 1998 .

[13]  F. Koenigsberger,et al.  An investigation into the cutting force pulsations during milling operations , 1961 .

[14]  Amin Al-Habaibeh,et al.  A new approach for systematic design of condition monitoring systems for milling processes , 2000 .

[15]  E. Mathias,et al.  Experimental Full Cut Milling Dynamics , 1980 .

[16]  Frank L. Lewis,et al.  Intelligent Diagnosis and Prognosis of Tool Wear Using Dominant Feature Identification , 2009, IEEE Transactions on Industrial Informatics.

[17]  D. Jaume,et al.  A Model-Based Diagnosis in Machine Tools: Application to the Milling Cutting Process , 1990 .

[18]  K. Sudhakar Reddy,et al.  Latest Developments in Condition Monitoring of Machining Operations , 2012 .

[19]  Min Xu,et al.  Energy Based Cutting Force Model Calibration for Milling , 2007 .

[20]  Torsten Jeinsch,et al.  A Survey of the Application of Basic Data-Driven and Model-Based Methods in Process Monitoring and Fault Diagnosis , 2011 .

[21]  Graham T. Smith,et al.  Cutting Tool Technology: Industrial Handbook , 2008 .

[22]  Graham T. Smith.  Cutting Tool Technology , 1993 .

[23]  Rolf Isermann.  Model-based fault-detection and diagnosis - status and applications § , 2004 .

[24]  Rolf Isermann,et al.  Supervision, fault-detection and fault-diagnosis methods — An introduction , 1997 .

[25]  Ting Xu,et al.  Study of Adaptive Model Parameter Estimation for Milling Tool Wear , 2011 .