RCSA

Receptance Coupling Substructure Analysis (RCSA)

The RCSA approach analytically predicts the tool-holder-spindle-machine assembly frequency response by joining models and measurements of the individual components through appropriate connection parameters. A primary impediment to the full implementation of the large body of academic research in high-speed machining, particularly  chatter (or unstable machining) models, at the production floor is the necessity of measuring each tool-holder-spindle-machine frequency response, typically by impact testing. The chatter models, which can be used to select cutting conditions for both dramatic increases in achievable material removal rates and improved part accuracy, all require knowledge of the tool point frequency response. Due to a lack of engineering support and limited knowledge of dynamic testing procedures, the frequency response measurements may not be completed and the well-established stability-improvement technology (i.e., stability lobe diagrams, which separate stable and unstable cutting zones graphically as a function of chip width and spindle speed) afforded by high-speed machining is not applied.  The result is reduced process efficiency and part quality and, therefore, increased cost. Using RCSA, the tool point response can be predicted and the obstacle imposed by the necessity for impact testing is removed.

 

Journal publications

Ozturk, E., Kumar, U., Turner, S., and Schmitz, T., 2012, Investigation of Spindle Bearing Preload on Dynamics and Stability Limit in Milling, CIRP Annals – Manufacturing Technology, 61/1: 343-346, http://dx.doi.org/10.1016/j.cirp.2012.03.134.

Kumar, U. and Schmitz, T., 2012, Spindle Dynamics Identification for Receptance Coupling Substructure Analysis, Precision Engineering, 36/3: 435-443, http://dx.doi.org/10.1016/j.precisioneng.2012.01.007.

Zhang, J., Schmitz, T., Zhao, W., and Lu, B., 2011, Receptance Coupling for Dynamics Prediction of a Fluted Tool, Chinese Journal of Mechanical Engineering, 24/3: 340-345.

Houck III, L., Schmitz, T., and Smith, K.S., 2011, Tuned Holder for Increased Boring Bar Dynamic Stiffness, Journal of Manufacturing Processes, 13: 24-29,  http://dx.doi.org/10.1016/j.jmapro.2010.09.002.

Filiz, S., Cheng, C.-H, Powell, K., Schmitz, T., and Ozdoganlar, O., 2009, An Improved Tool-Holder Model for RCSA Tool-Point Frequency Response Prediction, Precision Engineering, 33: 26–36, http://dx.doi.org/10.1016/j.precisioneng.2008.03.003.

Cheng, C.-H., Duncan, G.S., and Schmitz, T., 2007, Rotating Tool Point Frequency Response Prediction using RCSA, Machining Science and Technology, 11(3): 433-446, http://www.tandfonline.com/doi/abs/10.1080/10910340701539866.

Schmitz, T., Powell, K., Won, D., Duncan, G.S., Sawyer, W.G., and Ziegert, J., 2007, Shrink Fit Tool Holder Connection Stiffness/Damping Modeling for Frequency Response Prediction in Milling, International Journal of Machine Tools and Manufacture, 47(9): 1368-1380, http://dx.doi.org/10.1016/j.ijmachtools.2006.08.009.

Schmitz, T. and Duncan, G.S., 2006, Receptance Coupling for Dynamics Prediction of Assemblies with Coincident Neutral Axes, Journal of Sound and Vibration, 289/4-5: 1045-1065, http://dx.doi.org/10.1016/j.jsv.2005.03.006.

Schmitz, T. and Duncan, G.S., 2005, Three-Component Receptance Coupling Substructure Analysis for Tool Point Dynamics Prediction, Journal of Manufacturing Science and Engineering, 127/4: 781-790, http://dx.doi.org/10.1115/1.2039102.

Duncan, G.S., Tummond, M., and Schmitz, T., 2005, An Investigation of the Dynamic Absorber Effect in High-Speed Machining, International Journal of Machine Tools and Manufacture, 45: 497-507, http://dx.doi.org/10.1016/j.ijmachtools.2004.09.005.

Schmitz, T., Davies, M., and Kennedy, M., 2001, Tool Point Frequency Response Prediction for High-Speed Machining by RCSA, Journal of Manufacturing Science and Engineering, 123: 700-707, http://dx.doi.org/10.1115/1.1392994.

Schmitz, T., Davies, M., Medicus, K., and Snyder, J., 2001, Improving High-Speed Machining Material Removal Rates by Rapid Dynamic Analysis, Annals of the CIRP, 50/1: 263-268, http://dx.doi.org/10.1016/S0007-8506(07)62119-2.

Schmitz, T. and Donaldson, R., 2000, Predicting High-Speed Machining Dynamics by Substructure Analysis, Annals of the CIRP, 49/1: 303-308, http://dx.doi.org/10.1016/S0007-8506(07)62951-5.

 

Machine Tool Genome Project (MTGP)

The objective of this project is to enable pre-process milling parameter selection for “first part correct” production. This will replace the current practice of trial-and-error part path validation and will be achieved by predicting the tool point frequency response function using the RCSA algorithm. Given the tool point response, frequency-domain algorithms for stability and surface location error can be applied to separate feasible and infeasible zones within the spindle speed-axial depth of cut domain. Once acceptable spindle speed-axial depth of cut combinations are determined, they will be presented in a new user-friendly format, the Tool Dashboard, similar to an automotive dashboard display. The intended customer for this project is any user of computer numerically-controlled (CNC) machining technology. This work is a collaboration with Manufacturing Laboratories, Inc. and BlueSwarf.

An example Tool Dashboard is shown.

 

This approach is analogous to the Human Genome Project (HGP), an international scientific research effort with the primary goal of determining the sequence of chemical base pairs which make up DNA and identifying and mapping the 20,000 to 25,000 genes of the human genome. HGP was launched in 1990 and completed in 2003. In the MTGP, the “genes” are the tool, holder, and machine and the “mapping” is performed using RCSA to predict the tool point frequency response, i.e., the “body characteristics”. The spindle-machine “genes” are measured once and archived. The customer can then define the desired tool and holder “genes” using a software application, select the spindle-machine from the archived database, and receive the corresponding Tool Dashboard to enable selection of preferred operating parameters which respect the limitations imposed by the process dynamics. The “mapping” steps of predicting the tool point frequency response, calculating the corresponding stability lobe diagram, and representing this information using the Tool Dashboard are transparent to the user.

 

http://www.mmsonline.com/articles/the-online-optimizer

http://blueswarf.assistly.com/customer/portal/articles/76015-machine-tool-genome-project

 

Join the MTGP LinkedIn group!

 

http://www.linkedin.com/groups?gid=4064655&trk