Abstract
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.
Keywords
- Machine Tool
- Tool Wear
- Neural Network Model
- Machine Surface
- Material Processing Technology
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2006 International Federation for Information Processing
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Banerjee, A., Bordatchev, E.V., Choudhury, S.K. (2006). Modelling and On-Line Monitoring of Machined Surface in Turning Operations. In: Information Technology For Balanced Manufacturing Systems. BASYS 2006. IFIP International Federation for Information Processing, vol 220. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-36594-7_52
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DOI: https://doi.org/10.1007/978-0-387-36594-7_52
Publisher Name: Springer, Boston, MA
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