Optimization and Analysis of Tool Life Based On Flank Wear in a Turning Process
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 7
Abstract
Owing to the numerous interacting variables involved in a turning process, it is extremely difficult to assess the performance of a machining operation. The mathematical models which are currently in use for predicting the parameters like surface finish, tool life, tool wear etc. are highly non-linear because turning process is complex, random and uncertain in nature. A new methodology for finding the tool life is discussed in this project. In turning process, the machinability and tool wear are mainly stochastic rather than deterministic because of its complexity in nature. Tool change strategies are now based on the most conservative estimate of tool life from the past tool wear data. Always a complex relationship exists between process parameters like speed, feed, depth of cut, cutting time, tool geometry and cutting forces. Hence there is a need to develop models, which can capture this complex interrelationship between the parameters. In this project an empirical relationship has been developed between the parameters based on the experimental data. By taking speed, feed, depth of cut and cutting time as the input variables, the cutting forces and the flank wear are found out experimentally for a given tool material combination. Based on these experimental data, a mathematical model has been developed. This model can be applied to all circumstances to estimate the cutting forces and the flank wear. An artificial neural network model has also been developed to estimate the flank wear under varying cutting conditions. The results achieved by both the approaches have been compared and found to be closely related.A tool life equation has been developed using Taylor’s tool life criteria. Using the genetic algorithm technique, the cutting conditions have been optimized. For these optimized cutting conditions optimized tool life has also been estimated. A tool called ANOVA technique has been used for finding the percentage contribution of each cutting parameter.
Authors and Affiliations
Mandhadi Anusha, Dr. Venkata Ramesh Mamilla, Dr. G. Lakshmi Narayana Rao
Keywords
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