Formulation of Mathematical Model for the Investigation of Tool Wears in Boring Machining Operation on Cast Iron Using Carbide and CBN Tools

Abstract

Gray Cast Iron Casting (GCIC) materials are widely used particularly in automotive industries. However, the high cost of processing these materials limits the use of their improved mechanical properties. Tool life is one of the most important factors in machining operations of such materials and it is mainly affected by cutting conditions including the cutting speed, depth of cut, insert material and cooling environment along with length and diameter of the tool body. In addition, the modern industry is moving towards automating the manufacturing processes. Therefore, tool life monitoring is important to achieve an efficient manufacturing process. In this study, a tool wear prediction model during the boring machining operation of gray cast iron is studied. It is based on the monitoring of tool performance in controlled machining tests with measurements of tool life, surface finish, bore size variation, cutting time and load on spindle in terms of % current under different combinations of cutting parameters (cutting speed, depth of cut, tool nose radius, length & diameter of tool, tool material and coolant pressure & concentration). The influence of cutting parameters on the tool life was studied experimentally by performing more than 120 cutting tests. A prediction model was then developed to predict tool wear. The basic steps used in generating the model adopted in the development of the prediction model are: collection of data; analysis, pre-processing and feature extraction of the data, design of the prediction model, training of the model and finally testing the model to validate the results and its ability to predict tool wear. The evolution of boring machining operation properties using different parameters is a complex phenomenon. There are many factors (like cutting speed, depth of cut, insert material and cooling environment along with length and diameter of the tool bodyaffecting the performance of cast iron boring machining operation resulting to poor tool life. This paper presents an experimental investigations and Sequential classical experimentation technique has been used to perform experiments for various independent parameters. An attempt of mini-max principle has been made to optimize the range bound process parameters for minimizing cutting time and surface finish during cast iron boring machining operation. The test results proved that cutting time and surface finish were significantly influenced by changing important four dimensionless π terms. The process parameters grouped in π terms were suggested the effective guidelines to the manufacturer for improving tool life by changing any one or all from theavailable process parameters.

Exploiting Tool Toughness through Turning Process Parameter Optimization

“…Additionally, new approaches for enhancing cutting tool life by optimizing velocity and feed throughout the process have been proposed [36]. Tool life equations and wear models are established from experimental data, and optimization techniques are implemented to maximize tool life while maintaining a constant metal removal rate [37][38].…”

Regression and ANN Models of Pulse Rate for Tricycle Rickshaw

“…It is important to obtain results of an experiment quantitatively for regression model to faceplate understanding, interpretation and implementation [20][21]. Regression analysis is highly useful in design experiments where somewhat has gone incorrect.…”

Analysis of Machining Parameters in WEDM of Al/SiCp20 MMC Using Taguchi-Based Grey-Fuzzy Approach

“…A good agreement with experimental and calculated surface roughness has observed in the presented work. Kadu et al [15] used dimensional analysis approach for analyzing the performance of boring machining operation. e factors such as depth of cut, cutting speed, insert material, and machining environment along with the diameter and length of the tool have been considered as the influencing parameters.…”