Open AccessSurface Grinding Parameters Optimization of Austenitic Stainless Steel (AISI 304)
Author(s) -
Tushar Khule,
Rahul H. Naravade,
Sagar Dadasaheb Shelke
Publication year - 2020
Publication title -
international journal of scientific research in science, engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-1990
pISSN - 2394-4099
DOI - 10.32628/ijsrset207337
Subject(s) - machining , grinding , surface roughness , mechanical engineering , surface finish , materials science , austenitic stainless steel , work (physics) , taguchi methods , surface integrity , tool steel , process (computing) , surface grinding , calibration , computer science , process engineering , engineering drawing , metallurgy , engineering , mathematics , composite material , statistics , corrosion , operating system
The assembling procedure of surface grinding has been set up in the large scale manufacturing of thin, rotationally even parts. Due to the complex set-up and geometrical, kinematical, dynamical influence parameters, surface grinding is rarely applied within limited-lot production. Surface crushing is a basic procedure for last machining of parts requiring smooth surfaces and exact resiliences.As contrasted and other machining forms, crushing is exorbitant activity that ought to be used under ideal conditions.. Although widely used in industry. The project work takes the following input processes parameters namely Work speed, feed rate and depth of cut. The main objective of this work is to predict the grinding behaviour and achieve optimal operating processes parameters. A software package is utilized which integrates these various models to simulate what happens during surface grinding processes. Predictions from this simulation will be further analysed by calibration with actual data. The main objective in any machining process is to maximize the Metal Removal Rate (MRR) and to minimize the surface roughness (Ra). In order to optimize these values Taguchi method, ANOVA is used. The surface roughness (Ra) value and Material Removal Rate (MRR), obtained from experimentation and confirmation test, for this the optimum control parameters are analysed.