IJSRSET16249 | Received : 01 July 2016 | Accepted : 05 July 2016 | July-August 2016 [(2)4: 41-45] © 2016 IJSRSET | Volume 2 | Issue 4 | Print ISSN : 2395-1990 | Online ISSN : 2394-4099 Themed Section: Engineering and Technology 41 Process Parameter Optimization of Surface Grinding for AISI 321 by Using Taguchi Method Avinash S. Jejurkar, Vijay L. Kadlag Department of Mechanical Engineering, SVIT, Chincholi, Nashik, Maharashtra, India ABSTRACT Recently Austenitic stainless steel AISI-321 finding many applications like Automotive, Aerospace, Nuclear, Chemical and Cryogenics. The proposed 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 behavior and achieve optimal operating processes parameters. A software package may be utilized which integrates these various models to simulate what happens during surface grinding processes. Predictions from this work will be further analyzed by calibration with actual data. It involves many variables such as depth of cut, work speed, feed rate, chemical composition of work piece, etc. The main aim of 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. Keywords: Austenitic stainless steel, surface grinding, Taguchi, S/N ratio ANOVA, Optimization I. INTRODUCTION The manufacturing method of surface grinding has been established in the production of slim and flat symmetrical components. Due to the complex set-up, which results from the big sensitivity of this grinding method to a multiplicity of geometrical, kinematical and dynamical influence parameters, surface grinding is rarely applied inside limited-lot production. The substantial characteristics of this grinding process square measure the synchronic steering and machining of the work piece on its edge. Surface grinding is an essential method for final machining of elements requiring sleek surfaces and precise tolerances. As compared with other machining processes, grinding is costly operation that ought to be used underneath optimum conditions. Although wide used in business, grinding remains perhaps the least understood of all machining processes. The major operating input parameters that influence the output responses, metal removal rate, surface roughness, surface damage, and tool wear etc., are: (i) wheel parameters: abrasives, grain size, grade, structure, binder, shape and dimension, etc., (ii) Work piece parameters: fracture mode, mechanical properties and chemical composition, etc., (iii) Process parameters: work speed, depth of cut, feed rate, dressing condition, etc., (iv) machine parameters: static and dynamic Characteristics, spindle system, and table system, etc. The proposed work takes the following input processes parameters specifically Work speed, feed rate and depth of cut. Alloy 321 is commonly wont to rigid flanges; this application needs precise surface roughness thanks to use in chemical handling pipelines or equipments. Due to this reason surface grinding for this application requires to be optimum. A software package might be used that integrates these numerous models to simulate what happens throughout surface grinding processes. Predictions from this simulation will be any analyzed by standardization with actual information. It involves several variables such as depth of cut, work speed, feed rate, chemical composition of work piece, etc. The main objective in any machining process is to maximize the Metal Removal Rate (MRR) and to reduce the surface roughness (Ra). In order to optimize these values Taguchi method, ANOVA and regression analysis is employed.