MACHINING OF HARDSTONE QUARTZ WITH MODIFIED AJM PROCESS USING HOT SiC ABRASIVES: ANALYSIS, MODELING, OPTIMIZATION, AND COST ANALYSIS

Author:

PRADHAN SUBHADIP1,DAS SUDHANSU RANJAN1,NANDA BASANTA KUMAR2,JENA PANKAJ CHARAN1,DHUPAL DEBABRATA1

Affiliation:

1. Department of Production Engineering, Veer Surendra Sai University of Technology, Burla 768018, Odisha, India

2. School of Mechanical Engineering, KIIT University, Bhubaneswar 751024, Odisha, India

Abstract

Machining of hard and brittle materials such as engineering ceramics, glass, and silicon is a formidable task. Unlike cutting processes employing plasma and lasers, better machining capabilities of abrasive jet machining are characterized by thermally damaged free surface which is highly competitive as well as important for survival of materials in service. In this paper, an attempt has been made to combine hot abrasives and compressed air to form a hot abrasive air jet. This study aims to analyze the cutting performance in hot-abrasive jet machining (HAJM) of hardstone quartz concerning surface roughness, taper angle (TA), and material removal rate (MRR). Combined approach of Box–Behnken design — analysis of variance, response surface methodology, and statistical technique (here desirability function approach), followed by computational approach (here genetic algorithm), is, respectively, employed for experimental investigation, predictive modeling, and multi-response optimization. Thereafter, the effectiveness of proposed two multi-objective optimization techniques is evaluated by confirmation test and subsequently, the best optimal solution (i.e. at air pressure of 7[Formula: see text]kgf/cm2, abrasive temperature of [Formula: see text]C, stand-off distance of 4 mm) is used for economic analysis. Result shows that the most significant parameter is abrasive temperature for surface roughness, whereas it is pressure in case of both TA and MRR. Applications of hot abrasives in AJM process have shown attention in enhancing the cutting performance for material removal. Due to lower percentage contribution of error (6.68% to Rz, 9.89% to TA, and 6.42% in case of MRR), a higher correlation coefficient ([Formula: see text]) was obtained with the quadratic regression model, which showed values of 0.92, 0.9, and 0.93 for surface roughness, TA, and MRR, respectively.

Funder

All India Council for Technical Education

Publisher

World Scientific Pub Co Pte Lt

Subject

Materials Chemistry,Surfaces, Coatings and Films,Surfaces and Interfaces,Condensed Matter Physics

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