Parameters Optimization on Abrasive Water Jet Machining of Zerconia Ceramics

Abstract

Ceramic materials play a vital role in industries for several reasons, owing to their unique combination of properties such as high temperature resistance, high wear and abrasion resistance and electrical insulation. Conducting experiments on an abrasive water jet machine involves manipulating three input parameters at three levels each. The parameters are water pressure (1.5 bar, 2.5 bar, and 3.5 bar), standoff distance (4 mm, 8 mm, and 12 mm), and abrasive flow rate (6 gm/sec, 12 gm/sec, and 18 gm/sec). The responses measured are material removal rate (MRR) and surface roughness (Ra). To systematically study the impact of these parameters the Design of Experiments (DOE) methodology is employed utilizing the L9 Orthogonal Array (OA) to streamline the experimental design. The Taguchi technique is then applied to predict the optimal process parameters. For a comprehensive analysis of Variance (ANOVA) is employed to quantify the percentage contribution of each input parameter to the responses shedding light on their relative significance. Additionally the regression coefficient (R2) is assessed to gauge the degree of agreement between the experimental and predicted values for the responses.

Introduction

I. INTRODUCTION

Zirconium oxide, commonly known as zirconium (ZrO2) is a remarkable ceramic material that has garnered significant attention across industries due to its exceptional combination of properties. Formed from the chemical bonding of zirconium and oxygen atoms, zirconium exhibits a crystalline structure that can vary from cubic to tetragonal or monoclinic [1] each configuration offering distinct characteristics. With its high melting point [2], impressive mechanical strength, chemical inertness and biocompatibility, zirconium oxide has found myriad applications in fields as diverse as aerospace, biomedicine, electronics, and industrial manufacturing [3].

The abrasive water jet machine represents a cutting edge technology that revolutionizes the precision cutting and shaping of materials [4] across various industries. Harnessing the power of high pressure water streams infused with abrasive particles [5] this cutting system offers unparalleled versatility accuracy and efficiency in processing a wide range of materials, from metals and composites to stone and ceramics [6].

The Taguchi Method, developed by Dr. Genichi Taguchi is a powerful statistical approach to design optimization and quality improvement in manufacturing processes [7]. This methodology rooted in experimental design and robust engineering principles, aims to minimize variation and optimize performance by identifying and controlling key factors that influence product quality and performance [8].

ANOVA, or Analysis of Variance, is a statistical method used to analyze the differences among group means in a sample [9]. It is a powerful tool for comparing the means of three or more groups and determining whether there are statistically significant differences between them.

ANOVA is widely used in various fields such as psychology, biology, economics, and social sciences to compare the effects of different treatments or interventions on a dependent variable [10].

II. EXPERIMENTATION AND METHODOLOGY:

Size of the Specimen 50*50*5 mm

Experiment conducted on abrasive water jet machine on zerconia ceramic materials

Conclusion

The study utilized an L9 orthogonal array for experimentation. The results showed that all input parameters had probability values for responses that were less than 0.5, indicating their significance on the responses. ANOVA analysis revealed the percentage contribution of each parameter to the outcomes. For the material removal rate, abrasive flow rate had the highest contribution at 63.42%, followed by standoff distance at 19.36%, and water pressure at 12.7%. Regarding surface roughness, standoff distance had the most significant contribution at 49.55%, followed by water pressure at 30.15%, and abrasive flow rate at 8.86%. The regression coefficients (R2) for material removal rate and surface roughness were found to be 95.53% and 88.58%, respectively, at a 99% confidence level, as confirmed by further testing at the same confidence level. These findings underscore the substantial impact of the input parameters on the studied responses, highlighting the importance of optimizing these parameters for desired

References

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Copyright

Copyright © 2024 Dr. B. Anjaneyulu, P. Bhuvaneswar, K. Amaranath, A. Chennakesava Reddy, Shaik Althaf, V. Charan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.