Statistical modelling of depth milling in Ti-6AL4V using abrasive water jet machining

Author:

Mogul Yakub I1,Quadros Jaimon D2ORCID,Khan Sher Afghan3,Agrawal Manoj4,Kumar Indradeep5,Shaik Saboor6ORCID,Saleel Chanduveetil Ahamed7ORCID,Saxena Ashish8ORCID

Affiliation:

1. National Centre for Motorsport Engineering, University of Bolton, Bolton, UK

2. Department of Mechanical Engineering, University of Bolton, RAK Academic Center, Ras Al Khaimah, UAE

3. Department of Mechanical and Aerospace Engineering, Faculty of Engineering, International Islamic University Malaysia, Selangor, Malaysia

4. Department of Mechanical Engineering, GLA University, Mathura, UP, India

5. Department of Aeronautical Engineering, Institute of Aeronautical Engineering, Hyderabad, Telangana, India

6. School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

7. Department of Mechanical Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia

8. School of Mechanical Engineering, Lovely Professional University, Phagwara, India

Abstract

Multi-objective grey relational analysis optimization technique and multiple regression analysis were employed to determine the optimum values for depth of cut, surface roughness ( Ra), and kerf at entry and exit ([Formula: see text] and [Formula: see text]), for abrasive waterjet machining of Ti6AL4V materials. This method highlights a new process to extend the grey relational analysis technique for determining the optimum conditions for obtaining the best quality characteristics. The input parameters of the study were water pressure ( Wp), transverse speed ( Ts), abrasive mass flow rate ( Amf), abrasive orifice size ( Aos), nozzle/orifice diameter ratio ( N/Odia). The experiments were conducted as per the Taguchi-based L27 orthogonal array. The grey relational analysis technique found that Ts was the most significant parameter on the combined outputs. The regression models developed had an R2 of 81.58%, 79.79%%, 77.20%, and 74.39% for depth of cut, Ra, [Formula: see text] and [Formula: see text], respectively. Additionally, the analysis of variance showed that Wp and Aos had a significant influence on the output parameters. The predicted values were found to be reasonably close with the experimental values, and the maximum average deviation was 8.15% for [Formula: see text].

Funder

Deanship of Scientific Research, King Khalid University

Publisher

SAGE Publications

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