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Prediction of surface roughness using semi-empirical and regression models in machining of metal matrix composites using abrasive waterjet

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Abstract

Prediction of surface roughness has always been a challenging task in the abrasive waterjet machining (AWJM) of metal matrix composites (MMCs), since it involves quite a number of process and material variables. The present study explores the prediction of surface roughness (Ra) through development of models, namely regression and semi-empirical models for AWJM of unreinforced Al 6063 alloy, Al 6063 reinforced with 5%, 10%, and 15% boron carbide (B4C) particles. Accordingly, AWJM trials were performed using Box-Behnken experimental design by kee** the process variables viz. mesh size (ms), mass flow rate (ma), water pressure (p), and traverse speed (u) each at three levels. Response surface methodology (RSM) was applied to formulate a regression model using the data generated from experiments. The results analyzed through analysis of variance (ANOVA) revealed that ms, water pressure, and traverse speed render a significant contribution in the generation of Ra. The experimental investigations showed that the combination of process parameters such as ms of # 120, mass flow rate of 340 g/min, water pressure of 275 MPa, and traverse speed of 60 mm/min have resulted in lower Ra. By utilizing both the experimental data and materials properties data, a semi-empirical model was developed through dimensional analysis by applying Buckingham’s π-theorem. Predictions of the developed models were in good correlation with the data obtained through experiments under corresponding conditions. The study undertaken here proved that the semi-empirical models could efficiently predict the Ra in AWJM of MMCs.

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All data generated or analyzed during this study are included in this published article.

The submitted work is original, complete and has not been submitted/published elsewhere in any form or language.

Abbreviations

\({d}_{n}\) :

Diameter of nozzle (mm)

\({d}_{0}\) :

Diameter of orifice (mm)

\({d}_{p}\) :

Diameter of abrasive particles (mm)

\({E}_{c}\) :

Modulus of elasticity—composite (MPa)

\({E}_{m}\) :

Modulus of elasticity—matrix (MPa)

\({E}_{p}\) :

Modulus of elasticity—particles (MPa)

H c :

Hardness of the workpiece (MPa)

\({k}_{t}\) :

Momentum transfer coefficient

\({k}_{d}\) :

Coefficient of discharge

lm :

Mixing tube length (mm)

L:

Slant length (mm)

\({m}_{a}\) :

Mass flow rate of abrasive (g/min)

\({m}_{s}\) :

Mass flow rate of slurry (g/min)

\({m}_{w}\) :

Mass flow rate of water (g/min)

ms :

Mesh size of abrasives (#)

p :

Water pressure (MPa)

R a :

Surface roughness (µm)

u :

Traverse speed (mm/min)

\({S}_{d}\) :

Standoff distance (mm)

s:

Particle ratio

\({v}_{a}\) :

Velocity of abrasive particles (mm/min)

\({v}_{w}\) :

Velocity of waterjet (mm/min)

\({V}_{p}\) :

Volume content of reinforced particles (mm3)

\({V}_{m}\) :

Volume content of matrix (mm3

\({\sigma }_{c}\) :

Compressive strength (MPa)

\({\sigma }_{f}\) :

Dynamic flow stress (MPa)

\({\rho }_{w}\) :

Density of water (g/mm3)

\({\rho }_{p}\) :

Density of the abrasive particles (g/mm3)

θ:

Jet im**ement angle ( ͦ)

\({x}_{1}\),\({x}_{2}\),\({x}_{3}\),\({x}_{4}\), \({x}_{5}\) \({x}_{6}, { x}_{7 }, { x}_{8}\) \(\mathrm{and}\) \({x}_{9}\) :

Exponential constants

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Funding

This work is financially supported under Special Assistance Programme (SAP) by the University Grants Commission (UGC), New Delhi, India (UGC Ref. No. F.3–41/2012 (SAPII)) to the Department of Manufacturing Engineering, Anna University, Chennai.

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Authors and Affiliations

  1. Department of Manufacturing Engineering, College of Engineering Guindy, Anna University, Chennai, 600 025, India

    Mohankumar Veeraraghavan, Kanthababu Mani & Velayudham Arumugam

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  2. Kanthababu Mani
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Contributions

Mohankumar Veeraraghavan – carried out experimental work, surface roughness measurement, calculated the material properties, developed semi-empirical as well as regression models and prepared the manuscript. Kanthababu Mani – obtained a grant for the machining setup and installed the machine in the department for research work and meticulously planned the experimentation of this work and carried out statistical analysis (ANOVA) from the experimental data and compared them with semi-empirical and regression models. He also modified the structure of the article and carried out in-depth analysis of the data. Velayudham Arumugam – studied and analyzed the behavior of AWJM of MMC with respect to the machining aspects and material removal mechanism in terms of surface characterization using the images from SEM and experimental data. Overall, all the authors have contributed equally to the outcome of this work.

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Correspondence to Kanthababu Mani.

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Veeraraghavan, M., Mani, K. & Arumugam, V. Prediction of surface roughness using semi-empirical and regression models in machining of metal matrix composites using abrasive waterjet. Int J Adv Manuf Technol 119, 1623–1645 (2022). https://doi.org/10.1007/s00170-021-08150-8

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