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Prediction of surface roughness using machine learning approach for abrasive waterjet milling of alumina ceramic

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Abstract

The present work embodies the effect of abrasive waterjet milling process parameters on surface roughness in alumina ceramic material that is modelled with a machine learning approach. Experiments are carried out on the basis of response surface methodology (RSM) involving the Box–Behnken approach. The individual and interactive effects of the abrasive waterjet milling process parameters on surface roughness are studied through analysis of variance, and a quadratic regression model is developed. The combinations of abrasive waterjet milling input process parameters such as the pressure of 200 MPa, the step over of 0.2 mm, the abrasive flow rate of 0.42 kg/min and the traverse rate of 1000 mm/min have resulted in minimum surface roughness. In addition, the \(\varepsilon\)-support vector regression model of machine learning is developed to predict the surface roughness. To enhance the support vector regression model, its hyperparameters are tuned using grid search with fivefold cross-validation. The tuned hyperparameters are found to have the cost function \((C)\) of 5, \(\varepsilon\)-insensitive loss function of 0.0001, width of the radial basis function \((\gamma )\) of scale and radial basis kernel function. The support vector regression model (92.4%) has outperformed the quadratic regression model (70%) in the prediction of surface roughness.

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Data availability

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

Abbreviations

AWJM:

Abrasive waterjet machining

AWJ milling:

Abrasive waterjet milling

ANOVA:

Analysis of variance

P:

Pressure

SO:

Step over

AFR:

Abrasive flow rate

SOD:

Stand of distance

TR:

Traverse rate

Ra :

Surface roughness

RSM:

Response surface methodology

RBF:

Radial basis function

R2 :

Coefficient of determination

SVM:

Support vector machine

RMSE:

Root mean square error

SVC:

Support vector classification

\(C\) :

Penalty factor or cost function

\(\varepsilon\) :

Epsilon-insensitive loss function

SVR:

Support vector regression

\(K\langle {x}_{i},{x}_{j}\rangle\) :

Kernel functions

\(d\) :

Degree of polynomial

\(a\) :

Scaling parameter of the input data in sigmoid kernel function

\(r\) :

Shifting parameter in sigmoid kernel function

\(\gamma\) :

Width of the radial basis kernel function

\(w\) :

Weight vector

\(b\) :

Bias

\(x\) :

Input vector feature

N \(N\) :

Multidimensional input vector

\({\xi }_{i}, {\xi }_{i}^{*}\) :

Slack variables

\({\alpha }_{i},{\alpha }_{i}^{*}\) :

Lagrange multiplier

\({x}_{r},{x}_{s}\) :

Support vectors

OFd DOF:

Degree of freedom

\(t\) :

Number of training samples

\(n\) :

Number of training samples which are outside of the hypertube

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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, 600025, India

    Prabhu Ramesh & Kanthababu Mani

Authors
  1. Prabhu Ramesh
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  2. Kanthababu Mani
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Contributions

1. Prabhu Ramesh carried out experimental work and surface roughness measurement, developed support vector regression model and contributed in writing this article.

2. Kanthababu Mani obtained a grant for the machining setup and installed the machine in the department for research work and meticulously planned the experimentation and carried out statistical analysis from the experimental data. He also has modified the structure of this article and carried out in-depth analysis of the data.

Overall, both the authors have contributed equally to the outcome of this work.

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Ramesh, P., Mani, K. Prediction of surface roughness using machine learning approach for abrasive waterjet milling of alumina ceramic. Int J Adv Manuf Technol 119, 503–516 (2022). https://doi.org/10.1007/s00170-021-08052-9

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  • DOI: https://doi.org/10.1007/s00170-021-08052-9

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