What does this research mean for the field?

The CatBoost algorithm outperforms Decision Tree and Naïve Bayes models in predicting material removal rate (MRR) during Wire Electrical Discharge Machining of Al7075/Al2O3 composites, although accurate surface roughness prediction remains challenging across all tested models. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This study aims to improve predictions of surface roughness and material removal rate in WEDM of Al7075 composites using machine learning models.

May 31, 2026Discover Mechanical Engineering1 citationsOpen Access

Surface Roughness and MRR Prediction in WEDM of Al7075 Using CatBoost, Decision Tree, and Naïve...

Surface roughness and MRR prediction in WEDM of Al7075 using CAT boost in comparison with decision tree and Naïve Bayes models

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Authors

MAM. Arunadevi AAA. Anilkumar HMH. M. Manjula

Discussion

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Overview

Randomized trial evaluates surface roughness and material removal rate predictions in Al7075 composites, highlighting CatBoost's superior performance.

Key Points

This study aims to improve predictions of surface roughness and material removal rate in WEDM of Al7075 composites using machine learning models.
Experiments designed using Taguchi’s L₁̄8 orthogonal array with varied voltage, current, pulse-on time, pulse-off time, and bed speed.
Predictive models developed using CatBoost, Decision Tree, and Naïve Bayes algorithms.
Fabricated Al7075/Al2O3 composite via stir casting for uniform particle distribution.
CatBoost achieved 0.83 prediction accuracy and 1.0 precision for MRR, outperforming Decision Tree and Naïve Bayes.
Decision Tree models provided satisfactory accuracy, while Naïve Bayes performed least effectively with maximum accuracy of 0.5 for surface roughness.
Surface roughness prediction remains challenging, indicating a need for advanced hybrid modeling approaches.

Cite This Study

Arunadevi et al. (2026) studied this question.

synapsesocial.com/papers/6a1bcf835783ba022b6fb982 https://doi.org/10.1007/s44245-026-00270-3

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May 31, 2026Discover Mechanical Engineering1 citationsOpen Access

Surface Roughness and MRR Prediction in WEDM of Al7075 Using CatBoost, Decision Tree, and Naïve...

Surface roughness and MRR prediction in WEDM of Al7075 using CAT boost in comparison with decision tree and Naïve Bayes models

View Full Paper

Ask AI

Bookmark

Authors

MAM. Arunadevi AAA. Anilkumar HMH. M. Manjula

Discussion

Member takes

Overview

Randomized trial evaluates surface roughness and material removal rate predictions in Al7075 composites, highlighting CatBoost's superior performance.

Key Points

This study aims to improve predictions of surface roughness and material removal rate in WEDM of Al7075 composites using machine learning models.
Experiments designed using Taguchi’s L₁̄8 orthogonal array with varied voltage, current, pulse-on time, pulse-off time, and bed speed.
Predictive models developed using CatBoost, Decision Tree, and Naïve Bayes algorithms.
Fabricated Al7075/Al2O3 composite via stir casting for uniform particle distribution.
CatBoost achieved 0.83 prediction accuracy and 1.0 precision for MRR, outperforming Decision Tree and Naïve Bayes.
Decision Tree models provided satisfactory accuracy, while Naïve Bayes performed least effectively with maximum accuracy of 0.5 for surface roughness.
Surface roughness prediction remains challenging, indicating a need for advanced hybrid modeling approaches.

Cite This Study

Arunadevi et al. (2026) studied this question.

synapsesocial.com/papers/6a1bcf835783ba022b6fb982 https://doi.org/10.1007/s44245-026-00270-3

View Full Paper

Ask AI

Bookmark