Analysis of Surface Roughness and Machine Learning-Based Modeling in Dry Turning of Super Duplex Stainless Steel Using Textured Tools

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@Article{pawanr:2025:Technologies,

• author = "Shailendra Pawanr and Kapil Gupta",
• title = "Analysis of Surface Roughness and Machine Learning-Based Modeling in Dry Turning of Super Duplex Stainless Steel Using Textured Tools",
• journal = "Technologies",
• year = "2025",
• volume = "13",
• number = "6",
• pages = "Article No. 243",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2227-7080",
• URL = "https://www.mdpi.com/2227-7080/13/6/243",
• DOI = "doi:10.3390/technologies13060243",
• abstract = "One of the most critical aspects of turning, and machining in general, is the surface roughness of the finished product, which directly influences the performance, functionality, and longevity of machined components. The accurate prediction of surface roughness is vital for enhancing component quality and machining efficiency. This study presents a machine learning-driven framework for modelling mean roughness depth (Rz) during the dry machining of super duplex stainless steel (SDSS 2507). SDSS 2507 is known for its exceptional mechanical strength and corrosion resistance, but it poses significant challenges in machinability. To address this, this study employs flank-face textured cutting tools to enhance machining performance. Experiments were designed using the L27 orthogonal array with three continuous factors, cutting speed, feed rate, and depth of cut, and one categorical factor, tool texture type (dimple, groove, and wave), along with surface roughness as an output parameter. Gaussian Data Augmentation (GDA) was employed to enrich data variability and strengthen model generalisation, resulting in the improved predictive performance of the machine learning models. MATLAB R2021a was employed for preprocessing, the normalization of datasets, and model development. Two models, Least-Squares Support Vector Machine (LSSVM) and Multi-Gene Genetic Programming (MGGP), were trained and evaluated on various statistical metrics. The results showed that both LSSVM and MGGP models learnt well from the training data and accurately predicted Rz on the testing data, demonstrating their reliability and strong performance. Of the two models, LSSVM demonstrated superior performance, achieving a training accuracy of 98.14percent, a coefficient of determination (R2) of 0.9959, and a root mean squared error (RMSE) of 0.1528. It also maintained strong generalisation on the testing data, with 94.36percent accuracy and 0.9391 R2 and 0.6730 RMSE values. The high predictive accuracy of the LSSVM model highlights its potential for identifying optimal machining parameters and integrating into intelligent process control systems to enhance surface quality and efficiency in the complex machining of materials like SDSS.",
• notes = "also known as \cite{technologies13060243}",

}

Genetic Programming entries for Shailendra Pawanr Kapil Gupta

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