Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting Fluid

• Published in: Lubricants Vol. 10; no. 5; p. 81
• Main Authors: Dubey, Vineet, Sharma, Anuj Kumar, Pimenov, Danil Yurievich
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2022
• Subjects: Aluminum oxide; Artificial intelligence; Austenitic stainless steels; cutting fluid; Cutting fluids; Cutting parameters; Cutting speed; Energy consumption; Feed rate; Heat generation; Lubricants & lubrication; lubrication; Machine learning; Machine tools; machining; Mathematical models; nanofluid; Nanoparticles; Neural networks; Particle size; Response surface methodology; Solid lubricants; Stainless steel; Support vector machines; Surface roughness; Sustainability; turning; Turning (machining); Workpieces
• ISSN: 2075-4442; 2075-4442
• DOI: 10.3390/lubricants10050081

Surface roughness is considered as an important measuring parameter in the machining industry that aids in ensuring the quality of the finished product. In turning operations, the tool and workpiece contact develop friction and cause heat generation, which in turn affects the machined surface. The use of cutting fluid in the machining zone helps to minimize the heat generation. In this paper, minimum quantity lubrication is used in turning of AISI 304 steel for determining the surface roughness. The cutting fluid is enriched with alumina nanoparticles of two different average particle sizes of 30 and 40 nm. Among the input parameters chosen for investigation are cutting speed, depth of cut, feed rate, and nanoparticle concentration. The response surface approach is used in the design of the experiment (RSM). For the purpose of estimating the surface roughness and comparing the experimental value to the predicted values, three machine learning-based models, including linear regression (LR), random forest (RF), and support vector machine (SVM), are utilized in addition. For the purpose of evaluating the accuracy of the predicted values, the coefficient of determination (R2), mean absolute percentage error (MAPE), and mean square error (MSE) were all used. Random forest outperformed the other two models in both the particle sizes of 30 and 40 nm, with R-squared of 0.8176 and 0.7231, respectively. Thus, this study provides a novel approach in predicting the surface roughness by varying the particle size in the cutting fluid using machine learning, which can save time and wastage of material and energy.