Sustainable green cutting fluid for interpreting optimization of process variables while machining on various CNC manufacturing systems—an experimental approach for exploring

The International Journal of Advanced Manufacturing Technology, Год журнала: 2024, Номер unknown

Опубликована: Июль 8, 2024

Язык: Английский

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The effects of minimum quantity lubrication parameters on the lubrication efficiency in the turning of plastic mold steel

The International Journal of Advanced Manufacturing Technology, Год журнала: 2024, Номер 132(11-12), С. 5803 - 5821

Опубликована: Май 7, 2024

Язык: Английский

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Machine learning models and machinability analysis for comparison of various cooling and lubricating mediums during milling of Hardox 400 steel

Tribology International, Год журнала: 2024, Номер 198, С. 109860 - 109860

Опубликована: Июнь 13, 2024

Язык: Английский

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Bibliometric analysis and research trends in minimum quantity lubrication for reducing cutting forces

The International Journal of Advanced Manufacturing Technology, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 21, 2024

Язык: Английский

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Sustainability assessment and optimization for milling of compacted graphite iron using hybrid nanofluid assisted minimum quantity lubrication method

Sustainable materials and technologies, Год журнала: 2023, Номер 38, С. e00756 - e00756

Опубликована: Окт. 28, 2023

Язык: Английский

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Mathematical Modeling Using ANN Based on k-fold Cross Validation Approach and MOAHA Multi-Objective Optimization Algorithm During Turning of Polyoxymethylene POM-C

Jordan Journal of Mechanical and Industrial Engineering, Год журнала: 2024, Номер 18(01), С. 179 - 190

Опубликована: Фев. 29, 2024

The paper has a dual purpose: firstly, to examine the influence of various cutting conditions (cutting speed , feed depth cut tool nose radius ɛ and edge angle ) on quality machined parts (), tangential force ( power during turning process polyoxymethylene POM-C.Two carbide inserts, SPMR 120304 120308, were used for three-dimensional operations.Secondly, goal is identify optimal that maximize material removal rate () while minimizing three output parameters (, ).The study employed analysis variance (ANOVA) assess significance input desired outcomes utilized an artificial neural network (ANN) create mathematical models.The K-fold Cross-Validation approach was deemed suitable due its efficiency in requiring fewer experiments.To optimize conditions, new metaheuristic optimization algorithm called Multi-Objective Artificial Hummingbird Algorithm (MOAHA) selected.ANOVA reveals factors contribute 58.05% 32.25%, respectively, response .Classical ,, also impact mechanical actions MOAHA algorithm, coupled with four ANN models, optimized five resulting values = 250 /, 0.08 1.3 0.8 75°.Under these responses are: 0.6 µ, 21.51, 60.24, 26.38 3 /.The ANN-MOAHA coupling provides excellent, simple, fast computer multi-objective optimization.

Язык: Английский

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Predicting Surface Roughness and Grinding Forces in UNS S34700 Steel Grinding: A Machine Learning and Genetic Algorithm Approach to Coolant Effects

Physchem, Год журнала: 2024, Номер 4(4), С. 495 - 523

Опубликована: Дек. 3, 2024

In today’s tech world of digitalization, engineers are leveraging tools such as artificial intelligence for analyzing data in order to enhance their capability evaluating product quality effectively. This research study adds value by applying algorithms and various machine learning techniques—such support vector regression, Gaussian process neural networks—on a dataset related the grinding UNS S34700 steel. What sets this apart is its consideration factors like three types wheels, four distinct cooling solutions, seven varied depths cut. These parameters assessed impact on surface roughness forces, resulting conversion information into insights. A relational equation with 25 coefficients developed, using optimized predict an 85 percent accuracy forces 90 rate. Learning from models regression exhibited stability, R2 0.98 mean 93 percent. Artificial networks achieved 0.96, rate findings suggest that techniques versatile precise when dealing datasets. They align well digitalization predictive trends. conclusion; provides flexibility superior predicting trends compared formulaic approach, which contained existing datasets only. The versatility highlights significance engineering practices making data-informed decisions.

Язык: Английский

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"Optimising Subsurface Integrity and Surface Quality in Mild Steel Turning: A Multi-Objective Approach to Tool Wear and Machining Parameters"

Journal of Materials Research and Technology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

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Fault Detection in Steel Belts of Tires Using Magnetic Sensors and Different Deep Learning Models

Firat University Journal of Experimental and Computational Engineering, Год журнала: 2025, Номер 4(1), С. 85 - 99

Опубликована: Фев. 18, 2025

Tire failures pose significant safety risks, necessitating advanced inspection techniques. This research investigates the application of magnetic sensors and deep learning for detecting defects in steel belts tires. It was aim to develop a robust accurate fault detection system by measuring field variations caused defects. In this study, image sensor circuit had been designed then images obtained from it have classified as none, crack, delamination type belt errors. Various models their hybrid architectures, were explored compared. Experimental results demonstrate that all exhibit strong performance, with Transformer model achieving highest accuracy 96.12%. The developed offers potential solution improving tire reducing maintenance costs industries.

Язык: Английский

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Machinability assessment and optimization of turning AISI H11 steel under various minimum quantity lubrication (MQL) conditions using nanofluids

The International Journal of Advanced Manufacturing Technology, Год журнала: 2025, Номер unknown

Опубликована: Март 22, 2025

Язык: Английский

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