Hybrid quantum particle swarm optimization and variable neighborhood search for flexible job-shop scheduling problem

Journal of Manufacturing Systems, Год журнала: 2024, Номер 73, С. 334 - 348

Опубликована: Фев. 26, 2024

Язык: Английский

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Productivity prediction of a spherical distiller using a machine learning model and triangulation topology aggregation optimizer

Desalination, Год журнала: 2024, Номер 585, С. 117744 - 117744

Опубликована: Май 18, 2024

Язык: Английский

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Review of Machine Learning applications in Additive Manufacturing

Results in Engineering, Год журнала: 2024, Номер 25, С. 103676 - 103676

Опубликована: Дек. 8, 2024

Язык: Английский

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Artificial neural networks based computational and experimental evaluation of thermal and drying performance of partially covered PVT solar dryer

Process Safety and Environmental Protection, Год журнала: 2024, Номер 183, С. 1170 - 1185

Опубликована: Янв. 23, 2024

Язык: Английский

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Enhancing performance of multi-pressure evaporation organic Rankine Cycle/Supercritical Carbon Dioxide Brayton cycle through genetic algorithm and Machine learning optimization

Energy Conversion and Management, Год журнала: 2024, Номер 301, С. 118037 - 118037

Опубликована: Янв. 4, 2024

Язык: Английский

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A review on the grinding of SiC-based ceramic matrix composites reinforced by continuous fibre: Damage mechanisms and evaluations

Journal of Manufacturing Processes, Год журнала: 2024, Номер 132, С. 261 - 295

Опубликована: Ноя. 5, 2024

Язык: Английский

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Experimental investigation and machine learning modeling using LSTM and special relativity search of friction stir processed AA2024/Al2O3 nanocomposites

Journal of Materials Research and Technology, Год журнала: 2023, Номер 27, С. 7442 - 7456

Опубликована: Ноя. 1, 2023

In this study, the friction stir technique is proposed to process aluminum nanocomposites reinforced with alumina nanoparticles. The effects of different processing parameters, including spindle speed (900–1800 rpm), feed (10–20 mm/min), and number passes (1–3) on mechanical dynamic properties processed samples were investigated. investigated ultimate tensile strength, yield natural frequency, damping ratio. An advanced machine learning approach composed a long short-term memory model optimized by special relativity search algorithm was developed predict conditions. adequacy tested compared three other models; predicted in good agreement measured properties. outperformed models found be powerful prediction tool for predicting conditions obtain high-quality nanocomposite samples. succeeded ratio R2 0.912, 0.952, 0.951, 0.987, respectively. obtained results showed that samples' loss factor increase passes, while shear modulus, complex modulus decrease passes. Thus, can used improve materials.

Язык: Английский

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A gazelle optimization expedition for key term separated fractional nonlinear systems with application to electrically stimulated muscle modeling

Chaos Solitons & Fractals, Год журнала: 2024, Номер 185, С. 115111 - 115111

Опубликована: Июнь 15, 2024

Язык: Английский

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Impact of 3D printing parameters of short carbon fiber reinforced polymer CFRP on the mechanical and failure performance: Review and future perspective

Next Materials, Год журнала: 2025, Номер 8, С. 100645 - 100645

Опубликована: Апрель 18, 2025

Язык: Английский

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Influence of Fibre Orientation on the Slotting Quality of CFRP Composites Using the Multi-Tooth Mill

Materials, Год журнала: 2024, Номер 17(10), С. 2441 - 2441

Опубликована: Май 18, 2024

Carbon fibre-reinforced plastic (CFRP) composites, prized for their exceptional properties, often encounter surface quality issues during slotting due to inherent heterogeneity. This paper tackles CFRP challenges by employing multi-tooth mills in experiments with various fibre orientations and tool feed rates. In-plane scratching tests are performed under linearly varying loads; then, conducted at different parameters. The test results indicate that the orientation cutting angles have significant influences on forces fracture process. demonstrate roughness

Язык: Английский

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