Cited by Development of machine learning models for material classification and prediction of mechanical properties of FDM 3D printing outputs

A Review of AI for optimization of 3D Printing of Sustainable Polymers and Composites DOI

Malik Hassan, Manjusri Misra, Graham W. Taylor

et al.

Composites Part C Open Access, Journal Year: 2024, Volume and Issue: unknown, P. 100513 - 100513

Published: Sept. 1, 2024

Language: Английский

Citations

Deep learning identifies transversely isotropic material properties using kinematics fields DOI

Nikzad Motamedi, Hazem Wannous, Vincent Magnier

et al.

International Journal of Mechanical Sciences, Journal Year: 2024, Volume and Issue: 283, P. 109672 - 109672

Published: Sept. 4, 2024

Language: Английский

Citations

Experimental Study on Mechanical Performance of Single-Side Bonded Carbon Fibre-Reinforced Plywood for Wood-Based Structures DOI

Krzysztof Szwajka, Joanna Zielińska-Szwajka, Tomasz Trzepieciński

et al.

Materials, Journal Year: 2025, Volume and Issue: 18(1), P. 207 - 207

Published: Jan. 6, 2025

In addition to the traditional uses of plywood, such as furniture and construction, it is also widely used in areas that benefit from its special combination strength lightness, particularly a construction material for production finishing elements campervans yachts. light current need reduce emissions climate-damaging gases CO2, use lightweight materials very important. recent years, hybrid structures made carbon fibre-reinforced plastics (CFRPs) metals have attracted much attention many industries. contrast metal/carbon fibre composites, research relating laminates consisting CFRPs wood-based shows less interest. This article analyses laminate resulting bonding CFRP panel plywood terms performance using three-point bending test, static tensile test dynamic analysis. Knowledge characteristics allows adoption cutting parameters will help prevent occurrence self-excited vibrations process. Therefore, this work, was decided determine effect on both stiffness structure. Most studies field concern improving structure without analysing properties. proposes simple user-friendly methodology determining damping sandwich-type system. The results tests were modulus elasticity, rupture, position neutral axis frequency domain obtained. show CFRP-reinforced not only improves visual aspect but properties material. case panel, value stresses decreased by sixteen-fold (from 1.95 N/mm2 0.12 N/mm2), compressive more than seven-fold 0.27 N/mm2) compared unreinforced plywood. Based stress occurring at sides sample surface during cantilever text, found rupture three-fold elasticity five-fold sample. A analysis allowed us natural increased about 33% 30 Hz 40 Hz) beam

Language: Английский

Citations

Ensemble machine learning for predicting and enhancing tribological performance of Al5083 alloy with HEA reinforcement DOI

S. Kumaravel,

P.M. Suresh

Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 10, 2025

This study investigates the tribological behavior of Al5083 alloy reinforced with AlCoCrFeNiSi high-entropy (HEA) particles using friction stir processing (FSP). Wear characteristics were analyzed pin-on-disc experiments across varying HEA volume percentages, disc speeds, and test durations, revealing significant improvements in wear resistance increasing content. Machine learning techniques, including artificial neural networks (ANN) long short-term memory (LSTM) networks, employed to predict specific rate (SWR) coefficient (COF) high accuracy. The ensemble model combining ANN LSTM architectures achieved R-squared values 0.9653 for SWR 0.9718 COF, a root mean square error (RMSE) 0.024 0.017 COF respectively, indicating robust predictive capabilities. Cross-validation further validated model's effectiveness, achieving an average prediction 2.13% 1.89% COF. Response surface methodology (RSM) optimization refined process parameter relationships, identifying conditions that minimize 3.57 × 10 – 6 mm³/Nm 0.237. Scanning electron microscopy (SEM) analysis worn surfaces confirmed effectiveness reinforcement mitigating mechanisms, enhancing material's durability by 45% compared unreinforced alloy. comprehensive approach advances understanding HEA-reinforced composites. It provides practical insights optimizing material performance industrial applications, contributing developing high-performance materials tailored resistance.

Language: Английский

Citations

Development of machine learning models for material classification and prediction of mechanical properties of FDM 3D printing outputs DOI

Suhyun Kim, Ji-Hye Park, Ji Young Park

et al.

Journal of Mechanical Science and Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

Language: Английский

Citations