Neuro-Fuzzy Model Evaluation for Enhanced Prediction of Mechanical Properties in AM Specimens DOI Creative Commons

Emmanouil-Marinos Mantalas,

Vasileios D. Sagias, Paraskevi Zacharia

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 15(1), P. 7 - 7

Published: Dec. 24, 2024

This paper explores the integration of adaptive neuro-fuzzy inference systems (ANFIS) with additive manufacturing (AM) to enhance prediction mechanical properties in 3D-printed components. Despite AM’s versatility producing complex geometries, achieving consistent performance remains challenging due various process parameters and anisotropic behavior printed parts. The proposed approach combines learning capabilities neural networks decision-making strengths fuzzy logic, enabling ANFIS refine printing improve part quality. Experimental data collected from AM processes are used train model, allowing it predict outputs such as stress, strain, Young’s modulus under values. predictive model was assessed root mean square error (RMSE) coefficient determination (R2) evaluation metrics. study initially examined impact key on subsequently compared two partitioning techniques—grid subtractive clustering—to identify most effective configuration. experimental results analysis demonstrated that could dynamically adjust parameters, leading significant improvements accuracy modulus, showcasing its potential address inherent complexities processes.

Language: Английский

Emerging Applications of Machine Learning in 3D Printing DOI Creative Commons
Izabela Rojek, Dariusz Mikołajewski, Marcin Kempiński

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(4), P. 1781 - 1781

Published: Feb. 10, 2025

Three-dimensional (3D) printing techniques already enable the precise deposition of many materials, becoming a promising approach for materials engineering, mechanical or biomedical engineering. Recent advances in 3D scientists and engineers to create models with precisely controlled complex microarchitecture, shapes, surface finishes, including multi-material printing. The incorporation artificial intelligence (AI) at various stages has made it possible reconstruct objects from images (including, example, medical images), select optimize process, monitor lifecycle products. New emerging opportunities are provided by ability machine learning (ML) analyze data sets learn previous (historical) experience predictions dynamically individuate products processes. This includes synergistic capabilities ML development personalized

Language: Английский

Citations

5
Crashworthy Performance of Sustainable Filled Structures Using Recycled Beverage Cans and Eco-Friendly Multi-Cell Fillers DOI Open Access

Huijing Gao,

Jiangyang Xiang,

Junyu Lu

et al.

Polymers, Journal Year: 2025, Volume and Issue: 17(3), P. 315 - 315

Published: Jan. 24, 2025

The recycling of resources is an important measure to achieve circular economy and sustainable development. In this paper, a filled structure was proposed realized by combining recycled empty beverage cans with eco-friendly multi-cell fillers. Quasi-static axial compressions were carried out characterize the energy absorption performance synergistic effect tubes. Experimental results showed that crashworthiness structures varied both filling densities materials. With increase in density, specific tubes presented upward trend. variation materials, exhibited different performances. PLA tube could withstand larger external force higher SEA values, maximum value 9.64 J/g. PLAS excellent loading stability lower ULC value, minimum 10%. These findings provided valuable insights for designing novel structures.

Language: Английский

Citations

0
Recycling Post-Consumed Polylactic Acid Waste Through Three-Dimensional Printing: Technical vs. Resource Efficiency Benefits DOI Open Access
Md. Raquibul Hasan, Ian J. Davies, Alokesh Pramanik

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(6), P. 2484 - 2484

Published: March 12, 2025

The linear “take–make–dispose” model of plastic consumption has led to significant environmental challenges and unplanned waste legacies, emphasising the need for more sustainable recycling practices. This study explored integration post-consumer recycled polylactic acid (rPLA) into 3D printing filaments as a step towards manufacturing. Using 100% virgin PLA (vPLA) baseline, were produced with rPLA-to-vPLA ratios 0%, 25%, 50%, 75%, evaluated surface roughness, tensile strength, flexural properties, hardness. results revealed that increasing rPLA content negatively affects mechanical properties quality. Surface roughness increased from 7.06 µm pure vPLA 10.50 rPLA, whilst strengths decreased by 48.4% 49%, respectively, compared vPLA. Hardness also declined, showing 7.5% reduction relative Despite these reductions, blends up 50% retained over 90% performance vPLA, demonstrating viable compromise between sustainability. Morphological analysis highlighted poor interlayer adhesion void formation primary causes degradation in higher blends. challenges, this demonstrated rPLA-vPLA can extend life cycle promote manufacturing By addressing polymer research supports materials printing, contributing circular economy goals recycling, resource efficiency, production outcomes.

Language: Английский

Citations

0
Reviewing Additive Manufacturing Techniques: Material Trends and Weight Optimization Possibilities Through Innovative Printing Patterns DOI Open Access
A. Ramos, Virginia G. Angel, Miriam Siqueiros-Hernández

et al.

Materials, Journal Year: 2025, Volume and Issue: 18(6), P. 1377 - 1377

Published: March 20, 2025

Additive manufacturing is transforming modern industries by enabling the production of lightweight, complex structures while minimizing material waste and energy consumption. This review explores its evolution, covering historical developments, key technologies, emerging trends. It highlights advancements in innovations, including metals, polymers, composites, ceramics, tailored to enhance mechanical properties expand functional applications. Special emphasis given bioinspired designs their contribution enhancing structural efficiency. Additionally, potential these techniques for sustainable industrial scalability discussed. The findings contribute a broader understanding Manufacturing’s impact on design optimization performance, offering insights into future research

Language: Английский

Citations

0
Prediction of Mechanical Properties of Additively Manufactured Parts Using Machine Learning Techniques DOI

M. Arunadevi,

V. N. Vivek Bhandarkar,

R. Keshavamurthy

et al.

Journal of The Institution of Engineers (India) Series D, Journal Year: 2025, Volume and Issue: unknown

Published: June 2, 2025

Language: Английский

Citations

0
Neuro-Fuzzy Model Evaluation for Enhanced Prediction of Mechanical Properties in AM Specimens DOI Creative Commons

Emmanouil-Marinos Mantalas,

Vasileios D. Sagias, Paraskevi Zacharia

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 15(1), P. 7 - 7

Published: Dec. 24, 2024

This paper explores the integration of adaptive neuro-fuzzy inference systems (ANFIS) with additive manufacturing (AM) to enhance prediction mechanical properties in 3D-printed components. Despite AM’s versatility producing complex geometries, achieving consistent performance remains challenging due various process parameters and anisotropic behavior printed parts. The proposed approach combines learning capabilities neural networks decision-making strengths fuzzy logic, enabling ANFIS refine printing improve part quality. Experimental data collected from AM processes are used train model, allowing it predict outputs such as stress, strain, Young’s modulus under values. predictive model was assessed root mean square error (RMSE) coefficient determination (R2) evaluation metrics. study initially examined impact key on subsequently compared two partitioning techniques—grid subtractive clustering—to identify most effective configuration. experimental results analysis demonstrated that could dynamically adjust parameters, leading significant improvements accuracy modulus, showcasing its potential address inherent complexities processes.

Language: Английский

Citations

2