A comprehensive review on application of machine intelligence in additive manufacturing DOI Open Access
N. Ethiraj, T. Sivabalan, J. Sofia Vincent

et al.

Turkish Journal of Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 21, 2024

Additive manufacturing (AM), one of the emerging disruptive technologies, is gaining popularity not only in rapid prototyping but also complex shapes and dimensions. Artificial intelligence (AI) exhibited by computer systems to perform tasks such as learning, reasoning, decision making problem solving. Machine learning (ML) a subset artificial which enables AI imitate human process using data algorithms. The concept machine helps advanced computing technologies interact with environment highlights intersection ML. aim this review article provide comprehensive information about application ML various additive processes for different activities order improve performance operation. Also, it describes other Internet Things (IoT), Digital Twins (DT) Block Chain Technology augment producing quality products. Further, explains challenges that are encountered certain areas need be addressed future enhancement product production

Language: Английский

An optimization-centric review on integrating artificial intelligence and digital twin technologies in manufacturing DOI Creative Commons
Vispi Karkaria, Ying-Kuan Tsai, Yi-Ping Chen

et al.

Engineering Optimization, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 47

Published: Jan. 3, 2025

Language: Английский

Citations

4

Integrating 3D printing, Simulations and Surrogate Modelling: A Comprehensive Study on Additive Manufacturing focusing on a metal Twin-Cantilever benchmark DOI Creative Commons
C. Mallor,

Sébastien Lani,

V. Zambrano

et al.

Advances in Industrial and Manufacturing Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 100162 - 100162

Published: April 1, 2025

Language: Английский

Citations

0

Integrating sensors and Machine Learning: A smart monitoring system prototype for quality assurance in additive manufacturing for the aerospace industry DOI
Leonardo Agnusdei, Antonio Ficarella, Antonio Del Prete

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 24, 2025

Abstract Ensuring quality of aerospace components produced via additive manufacturing is challenging because conventional methods are often reactive and insufficient to detect prevent defects in real time, not addressing the complexity precision required. The main aim this study develop an innovative smart monitoring system which integrates electronic nose (e-nose), a thermal camera, convolutional neural networks (CNN) anomalies during production process designed for purposes. adopted methodology involves combining chemical data collected by e-nose be analyzed using CNN. anomaly detection enables real-time corrective actions, optimizing parameters. CNN’s iterative learning capabilities ensure adaptive improved over time. Results demonstrate that integrated multi-sensor approach has potential enhance significantly accuracy, reduce material waste, compliance with standards. prototype originality lies synergistic integration advanced technologies machine AM processes, providing proactive solution defect prevention. Practical implications include increased efficiency, reduced costs, sustainability, as well scalability other high-stakes industries requiring rigorous assurance.

Language: Английский

Citations

0

Additive Manufacturing in Biomedical: Applications, Challenges, and Prospects DOI Creative Commons
Md Hosne Mobarak,

Abu Sofian Abid,

Mohiuddin Munna

et al.

Hybrid Advances, Journal Year: 2025, Volume and Issue: unknown, P. 100467 - 100467

Published: March 1, 2025

Language: Английский

Citations

0

A comprehensive review on application of machine intelligence in additive manufacturing DOI Open Access
N. Ethiraj, T. Sivabalan, J. Sofia Vincent

et al.

Turkish Journal of Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 21, 2024

Additive manufacturing (AM), one of the emerging disruptive technologies, is gaining popularity not only in rapid prototyping but also complex shapes and dimensions. Artificial intelligence (AI) exhibited by computer systems to perform tasks such as learning, reasoning, decision making problem solving. Machine learning (ML) a subset artificial which enables AI imitate human process using data algorithms. The concept machine helps advanced computing technologies interact with environment highlights intersection ML. aim this review article provide comprehensive information about application ML various additive processes for different activities order improve performance operation. Also, it describes other Internet Things (IoT), Digital Twins (DT) Block Chain Technology augment producing quality products. Further, explains challenges that are encountered certain areas need be addressed future enhancement product production

Language: Английский

Citations

1