AI-Driven Innovations in 3D Printing: Optimization, Automation, and Intelligent Control

By greatly increasing automation, accuracy, and flexibility at every step of the additive manufacturing process, from design and production to quality assurance, artificial intelligence (AI) is revolutionizing the 3D printing industry. The integration of AI algorithms into 3D printing systems enables real-time optimization of print parameters, accurate prediction of material behavior, and early defect detection using computer vision and sensor data. Machine learning (ML) techniques further streamline the design-to-production pipeline by generating complex geometries, automating slicing processes, and enabling adaptive, self-correcting control during printing—functions that align directly with the principles of Industry 4.0/5.0, where cyber-physical integration, autonomous decision-making, and human–machine collaboration drive intelligent manufacturing systems. Along with improving operational effectiveness and product uniformity, this potent combination of AI and 3D printing also propels the creation of intelligent manufacturing systems that are capable of self-learning. This confluence has the potential to completely transform sectors including consumer products, healthcare, construction, and aerospace as it develops. This comprehensive review explores how AI enhances the capabilities of 3D printing, with a focus on process optimization, defect detection, and intelligent control mechanisms. Moreover, unresolved challenges are highlighted—including data scarcity, limited generalizability across printers and materials, certification barriers in safety-critical domains, computational costs, and the need for explainable AI.

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Publication Details

Journal
Journal of Manufacturing and Materials Processing
Published
2025-10-07
DOI
https://doi.org/10.3390/jmmp9100329
Citations
16
Primary Topic
Additive Manufacturing and 3D Printing Technologies
Type
article
Field-Weighted Citation Impact
10.93
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article

AI-Driven Innovations in 3D Printing: Optimization, Automation, and Intelligent Control

Eylem Asmatulu, Abdulhammed K. Hamzat, Ramazan Asmatulu, Abdülcelil BAYAR et al.
16 citations
Journal of Manufacturing and Materials Processing
Additive Manufacturing and 3D Printing Technologies
10.93
article

AI-Driven Innovations in 3D Printing: Optimization, Automation, and Intelligent Control

Eylem Asmatulu, Abdulhammed K. Hamzat, Ramazan Asmatulu, Abdülcelil BAYAR, Zaara Ali, Fatih Altun
article en
16 citations

Abstract

By greatly increasing automation, accuracy, and flexibility at every step of the additive manufacturing process, from design and production to quality assurance, artificial intelligence (AI) is revolutionizing the 3D printing industry. The integration of AI algorithms into 3D printing systems enables real-time optimization of print parameters, accurate prediction of material behavior, and early defect detection using computer vision and sensor data. Machine learning (ML) techniques further streamline the design-to-production pipeline by generating complex geometries, automating slicing processes, and enabling adaptive, self-correcting control during printing—functions that align directly with the principles of Industry 4.0/5.0, where cyber-physical integration, autonomous decision-making, and human–machine collaboration drive intelligent manufacturing systems. Along with improving operational effectiveness and product uniformity, this potent combination of AI and 3D printing also propels the creation of intelligent manufacturing systems that are capable of self-learning. This confluence has the potential to completely transform sectors including consumer products, healthcare, construction, and aerospace as it develops. This comprehensive review explores how AI enhances the capabilities of 3D printing, with a focus on process optimization, defect detection, and intelligent control mechanisms. Moreover, unresolved challenges are highlighted—including data scarcity, limited generalizability across printers and materials, certification barriers in safety-critical domains, computational costs, and the need for explainable AI.

Journal of Manufacturing and Materials ProcessingVol. 9(10)
Wichita State University (US), Turkish Aerospace Industries (Turkey) (TR), Gazi University (TR)
Openalex Percentile: Top 1%
Additive Manufacturing and 3D Printing Technologies
10.93
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