Data-Driven and AI-Assisted Manufacturing of 3D-Printed Polymer and Composite Components

Dear Colleagues,

The rapid development of additive manufacturing technologies, together with advances in artificial intelligence (AI), machine learning, data analytics, and digital manufacturing systems, is transforming the production of polymer and composite components. In particular, fused deposition modeling (FDM/FFF) and related polymer-based additive manufacturing technologies are being increasingly integrated with intelligent computational and data-driven approaches, enabling improved process control, predictive quality assessment, adaptive manufacturing strategies, and enhanced product performance. These developments represent an important step toward the realization of smart and sustainable manufacturing systems aligned with the principles of Industry 4.0 and Industry 5.0.

Modern additive manufacturing systems generate a large amount of process and material data from sensors, monitoring systems, machine parameters, simulations, and experimental measurements. The integration of these data with AI-assisted methodologies, such as machine learning, deep learning, computer vision, and predictive analytics, enables the development of intelligent manufacturing frameworks capable of improving mechanical performance, production efficiency, process reliability, and sustainability.

This Special Issue will particularly welcome contributions that address recent advances in the integration of artificial intelligence, machine learning, and digital technologies in additive manufacturing processes, with a focus on improving mechanical properties, process optimization, energy efficiency, and sustainable manufacturing strategies. Contributions related to structure–property relationships, in situ monitoring systems, sensor integration, and digital twin approaches for modern manufacturing environments are especially encouraged.

Special attention will be devoted to understanding the relationship between material structure and component performance in 3D-printed polymer and composite systems. Parameters such as infill density, infill geometry, build orientation, layer thickness, raster angle, material composition, and thermal history significantly influence the mechanical, thermal, and microstructural properties of manufactured components. Data-driven and AI-assisted approaches offer new possibilities for predicting and optimizing these relationships, enabling the design of advanced lightweight and high-performance structures for industrial applications.

Another important focus of this Special Issue is the development of intelligent in situ monitoring and adaptive control systems capable of tracking manufacturing conditions in real time using sensors, thermal imaging, machine vision, and other advanced measurement technologies. These systems enable the detection of process anomalies, defect prediction, automated parameter adjustment, and improved manufacturing consistency.

The Special Issue will also emphasize the role of digital twins and cyber–physical manufacturing systems in additive manufacturing environments. Digital twins provide virtual representations of physical manufacturing processes by integrating simulation models, experimental data, and real-time production information. Such approaches support predictive maintenance, process optimization, product quality improvement, reduced development costs, and more efficient decision-making throughout the manufacturing lifecycle.

This Special Issue invites researchers, engineers, and industry professionals to submit original research papers, review articles, and short communications related, but not limited to, the following topics:

• Artificial intelligence and machine learning in additive manufacturing;
• Data-driven optimization of 3D-printing processes for polymers and composites;
• Predictive modeling of mechanical, thermal, and microstructural properties;
• Structure–property relationships in 3D-printed polymer and composite materials;
• In situ monitoring, machine vision, and sensor-based process control;
• Digital twins and cyber–physical systems for additive manufacturing;
• Simulation and numerical modeling of additive manufacturing processes;
• Smart manufacturing systems and intelligent automation;
• Sustainable and energy-efficient additive manufacturing technologies;
• Human–machine collaboration in advanced manufacturing environments;
• Experimental validation and industrial applications of AI-assisted manufacturing systems;
• Emerging trends and future directions in intelligent additive manufacturing.

With this Special Issue, we aim to provide a multidisciplinary platform for the exchange of innovative ideas, advanced methodologies, and practical industrial solutions that contribute to the advancement of intelligent additive manufacturing technologies. By combining materials science, manufacturing engineering, artificial intelligence, and digital technologies, this Special Issue seeks to support the development of more adaptive, efficient, sustainable, and intelligent manufacturing systems for future industrial applications.

We look forward to receiving your contributions.

Dr. Elvis Hozdić
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• additive manufacturing
• 3D printing
• artificial intelligence
• data-driven optimization
• polymer composites
• digital twin