ML-Based Materials Evaluation in 3D Printing
Abstract
:Featured Application
Abstract
1. Introduction
- The lack of standardized protocols for data collection and reporting results in inconsistent datasets that are difficult to integrate or compare;
- Many ML models act as black boxes, making it difficult for researchers to understand and verify their predictions in the context of materials science [16];
- The computational requirements of ML models, especially for large-scale or real-time applications, still constitute a significant barrier to industrial implementation even using cloud technologies—recently, these are increasingly often limitations related to the cost of electricity required for computation [17];
- ML models trained on specific materials, technologies, or printing conditions often fail to generalize to other materials, systems, or environments, which limits their usefulness [18];
- The integration of the knowledge of physics, chemistry, and mechanics specific to a given 3D printing technology and related printing materials with ML algorithms is still underdeveloped, leading to gaps in the accuracy and reliability of predictions;
- Predicting interactions and optimizing parameters for multi-material or composite 3D printing remains a major challenge;
- Limited research has investigated the role of ML in assessing the environmental impact, recyclability, and life cycle of 3D printing materials;
2. Materials and Methods
2.1. Dataset
- RQ1: what is the most common origin of publications (institutions, country, if possible, funding mode)?
- RQ2: who are the most influential authors and what are their articles?
- RQ3: what are the most popular topics, and, if possible, how are the research topics evolving?
- RQ4: what Sustainable Development Goals (SDGs) are related to the publications included in the review?
2.2. Methods
3. Results
3.1. Data Sources
3.2. General Results of Analysis
- The better or alternative use of existing materials, including improving material preparation procedures to improve the quality, efficiency, and sustainability (energy costs, waste, gases) of printing;
- The development of currently used materials (bio-ink, composites, multi-material printing materials);
- The development of completely new materials (e.g., nanomaterials), complementary or alternative to the current ones.
- The modeling of mechanical properties of products, combining the actual microstructure (as input to the network) with descriptors of the structure properties within the network;
- The modeling of geometrical distortions, where a convolutional neural network with an encoder and decoder allows for explicit modeling of geometric distortions of the entire printable shape.
3.3. Detailed Results of Review
4. Discussion
4.1. State-of-the-Art Summary
- ML can rapidly analyze data to discover new materials, reducing development time from years to months;
- By tailoring material properties to specific applications, ML enables highly specialized solutions in various industries such as aerospace and healthcare;
- ML optimizes material usage, minimizing waste and promoting the development of recyclable or bio-based materials;
- Algorithms optimize printing parameters in real time, leading to fewer defects and higher quality results;
- Automated optimization reduces trial-and-error experiments, lowering material and energy costs;
- ML requires vast amounts of high-quality data that can be difficult or expensive to acquire and maintain;
- Poorly trained models can introduce biases or inaccuracies, leading to suboptimal material properties or printing failures;
- As ML integrates with IoT and cloud systems, security flaws can expose proprietary material designs to cyberattacks;
- Models optimized for narrow scenarios may not generalize well to other applications, limiting versatility;
- ML integration requires skilled personnel and advanced infrastructure, which is a challenge for smaller companies;
- The automated development of advanced materials raises concerns about misuse in areas such as weapons or surveillance;
4.2. Limitations
4.3. Directions for Further Studies
- prioritize the development of large, standardized datasets that integrate material properties, printing parameters, and results to enable more robust and transferable models;
- develop physics-based machine learning that combines domain knowledge with data-driven methods to improve accuracy and interpretability;
- improve real-time monitoring and adaptive control using machine learning can enable dynamic tuning of printing parameters to achieve consistent material quality;
- develop multimodal learning that combines imaging, sensor data, and numerical simulations and can help to more holistically assess complex material behaviors;
- focus on interpretable AI to ensure that materials scientists can trust and understand machine learning-based insights;
- develop scalable inverse design framework to automate the discovery of new materials with tailored multifunctional properties;
- integrate sustainability metrics with machine learning assessment can promote eco-friendly materials and reduce waste in 3D printing within all three main stages of ML-modeling: input features, data-driven models, and output (quantity of interests) [96].
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ABS | Acrylonitrile-butadiene-styrene |
AI | Artificial intelligence |
FDM | Fused deposition modeling |
FFF | Fused filament fabrication |
FRE | Free-form reversible deposition |
L-PBF | Laser powder fusion |
ML | Machine learning |
PLA | Polylactid acid |
rPLA | Recycled polylactid acid |
XAI | eXplainable artificial intelligence |
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Stage Name | Tasks |
---|---|
Defining research objectives | Defining goals of the bibliometric analysis |
Selecting data bases and data collections | Choosing appropriate dataset(s) and developing research queries according to the study goals |
Data preprocessing | Cleaning the collected data to remove duplicates and irrelevant records |
Bibliometric software selection | Choosing suitable bibliometric software/tools for analysis |
Data analysis | Description, author, journal, area, topics, institution, country, etc. |
Visualization (if possible) | Visualizing the analysis results to present insights |
Interpretation and discussion | Interpreting findings in the context of the research goals |
Parameter/Feature | Detailed Description |
---|---|
Inclusion criteria | Books (and chapters in books), articles (original, reviews, communication, editorials), and conference proceedings, in English |
Exclusion criteria | Books older than 10 years, letters, conference abstracts without full text, other languages than English |
Keywords used | deep learning, energy optimization/optimization, smart city |
Used field codes (WoS) | “Subject” field (consisting of title, abstract, keyword plus and other keywords) |
Used field codes (Sopus) | article title, abstract and keywords |
Used field codes (PubMed) | manually |
Used field codes (dblp) | manually |
Boolean operators used | Yes, e.g.,“3D print” AND (“optimization” OR “optimization”) AND “machine learning” |
Applied filters | Results refined by publication year, document type (e.g., articles, reviews), and subject area (e.g., industry, engineering). |
Iteration and validation options | Queries are run iteratively, refined based on results, and validated by ensuring that relevant publications appear among the top results |
Leverage truncation and wildcards used | Used symbols like * for word variations (e.g., “3D print*” for “3D print” or “3D printing”) and ? for alternative spellings (e.g., “optimi?ation” for “optimisation” or “optimization”) |
Parameter/Feature | Value |
---|---|
Leading types of publication | Article (58.9%), proceeding paper (25.8%), review (7.3%) (Figure 2) |
Leading areas of science | Engineering manufacturing (33.9%), Materials science (17.4%), Computer science (14.0%) (Figure 3) |
Leading topics | Nanofibers, scaffolds and fabrication, Mechanics |
Leading countries | USA, India, China (Figure 4) |
Leading scientists | Baldwin M., McComb C.; Meisel N.A. |
Leading affiliations | Pennsylvania State University, Carnegie Mellon University, Georgia Institute of Technology |
Leading funders (where information available) | National Science Foundation, United States Department of Defence, United States Department of Energy, European Commission (Figure 5) |
Sustainable development goals | Industry innovation and infrastructure, Good health and well being |
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Rojek, I.; Mikołajewski, D.; Galas, K.; Kopowski, J. ML-Based Materials Evaluation in 3D Printing. Appl. Sci. 2025, 15, 5523. https://doi.org/10.3390/app15105523
Rojek I, Mikołajewski D, Galas K, Kopowski J. ML-Based Materials Evaluation in 3D Printing. Applied Sciences. 2025; 15(10):5523. https://doi.org/10.3390/app15105523
Chicago/Turabian StyleRojek, Izabela, Dariusz Mikołajewski, Krzysztof Galas, and Jakub Kopowski. 2025. "ML-Based Materials Evaluation in 3D Printing" Applied Sciences 15, no. 10: 5523. https://doi.org/10.3390/app15105523
APA StyleRojek, I., Mikołajewski, D., Galas, K., & Kopowski, J. (2025). ML-Based Materials Evaluation in 3D Printing. Applied Sciences, 15(10), 5523. https://doi.org/10.3390/app15105523