# ML-Based Materials Evaluation in 3D Printing
## 1 Introduction
Currently observed research gaps in the evaluation of ML-based materials for 3D printing are as follows:  
* The number of high-quality, diverse datasets for training ML models is limited, which makes it difficult to develop robust predictive models [14; 15];
* 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;Although real-time ML frameworks exist, they often lack the precision or speed needed for dynamic adjustments in complex printing processes [19; 20; 21];
* 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;
* The cost of implementing ML solutions, including hardware and expertise, remains prohibitive for smaller organizations and research labs, even with the advent of cloud solutions [1; 5; 22; 23]. These gaps underscore the need for further research and innovation to fully leverage the potential of ML in 3D printing materials evaluation. It is important to note that it is necessary to accelerate research into 3D printing materials and their targeted dynamization. New materials (with improved and specialized properties, e.g., antibacterial properties, etc.) are revolutionizing 3D printing, expanding the range of possible properties and applications beyond traditional plastics, metals, and ceramics. Materials such as carbon fiber composites increase mechanical strength and reduce weight, making them invaluable in the aerospace, automotive, and high-performance sports equipment industries [24; 25]. Conductive polymers and graphene-based materials enable the creation of electronic components, flexible circuits, and 3D-printed smart devices (e.g., clothing items) directly through 3D printing. Biocompatible materials such as hydrogels and medical-grade polymers (according to the Medical Devices Regulation) are facilitating advances in bioprinting for the creation of custom implants, tissue engineering, and drug delivery systems. Ceramic and glass materials open up applications in electronics, optics, and high-temperature environments where conventional plastics would fail. Shape-memory alloys and polymers offer capabilities such as self-healing, supporting applications in robotics, medical devices, and responsive structures. Sustainable materials, including bio-based plastics and recycled composites, address environmental concerns, making 3D printing more environmentally friendly and attractive for large-scale implementation [26]. Multi-material printing enables the integration of different materials within a single structure, enabling the creation of complex, multifunctional objects such as sensors with embedded electronics. Advanced material formulations tuned for high precision or specific thermal, chemical, or electrical properties are key to industries such as semiconductors, energy, and aerospace [27; 28; 29].