# ML-Based Materials Evaluation in 3D Printing
## 1 Introduction  
The emergence of machine learning (ML)-based materials evaluation in 3D printing is the result of a combination of advances in 3D printing technologies, materials science, and computer science (AI).Early 3D printing research focused largely on using trial-and-error to optimize material properties and printing parameters. As 3D printing became more sophisticated, researchers recognized the limitations of traditional approaches, particularly in terms of time, cost, and scalability. The rapid development of ML algorithms in the 2010s provided a fast track to addressing these challenges by leveraging insights from historical and current 3D printing processes [1, 2, 3].Initial applications of ML in materials science involved predicting the properties of conventional materials, such as metals and polymers, based on data focused on material composition and how it changed during manufacturing and processing. These successes inspired researchers to apply similar techniques to the emerging field of 3D printing. One of the earliest applications of ML in 3D printing involved optimizing process parameters, such as temperature, speed, and layer height, to improve print quality and material performance [4]. Over time, advances in computing power (including the emergence of cloud technologies) and the availability of larger sets of historical and real-timedata (thanks to the Internet of Things) have enabled the development of more complex ML models tailored to the needs of 3D printing. Scientists have begun to integrate ML with advanced characterization techniques, such as scanning electron microscopy and spectroscopy, to analyze and predict the behavior of materials at the micro- and nano-scale [5]. In the mid-2010s, the emergence of generative models enabled the virtual design of new 3D printing materials with specific properties (mechanical, chemical) [6]. Collaborations between academia and industry have further accelerated the adoption of ML in 3D printing materials analysis, as companies continue to improve product and material development cycles; supply chain issues related to the pandemic and political crises around the world have played a role [7; 8]. Interdisciplinary approaches have begun to emerge, with physicists, chemists, and computer scientists working together to build hybrid ML-physics models to achieve more accurate classifications and predictions [9]. Open-source platforms and initiatives have democratized access to ML tools, encouraging scientists and engineers to experiment and innovate more widely in materials evaluation. The late 2010s also saw the emergence of real-time ML applications in 3D printing, focusing on defect detection and adaptive control during the printing process. Sustainability concerns have also influenced the adoption of ML, as it has proven effective in evaluating recyclable and bio-based materials for 3D printing [10]. ML-assisted materials evaluation in 3D printing allows for the prediction of material properties before printing begins. This allows for the optimization of the production process through better selection of parameters and materials. ML models analyze data from previous prints, allowing for automatic adjustment of settings to obtain the best quality. Such optimization shortens experimentation time, reduces costs, and increases efficiency. As a result, ML becomes a key tool supporting continuous improvement of the 3D printing process. Today, ML-based materials evaluation is a key tool in advancing 3D printing capabilities, combining computational intelligence with manufacturing technologies. As part of predictive maintenance, ML algorithms help predict when 3D printer components are likely to fail or require maintenance, which helps reduce material waste, avoid print quality degradation, reduce downtime, and increase productivity. This evolution reflects the synergy between traditional scientific research and modern data-driven methodologies, setting the stage for further breakthroughs in 3D printing [11; 12; 13].