# ML-Based Materials Evaluation in 3D Printing
## 3 Results
### Detailed Results of Review  
In light of the review, current research on the use of ML in the area of material selection and adapting 3D printing parameters to the selected material is fruitful. Already, factors influencing the number and types of defects in 3D printing (unexpected material flow, machine setting errors) are analyzed using anomaly detection systems based on, for example, image recognition by a DL model with a variational autoencoder. On this basis, the autocalibration algorithm corrects defects. It is also possible to use a set of simulated parameters that maximize printing speed while minimizing deflection-related failures throughout the 3D printing process in terms of material properties and geometry, ensuring high process efficiency [32]. In the case of multi-material 3D printing, the 3D printing of active electronics  
Figure 6: ML model applications in materials evaluation in 3D printing. (photodetectors, light-emitting diodes) has been shown, but this process is sensitive to printing parameters, ink concentration, and composition, and environmental disturbances. This is due to the fact that the assembly process is governed by multi-step interactions between solutes, solvents, and the microenvironment. Any instabilities can cause deviations from the target printing patterns. The integration of a multi-scale microfluidics-driven 3D printer with an ML algorithm can classify and optimize complex sets of internal features (explore the multidimensional parameter space) by modulating ink composition [33]. In the case of inkjet 3D printing, this challenge has been the ability to continuously jet different materials from print heads. Active ML is used to construct detailed jetting diagrams that identify complex regions rather than just individual jet points. The method requires more than 200 experiments for each fluid, but still reduces the number of experiments by 80%, achieving an accuracy of more than 95% in predicting useful jet zones for the tested fluids. This will accelerate the development of new inks and jet heads for 3D printing [34]. Recent years have shown that the availability of polymer materials for 3D printing is limited, and the experimental determination of resin compositions is time-consuming and expensive. For the above reasons, an active ML approach was used to select the composition in the space of ternary monomers with specific parameters (from rigid to elastomeric, based on, among others, Young's modulus, peak stress, ultimate strain, or Shore hardness) in successive rounds of tests. This allows the selection of the composition of materials within, for example, 10% of the target value of [PERSON]'s modulus. This allows for better adjustment of the properties of the 3D-printed product to the user's requirements, accelerates material discovery, and limits the space of monomers to those currently available [35]. The 3D printing filaments and polymer composites (e.g., acrylonitrile-butadiene-styrene--ABS) are increasingly combined with electrically conductive carbon nanostructures. Here, ML models can be used to predict tensile strength and resistance values in real time. Gaussian Process Regression and Support Vector Machine models confirmed the linear dependence of electrical resistance on the length of ABS products and the influence of the method on producing strength values [36]. The widespread use of 3D printing technology is increasing the need for non-destructive inspection methods that can assess and quantify build quality with high certainty. To this end, a solution is proposed for automatic defect recognition in 3D-printed parts using X-ray computed tomography scans and ML, assuming no a priori information about defect appearance, size, and/or shape. Processing times of several minutes and low false positive rates are achieved [37].