# Development of Prediction Method for Dimensional Stability of 3D-Printed Objects
From the results, the contour width is the important factor affecting profile error and aperture area. [PERSON] and [PERSON] [19] studied the impact of geometric forms along with process parameters on part accuracy for the FDM process. A general factorial design, with simple geometric shapes, including thick-wall and thin-wall features, was developed, and three response variables (perpendicularity, cylindricity, and flatness) were measured to investigate accuracy. The effects of process parameters, such as the position of the part in the work envelope, layer thickness, and orientation of the test models, were investigated. They proposed that the geometry and feature interfaces affected the accuracy more than the process parameter. [PERSON] et al. [20] investigated the effect of the nozzle temperature and feed rates on the dimensions of an FDM part made of ABS and PLA experimentally and numerically. An increase in the feed rate resulted in a reduction in strip width. Furthermore, when evaluating the effect of nozzle temperature and feed rate on strip width, it was found that the measurement positions had a more significant influence. The numerical model accurately predicted the experimental data, exhibiting a high level of agreement. However, some disparities were noted at elevated feed rates and nozzle temperatures. [PERSON] et al. [21] investigated the dimensional accuracy and surface characteristics of different areas in 3D-printed dental casts made with various 3D printing methods. From the superimposing analysis, it was observed that FDM exhibited more systematic deviations compared to DLP,PolyJet, and SLA techniques. The three-dimensional deviations at each cylinder location were found to be the lowest in the left canine region, and deviations increased as the distance from this specific site increased in all the studied groups. Machine learning technology has experienced notable progress in recent years, primarily attributed to the availability of extensive datasets, enhanced computational capabilities, and refined algorithms. Machine learning offers several advantages such as its proficiency in handling voluminous and intricate datasets, its capability to automate decision-making processes, its ability to identify intricate patterns or anomalies that may pose difficulties for humans, and its potential to enhance predictive accuracy and generate valuable insights over time [22; 23]. Consequently, machine learning has the potential to augment efficiency, precision, and innovation within numerous industries. Nonetheless, the field also presents challenges concerning the quality of data, potential biases within datasets or algorithms, the interpretability of complex models, and ethical considerations [24; 25]. Various prediction and optimization studies related to a machine learning model have been released. [PERSON] et al. [26] predicted and optimized the shear strength and curing time of non-conductive adhesives (NCAs) using an artificial neural network (ANN) model. The model accuracy was improved by 28.9-35.2% compared to other studies. They proposed the optimized NCA formulation as the range of 0.2137 \(\times\) (resin content in phr) + (catalyst content in phr) \(\geq\) 35.87 for mass production requirements. [PERSON] et al. [27] studied the statistical analysis of the dimensional accuracy in the FDM process using parameters such as layer thickness, part orientation, raster angle, air gap, and raster width. To optimize the percentage change in length, width, and thickness simultaneously, the grey Taguchi method is employed to determine the optimal levels of process parameters. In addition, they predicted overall dimensional accuracy using the ANN model. The errors between predicted data and measured data were 0-3.5%. [PERSON] et al. [28] investigated the effects of FDM fabrication conditions on dimensional accuracy. An ANN model was employed to predict and optimize the effects of six operating parameters including slice thickness, raster air gap, deposition angle, print direction, width, and perimeters.