Akademik Veri Yönetim Sistemi
3D Printing and Additive Manufacturing, 2025 (SCI-Expanded)
This study aims to examine the differences between parametric modeling (Pm) and automatic surface modeling (ASm) methods in terms of dimensional accuracy and precision in designs. A component with amorphous, cylindrical, and flat surfaces was chosen as the sample. Fused Deposition Method (FDM) and Polyjet methods were used in 3D printers using the nominal data of this component. A 3D scanner was then used to scan the parts in question. These scans were remodeled with two different modeling methods, Pm and ASm. After modeling, the measured scan data were compared with the nominal data. In addition, the results of the study were optimized with the help of deep learning (DL) and extreme learning machines (ELM). The performance of the model created in DL and ELM was quite close to the actual performance. This result shows the validity of the optimization study. DL r2 value was determined as 0.9774 (%97,74). The most effective results with DL optimization were obtained by using Adam as the optimization algorithm, ReLU as the activation function, 2 hidden layers, 20 neurons, and 20% of the data for testing. As a result, the lowest mean squared error (MSE) of 1.011 × 10−6 was obtained in DL. The results can serve as a guide for the use of DL in new projects. However, when these results were optimized with ELM, the MSE value was 1.011 × 10−6 and the r2 value was 0.9748 (%97,48). To compare the optimization results, they were also optimized with ANN and linear regression. In linear regression, MSE value was calculated as 1.484 × 10−6 and r2 value as 0.9677 (%96,77). This shows that ELM gave very successful prediction results as the most effective optimization. The points examined in the study were obtained from three different surface structures, including plane, cylindrical, and amorphous surfaces. In the results of this study, it will be extremely important and easy to determine the modeling method according to the type of surface to be studied, depending on the data.