Compound casting has received a great deal of attention as a useful method for fabricating a single complicated part from dissimilar metallic materials. For example, in the case of automobile cylinder blocks, research is being carried out to compound cast iron with aluminum alloys. In order to manufacture such as composite parts, it is essential to control the intermetallic compound formed at the interface between two metals. In this research, the type and growth behavior of compounds formed at the interface between cast iron and aluminum, or Al–Si alloy, were investigated. It was observed that the Al₅Fe₂ phase was mainly formed at the interface between the pure aluminum melt and the solid cast iron, and the thickness of the interfacial compound increased proportionally with increasing contact time. On the other hand, more complex phases were formed at the interface between the Al–Si alloy melt and the solid cast iron. In the case of a specimen having a contact time of 10 min, Al_4.5FeSi, Al₈Fe₂Si and Al₅Fe₂ phases appeared to occupy the largest portion of the interfacial compound region. The total thickness of the interfacial compounds also increased in proportion to the contact time.

Polymer materials used in 3D printing exhibit degradation of material mechanical properties when exposed to thermal environments and thermal expansions can induce residual stresses in products or molds, which may result in dimensional instability and subsequent structural failures. In this study, based on linear thermo-viscoelastic principles, material degradation master curves, shift functions, and glass transition temperatures for four different polymers used for 3D printing techniques such as MultiJet Printing and Digital Lighting Process were measured by using a dynamic mechanical analyzer. Based on the single frequency test, the glass transition temperature was measured. In addition, dynamic measurements were carried out over a frequency range at isothermal condition and storage modulus vs. frequency curves were obtained. Then, the storage moduli curves measured at different temperatures were superposed into master curves using the frequency–temperature superposition principle and shift factors were calculated as a function of temperature. Subsequently, the complex moduli curves that were measured in the frequency were curve-fitted onto generalized Maxwell models by using the least squares method and the master curves of relaxation moduli at reference temperature were obtained. The effects of temperature, frequency, and time on dynamic moduli and relaxation behaviors of four polymers used for 3D printing were evaluated. Experimental results showed that Polymers C and D could be suitable to use at the service temperature above 100 ${}^{°}$C and Polymer C was highly crosslinked and showed low modulus reduction after about a year. The master relaxation curves obtained through this process can be utilized to predict the long-term performance of polymer molds made by 3D printing at a given environmental condition.

Fused deposition modeling (FDM), as one of the additive manufacturing processes, is known for strong layer adhesion suitable for prototypes and end-use items. This study used a multiple regression model and statistical analysis to explore the dimensional accuracy of FDM objects. Factors such as inclination angle, layer thickness, support space, and raster angle were examined. Machine learning models (Gaussian process regression (GPR), support vector machines (SVM), and artificial neural network (ANN)) predicted dimensions using 81 datapoints. The mean squared dimensional error (MSDE) between the measured and designed surface profiles was selected as an output for the dimensional accuracy. Support spacing, layer thickness, and raster angle were determined to be statistically significant, and all factors were confirmed as significant predictors. The coefficients of determination for multiple linear regression, GPR, SVM, and ANN models were 76%, 98%, 93%, and 99%, respectively. The mean absolute errors (MAEs)—errors between the measured and the predicted MSDEs—were 0.020 mm and 0.034 mm, respectively, for GPR and SVM models. The MAEs for ANN models were 0.0055 mm for supporting cases and 2.1468 × 10⁻⁵ mm for non-supporting cases.