Binder jetting additive manufacturing of aluminum nitride components

In this work, we report on the novel fabrication of aluminum nitride (AlN) components using Binder Jetting (BJT) additive manufacturing (AM). The AlN constructs were subjected to post-fabrication thermal treatment by hot isostatic pressing (HIPing) for 8 hours at a pressure of 206 MPa and temperature of 1900 °C. This treatment resulted in a 60.1% relative density maximum densification for AlN. The BJT printed AlN specimens were analyzed using various characterization techniques. The purity, microstructure, and polycrystallinity of the AlN phase formed were confirmed by techniques that included x-ray diffraction (XRD), scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS), and high-resolution transmission electron microscopy (HRTEM). Second harmonic generation (SHG) microscopy showed polarization dependence and second harmonic signal at 470 nm, indicating the potential to produce thermal and optical-mechanical devices. Mechanical properties obtained by nanoindentation resulted in an elastic modulus of ~251 GPa when measured in fully dense, contiguous crystalline regions, corresponding to an apparent, porous bulk stiffness of ~90 GPa for the final, 60.1 % dense products. Finally, the laser flash method (LFM) was used to measure the thermal conductivity of the material as a function of temperature resulting in values from 4.82 W/mK to 3.17 W/mK for the temperature range from 23 °C to 500 °C, respectively.     

Interlayer bonding between thermoplastic composites and metals by in-situ polymerization technique

Fiber-metal laminates (FMLs) offer the superior characteristics of polymer composites (i.e., light weight, high strength and stiffness) with the ductility and fracture strength of metals. The bond strength between the two dissimilar materials, composite and metal, dictates the properties and performance of the FMLs. The bonding becomes more critical when the polymer matrix is thermoplastic and hydrophobic in nature. This work employed a novel bonding technique between thermoplastic composites and a metal layer using six different combinations of organic coatings. The flexural, and interlaminar shear strength of the thermoplastic fiber metal laminates (TP-FMLs) were examined to investigate the bond strengths in the different cases along with fracture characteristics revealed from the tested samples using scanning electron microscopy. The viscoelastic performance of the fabricated TP-FMLs were also investigated using the dynamic mechanical thermal analysis method.     

A wavelet-based multi-dimensional temporal recurrent neural network for stencil printing performance prediction

The solder paste printing (SPP) is a critical procedure in a surface mount technology (SMT) based assembly line, which is one of the major attributes to the defect of the printed circuit boards (PCBs). The quality of SPP is influenced by multiple factors, such as the squeegee speed, pressure, the stencil separation speed, cleaning frequency, and cleaning profile. During printing, the printer environment is dynamically varying due to the physical change of solder paste, which can result in a dynamic variation of the relationships between the printing results and the influential factors. To reduce the printing defects, it is critical to understand such dynamic relationships. This research focuses on determining the printing performance during printing by implementing a wavelet filtering-based temporal recurrent neural network. To reduce the noise factor in the solder paste inspection (SPI) data, this research applies a three-dimensional dual-tree complex wavelet transformation for low-pass noise filtering and signal reconstruction. A recurrent neural network is utilized to model the performance prediction with low noise interference. Both printing sequence and process setting information are considered in the proposed recurrent network model. The proposed approach is validated using practical dataset and compared with other commonly used data mining approaches. The results show that the proposed wavelet-based multi-dimensional temporal recurrent neural network can effectively predict the printing process performance and can be a high potential approach in reducing the defects and controlling cleaning frequency. The proposed model is expected to advance the current research in the application of smart manufacturing in surface mount technology.     

Additive manufacturing of zirconia ceramics by material jetting

The possibility of additive manufacturing of ceramics has been reported widely in scientific literature. This study investigates the potential of direct inkjet printing or material jetting of 3Y-TZP ceramics by assessing the microstructure and mechanical properties of the sintered printed parts. The technique allows to print in layers of 10.5 μm, with an as-printed green density of 58 % and nearly fully sintered density of 6.03 ± 0.1 g/cm³ (99.7 % TD). The dimensions of the green and sintered parts were highly accurate but showed an anisotropic roughness in function of the building direction, mainly due to the support structures. The biaxial bending and 4-point bending strength of the sintered material was found to be substantially higher in the XY direction than in the building (Z) direction. SEM and X-Ray computed tomography revealed the presence of delamination cracks, agglomerates and spherical pores, which were identified as fracture origins on fractured surfaces.     

Feasibility of manufacturing of Al2O3–Mo HTCC by hybrid additive process

Additive manufacturing processes make it possible to produce increasingly complex 3D parts. In addition, these numerical processes can be usefully used to manufacture ceramic/metal parts of high dimensional resolution with thermal, electrical and electronic properties of interest for applications in the field of power electronics.In this context, a hybrid additive machine was developed to manufacture ceramic/metal parts. This machine consists in the combination of two additive manufacturing processes: stereolithography and robocasting.Using this hybrid process, the feasibility of HTCC components has been demonstrated by building dielectric alumina by stereolithography and molybdenum conductive network by robocasting. Molybdenum-based metallic formulation adapted to the process and allowing to obtain a high conductive metallic network has been developed. The co-debinding and co-sintering cycles have been optimized to minimize the content of residual carbon and to prevent the oxidation of molybdenum. The alumina/molybdenum interface has also been observed to conclude about a possible delamination between these two materials with different thermal expansion coefficients (CTE). Sintered HTCC parts have been characterized in the domain of hyperfrequency. The frequency responses deviate from the simulation due to a lack of dimensional accuracy of the metallic network.     

The effect of particle size distribution on the microstructure and properties of Al2O3 ceramics formed by stereolithography

Stereolithography (SL) shows advantages for preparing alumina-based ceramics with complex structures. The effects of the particle size distribution, which strongly influence the sintering properties in ceramic SL, have not been systematically explored until now. Herein, the influence of the particle size distribution on SL-manufactured alumina ceramics was investigated, including bending strength at room temperature, post-sintering shrinkage, porosity, and microstructural morphology. Seven particle size distributions of alumina ceramics were studied (in μm/μm: 30/5, 20/3, 10/2, 5/2, 5/0.8, 3/0.5, and 2/0.3); a coarse:fine particle ratio of 6:4 was maintained. At the same sintering temperature, the degree of sintering was greater for finer particle sizes. The particle size distribution had a larger influence on flexural strength, porosity and shrinkage than sintering temperature when the particle size distribution difference reached 10-fold but was weaker for 10 μm/2 μm, 5 μm/2 μm and 5 μm/0.8 μm. The sintering shrinkage characteristics of cuboid samples with different particle sizes were studied. The use of coarse particles influenced the accuracy of small-scale samples. When the particle size was comparable to the sample width, such as 30 μm/5 μm and 5 mm, the width shrinkage was consistent with the height shrinkage. When the particle size was much smaller than the sample width, such as 2 μm/0.3 μm and 5 mm, the width shrinkage was consistent with the length shrinkage. The results of this study provide meaningful guidance for future research on applications of SL and precise control of alumina ceramics through particle gradation.     

Direct additive manufacturing of melt growth Al2O3-ZrO2 functionally graded ceramics by laser directed energy deposition

Functionally graded ceramics (FGC), which combine properties of different ceramics in one part, usually have better comprehensive function and structural efficiency. In this study, four different gradient transition Al₂O₃-ZrO₂ FGC samples were prepared by laser directed energy deposition (LDED) method. The results show that there is an obvious interface in direct transition sample. The transition section bears tensile stress caused by difference of thermophysical properties of materials, resulting in significant longitudinal cracks. Element transition in interface region shows a step sharp transition. The direct transition sample shows intergranular fracture and the bonding strength is very low. Gradient transition mode can effectively suppress cracks, and avoid the step transition of microstructure and elements. Elements, microhardness of 25, 20 wt% FGC samples realized a nearly linear smooth transition. The interface fracture of FGC samples changed to transgranular fracture, bonding strength was significantly improved, and the maximum flexural strength reached 160.19 MPa.