Search for: All records

Thermal conductivity (TC) is greatly influenced by the working temperature, microstructures, thermal processing (heat treatment) history and the composition of alloys. Due to computational costs and lengthy experimental procedures, obtaining the thermal conductivity for novel alloys, particularly parts made with additive manufacturing, is difficult and it is almost impossible to optimize the compositional space for an absolute targeted value of thermal conductivity. To address these difficulties, a machine learning method is explored to predict the TC of additive manufactured alloys. To accomplish this, an extensive thermal conductivity dataset for additively manufactured alloys was generated for several AM alloy families (nickel, copper, iron, cobalt-based) over various temperatures (300–1273 K). This unique dataset was used in training and validating machine learning models. Among the five different regression machine learning models trained with the dataset, extreme gradient boosting performs the best as compared with other models with an R2 score of 0.99. Furthermore, the accuracy of this model was tested using Inconel 718 and GRCop-42 fabricated with laser powder bed fusion-based additive manufacture, which have never been observed by the extreme gradient boosting model, and a good match between the experimental results and machine learning prediction was observed. The average mean error in predicting the thermal conductivity of Inconel 718 and GRCop-42 at different temperatures was 3.9% and 2.08%, respectively. This paper demonstrates that the thermal conductivity of novel AM alloys could be predicted quickly based on the dataset and the ML model.

 

This study investigated the influence of diverse laser processing parameters on the thermophysical properties of Ti-6Al-4V and AlSi10Mg alloys manufactured via laser powder bed fusion. During fabrication, the laser power (50 W, 75 W, 100 W) and laser scanning speed (0.2 m/s, 0.4 m/s, 0.6 m/s) were adjusted while keeping other processing parameters constant. Besides laser processing parameters, this study also explored the impact of test temperatures on the thermophysical properties of the alloys. It was found that the thermophysical properties of L-PBF Ti-6Al-4V alloy samples were sensitive to laser processing parameters, while L-PBF AlSi10Mg alloy showed less sensitivity. In general, for the L-PBF Ti-6Al-4V alloy, as the laser power increased and laser scan speed decreased, both thermal diffusivity and conductivity increased. Both L-PBF Ti-6Al-4V and L-PBF AlSi10Mg alloys demonstrated similar dependence on test temperatures, with thermal diffusivity and conductivity increasing as the test temperature rose. The CALPHAD software Thermo-Calc (2023b), applied in Scheil Solidification Mode, was utilized to calculate the quantity of solution atoms, thus enhancing our understanding of observed thermal conductivity variations. A detailed analysis revealed how variations in laser processing parameters and test temperatures significantly influence the alloy’s resulting density, specific heat, thermal diffusivity, and thermal conductivity. This research not only highlights the importance of processing parameters but also enriches comprehension of the mechanisms influencing these effects in the domain of laser powder bed fusion.

 

In this paper, small blocks of 17-4 PH stainless steel were manufactured via extrusion-based bound powder extrusion (BPE)/atomic diffusion additive manufacturing (ADAM) technology in two different orientations. Ultrasonic bending-fatigue and uniaxial tensile tests were carried out on the test specimens prepared from the AM blocks. Specifically, a recently-introduced small-size specimen design is employed to carry out time-efficient fatigue tests. Based on the results of the testing, the stress–life (S-N) curves were created in the very high-cycle fatigue (VHCF) regime. The effects of the printing orientation on the fatigue life and tensile strength were discussed, supported by fractography taken from the specimens’ fracture surfaces. The findings of the tensile test and the fatigue test revealed that vertically-oriented test specimens had lower ductility and a shorter fatigue life than their horizontally-oriented counterparts. The resulting S-N curves were also compared against existing data in the open literature. It is concluded that the large-sized pores (which originated from the extrusion process) along the track boundaries strongly affect the fatigue life and elongation of the AM parts. 

This paper examined the effect of Si addition on the cracking resistance of Inconel 939 alloy after laser additive manufacturing (AM) process. With the help of CALculation of PHAse Diagrams (CALPHAD) software Thermo-Calc, the amounts of specific elements (C, B, and Zr) in liquid phase during solidification, cracking susceptibility coefficients (CSC) and cracking criterion based on$$\left| {{\text{d}}T/{\text{d}}f_{{\text{s}}}^{1/2} } \right|$$$\left(\text{d}T/\text{d}{f}_{\text{s}}^{1/2}\right)$values (T: solidification temperature,f_s: mass fraction of solid during solidification) were evaluated as the indicators for composition optimization. It was found that CSC together with$$\left| {{\text{d}}T/{\text{d}}f_{{\text{s}}}^{1/2} } \right|$$$\left(\text{d}T/\text{d}{f}_{\text{s}}^{1/2}\right)$values provided a better prediction for cracking resistance.

Graphical abstract

 

In this paper, the phase structure, composition distribution, grain morphology, and hardness of Al6061 alloy samples made with additive friction stir deposition (AFS-D) were examined. A nearly symmetrical layer-by-layer structure was observed in the cross section (vertical with respect to the fabrication-tool traversing direction) of the as-deposited Al6061 alloy samples made with a back-and-forth AFS-D strategy. Equiaxed grains were observed in the region underneath the fabrication tool, while elongated grains were seen in the “flash region” along the mass flow direction. No clear grain size variance was discovered along the AFS-D build direction except for the last deposited layer. Grains were significantly refined from the feedstock (~163.5 µm) to as-deposited Al6061 alloy parts (~8.5 µm). The hardness of the as-fabricated Al6061 alloy was lower than those of the feedstock and their heat-treated counterparts, which was ascribed to the decreased precipitate content and enlarged precipitate size. 

Currently, no commercial aluminum 7000 series filaments are available for making aluminum parts using fused deposition modeling (FDM)-based additive manufacturing (AM). The key technical challenge associated with the FDM of aluminum alloy parts is consolidating the loosely packed alloy powders in the brown-body, separated by thin layers of surface oxides and polymer binders, into a dense structure. Classical pressing and sintering-based powder metallurgy (P/M) technologies are employed in this study to assist the development of FDM processing strategies for making strong Al7075 AM parts. Relevant FDM processing strategies, including green-body/brown-body formation and the sintering processes, are examined. The microstructures of the P/M-prepared, FDM-like Al7075 specimens are analyzed and compared with commercially available FDM 17-4 steel specimens. We explored the polymer removal and sintering strategies to minimize the pores of FDM-Al7075-sintered parts. Furthermore, the mechanisms that govern the sintering process are discussed. 

In this work, the performance of the carbon doped compositionally complex alloy (CCA) MoNbTaW was studied under ambient and high pressure and high temperature conditions. TaC and NbC carbides were formed when a large concentration of carbon was introduced while synthesizing the MoNbTaW alloy. Both FCC carbides and BCC CCA phases were detected in the sample compound at room temperature, in which the BCC phase was believed to have only refractory elements MoNbTaW while FCC carbide came from TaC and NbC. Carbides in the carbon doped MoNbTaW alloy were very stable since no phase transition was obtained even under 3.1 GPa and 870 °C by employing the resistor-heating diamond anvil cell (DAC) synchrotron X-ray diffraction technique. Via in situ examination, this study confirms the stability of carbides and MoNbTaW in the carbon doped CCA even under high pressure and high temperature. 

Hardness is an essential property in the design of refractory high entropy alloys (RHEAs). This study shows how a neural network (NN) model can be used to predict the hardness of a RHEA, for the first time. We predicted the hardness of several alloys, including the novel C0.1Cr3Mo11.9Nb20Re15Ta30W20 using the NN model. The hardness predicted from the NN model was consistent with the available experimental results. The NN model prediction of C0.1Cr3Mo11.9Nb20Re15Ta30W20 was verified by experimentally synthesizing and investigating its microstructure properties and hardness. This model provides an alternative route to determine the Vickers hardness of RHEAs.