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1. Diamond-reinforced cutting tools using laser-based additive manufacturing

   https://doi.org/doi.org/10.1016/j.addma.2020.101602  

   Traxel, Kellen; Bandyopadhyay, Amit (January 2021, Additive manufacturing)

   null (Ed.)

   Production-volume and cost requirements currently limit machine tool manufacturers’ ability to produce application-specific tooling with traditional methods, motivating the development of innovative manufacturing technologies. To this end, we detail a manufacturing framework for the design and production of application-specific cutting tools based on industry standard tungsten carbide-cobalt (WC-Co)-based “carbide” cutting materials using additive manufacturing (AM). Herein, novel diamond-reinforced carbide structures were designed and manufactured via AM and subsequently tested in comparison to current commercial products that are traditionally-processed. The resulting diamond-reinforced composites were free from large scale cracking and maintained microstructures with multiple reinforcing phases. Diamond incorporation had a remarkable effect on the processing, microstructure, and machining performance of the WC-Co based material in comparison to a commercial carbide cutting tool of similar composition as well as the base WC-Co matrix. Detailed microstructure and phase analysis, as well as machining experiments, demonstrate the ability to exploit laser-based directed energy deposition (DED)-based AM to create multifunctional cutting tools that can be designed to meet ever-increasing manufacturing demands across many industries. 

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2. Additive Manufacturing of High-Entropy Alloys: A Review

   https://doi.org/10.3390/e20120937  

   Chen, Shuying; Tong, Yang; Liaw, Peter (December 2018, Entropy)

   Owing to the reduced defects, low cost, and high efficiency, the additive manufacturing (AM) technique has attracted increasingly attention and has been applied in high-entropy alloys (HEAs) in recent years. It was found that AM-processed HEAs possess an optimized microstructure and improved mechanical properties. However, no report has been proposed to review the application of the AM method in preparing bulk HEAs. Hence, it is necessary to introduce AM-processed HEAs in terms of applications, microstructures, mechanical properties, and challenges to provide readers with fundamental understanding. Specifically, we reviewed (1) the application of AM methods in the fabrication of HEAs and (2) the post-heat treatment effect on the microstructural evolution and mechanical properties. Compared with the casting counterparts, AM-HEAs were found to have a superior yield strength and ductility as a consequence of the fine microstructure formed during the rapid solidification in the fabrication process. The post-treatment, such as high isostatic pressing (HIP), can further enhance their properties by removing the existing fabrication defects and residual stress in the AM-HEAs. Furthermore, the mechanical properties can be tuned by either reducing the pre-heating temperature to hinder the phase partitioning or modifying the composition of the HEA to stabilize the solid-solution phase or ductile intermetallic phase in AM materials. Moreover, the processing parameters, fabrication orientation, and scanning method can be optimized to further improve the mechanical performance of the as-built-HEAs. 

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3. Influence of active cooling on microstructure and mechanical properties of wire arc additively manufactured mild steel

   https://doi.org/10.3389/fmech.2023.1130407  

   Dash, Aruntapan; Squires, Lile; Avila, Jose D.; Bose, Susmita; Bandyopadhyay, Amit (February 2023, Frontiers in Mechanical Engineering)

   Additive manufacturing (AM) of metals attracts attention because it can produce complex structures in a single step without part-specific tooling. Wire arc additive manufacturing (WAAM), a welding-based method that deposits metal layer by layer, is gaining popularity due to its low cost of operation, feasibility for large-scale part fabrication, and ease of operation. This article presents the fabrication of cylindricalshaped mild steel (ER70S-6) samples with a gas metal arc (MIG)—based hybrid WAAM system. A mechanism for actively cooling the substrate is implemented. Deposition parameters are held constant to evaluate the impact of active cooling on deposition quality, inter-pass cooling time, and internal defects. Surface and volume defects can be seen on the cylindrical sample fabricated without an active cooling setup. Defect quantification and phase analysis are performed. The primary phase formed was α-iron in all samples. Actively cooled deposition cross section showed a 99% decrease of incomplete fusion or porosity, with temperature measured 60 s after deposition averaging 235°C less than non-cooled. Microstructural analysis revealed uniformity along the build direction for actively cooled deposition but non-uniform microstructures without cooling. Hardness decreased by approximately 22HV from the first layer to the final layer in all cases. Property variation can be attributed to the respective processing strategies. The current study has demonstrated that active cooling can reduce production time and porosity while maintaining uniform microstructure along the build direction. Such an approach is expected to enhance the reliability of WAAM-processed parts in the coming days. 

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4. Emulating the evolution of phase separating microstructures using low-dimensional tensor decomposition and nonlinear regression

   https://doi.org/10.1557/s43577-022-00443-x  

   Iquebal, Ashif S.; Wu, Peichen; Sarfraz, Ali; Ankit, Kumar (January 2023, MRS Bulletin)

   The phase-field method is an attractive computational tool for simulating microstructural evolution during phase separation, including solidification and spinodal decomposition. However, the high computational cost associated with solving phase-field equations currently limits our ability to comprehend phase transformations. This article reports a novel phase-field emulator based on the tensor decomposition of the evolving microstructures and their corresponding two-point correlation functions to predict microstructural evolution at arbitrarily small time scales that are otherwise nontrivial to achieve using traditional phase-field approaches. The reported technique is based on obtaining a low-dimensional representation of the microstructures via tensor decomposition, and subsequently, predicting the microstructure evolution in the low-dimensional space using Gaussian process regression (GPR). Once we obtain the microstructure prediction in the low-dimensional space, we employ a hybrid input–output phase-retrieval algorithm to reconstruct the microstructures. As proof of concept, we present the results on microstructure prediction for spinodal decomposition, although the method itself is agnostic of the material parameters. Results show that we are able to predict microstructure evolution sequences that closely resemble the true microstructures (average normalized mean square of 6.78×10^−7) at time scales half of that employed in obtaining training data. Our data-driven microstructure emulator opens new avenues to predict the microstructural evolution by leveraging phase-field simulations and physical experimentation where the time resolution is often quite large due to limited resources and physical constraints, such as the phase coarsening experiments previously performed in microgravity. 

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5. Fast-throughput simulations of laser-based additive manufacturing in metals to study the influence of processing parameters on mechanical properties

   https://doi.org/10.1016/j.heliyon.2023.e23202  

   McElfresh, Cameron; Wang, Y Morris; Marian, Jaime (January 2024, Heliyon)

   Schultz, Christian (Ed.)

   Laser-powder bed fusion additive manufacturing (LPBF-AM) of metals is rapidly becoming one of the most important materials processing pathways for next-generation metallic parts and components in a number of important applications. However, the large parametric space that characterizes laser-based LPBF-AM makes it challenging to understand what are the variables controlling the microstructural and mechanical property outcomes. Sensitivity studies based on direct LPBF-AM processing are costly and lengthy to conduct, and are subjected to the specifications and variability of each printer. Here we develop a fast-throughput numerical approach that simulates the LPBF-AM process using a cellular automaton model of dynamic solidification and grain growth. This is accompanied by a polycrystal plasticity model that captures grain boundary strengthening due to complex grain geometry and furnishes the stress-strain curves of the resulting microstructures. Our approach connects the processing stage with the mechanical testing stage, thus capturing the effect of processing variables such as the laser power, laser spot size, scan speed, and hatch width on the yield strength and tangent moduli of the processed materials. When applied to pure Cu and stainless 316L steel, we find that laser power and scan speed have the strongest influence on grain size in each material, respectively. 

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