Additively manufactured materials and structures: A state-of-the-art review on their mechanical characteristics and energy absorption

Microstructural evolution and mechanical properties of Inconel 625 superalloy fabricated by pulsed microplasma rapid additive manufacturing

Selective Laser Melting (SLM) process has a great potential to create unique 3D components of AlSi10Mg with exceptional degrees of freedom for wide range of applications in industries. However, the presence of porosity and other defects influenced by process parameters can be harmful to fabricated components in some applications. Process parameters and post-processing treatments helps in minimizing the metallurgical defects and enhance the mechanical properties. This review aims to provide a detailed description of the recent research done on the SLM-AlSi10Mg including the effect of SLM process parameters on the mechanical properties of the alloy. The effect of different heat treatments on the microstructure and overall mechanical properties of SLM fabricated AlSi10Mg alloy parts also discussed. The findings of the review revealed that the density of additively manufactured AlSi10Mg built part first increases with increase in energy density and then decreases above threshold value due to lack of fusion at low energy and at high energy key hole formation take place. Tensile strength of the built parts is affected by the process parameters, built direction, baseplate preheating temperature and built environment. The process parameters and built condition also affect the roughness and hardness of additively manufactured AlSi10Mg parts. Heat and surface treatment also help to improve the mechanical and surface properties by changing microstructure orientation. Furthermore, a comprehensive study of various surface finishing techniques based on energy sources such as mechanical, chemical and thermal improves the surface roughness and fatigue life of SLM fabricated AlSi10Mg parts is also presented. 

A comprehensive review on the effect of process parameters and post-process treatments on microstructure and mechanical properties of selective laser melting of AlSi10Mg

Direct metal printing is a promising technique for manufacturing injection molds with complex conformal cooling channels from maraging steel powder, which is widely applied in automotive or aerospace industries. However, two major disadvantages of direct metal printing are the narrow process window and length of time consumed. The fabrication of high-density injection molds is frequently applied to prevent coolant leakage during the cooling stage. In this study, we propose a simple method of reducing coolant leakage for a direct-metal-printed injection mold with conformal cooling channels by combining injection mold fabrication with general process parameters, as well as solution and aging treatment (SAT). This study comprehensively investigates the microstructural evolution of the injection mold after SAT using field-emission scanning electron microscopy and energy-dispersive X-ray spectroscopy. We found that the surface hardness of the injection mold was enhanced from HV 189 to HV 546 as the Ni-Mo precipitates increased from 12.8 to 18.5%. The size of the pores was reduced significantly due to iron oxide precipitates because the relative density of the injection mold increased from 99.18 to 99.72%. The total production time of the wax injection mold without coolant leakage during the cooling stage was only 62% that of the production time of the wax injection mold fabricated with high-density process parameters. A significant savings of up to 46% of the production cost of the injection mold was obtained.

A Simple Method of Reducing Coolant Leakage for Direct Metal Printed Injection Mold with Conformal Cooling Channels Using General Process Parameters and Heat Treatment

Effect of individual printing parameters on residual stress and tribological behaviour of 316L stainless steel fabricated with laser powder bed fusion (L-PBF)

Additive manufacturing (AM) is one of the recently studied research areas, due to its ability to eliminate different subtractive manufacturing limitations, such as difficultly in fabricating complex parts, material wastage, and numbers of sequential operations. Laser-powder bed fusion (L-PBF) AM for SS316L is known for complex part production due to layer-by-layer deposition and is extensively used in the aerospace, automobile, and medical sectors. The process parameter selection is crucial for deciding the overall quality of the SS316L build component with L-PBF AM. This review critically elaborates the effect of various input parameters, i.e., laser power, scanning speed, hatch spacing, and layer thickness, on various mechanical properties of AM SS316L, such as tensile strength, hardness, and the effect of porosity, along with the microstructure evolution. The effect of other AM parameters, such as the build orientation, pre-heating temperature, and particle size, on the build properties is also discussed. The scope of this review also concerns the challenges in practical applications of AM SS316L. Hence, the residual stress formation, their influence on the mechanical properties and corrosion behavior of the AM build part for bio implant application is also considered. This review involves a detailed comparison of properties achievable with different AM techniques and various post-processing techniques, such as heat treatment and grain refinement effects on properties. This review would help in selecting suitable process parameters for various human body implants and many different applications. This study would also help to better understand the effect of each process parameter of PBF-AM on the SS316L build part quality.

A Critical Review on Effect of Process Parameters on Mechanical and Microstructural Properties of Powder-Bed Fusion Additive Manufacturing of SS316L.

A comprehensive review on effect of process parameters and heat treatment on tensile strength of additively manufactured Inconel-625

Machine learning (ML) is one of the artificial intelligence tools which uses past data to learn the relationship between input and output and helps to predict future trends. Powder bed fusion additive manufacturing (PBF-AM) is extensively used for a wide range of applications in the industry. The AM process establishment for new material is a crucial task with trial-and-error approaches. In this work, ML techniques have been applied for the prediction of the density of PBF-AM. Density is the most vital property in evaluating the overall quality of the AM building part. The ML techniques, namely, artificial neural network (ANN), K-nearest neighbor (KNN), support vector machine (SVM), and linear regression (LR), are used to develop a model for predicting the density of the stainless steel (SS) 316L build part. These four methods are validated using R-squared values and different error functions to compare the predicted result. The ANN and SVM model performed well with the R-square value of 0.95 and 0.923, respectively, for the density prediction. The ML models would be beneficial for the prediction of the process parameters. Further, the developed ML model would also be helpful for the future application of ML in additive manufacturing.

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Density Prediction in Powder Bed Fusion Additive Manufacturing: Machine Learning-Based Techniques

Experimental investigation of mechanical properties for wrought and selective laser melting additively manufactured SS316L and MS300

Additive manufacturing (AM) is getting wide acceptance for various applications in the industries to fabricate 3D complex components with least material wastage. However, it is difficult to build the defect-free components with nominal residual stresses and high mechanical strength due to the high thermal gradient during the AM process. Hence, it is important to study temperature distribution on a layer by layer deposition to get defect-free components. In this paper, 3-D transient heat transfer numerical model is developed for laser powder based additive manufacturing process to compute the thermal behavior. The conservation of energy is solved to compute the temperature distribution on each layer during AM processes. Thermal cycles show variation in the peak temperature as the subsequent layer gets deposited over previous layer. The peak temperature is highest in the beginning and decreases as the more layers get deposited during the AM process. The heating and cooling gradient is also observed more in the beginning and successively reduces with the time. The developed model will be useful to reduce residual stresses and improve geometrical stability. This study will also be helpful to understand the AM process and to produce defect-free components.

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Papers

A Critical Review on Effect of Process Parameters on Mechanical and Microstructural Properties of Powder-Bed Fusion Additive Manufacturing of SS316L.

A comprehensive review on effect of process parameters and heat treatment on tensile strength of additively manufactured Inconel-625

Density Prediction in Powder Bed Fusion Additive Manufacturing: Machine Learning-Based Techniques

Experimental investigation of mechanical properties for wrought and selective laser melting additively manufactured SS316L and MS300

Thermal Modeling of Laser Powder-Based Additive Manufacturing Process