On the measurement of relative powder-bed compaction density in powder-bed additive manufacturing processes

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.matdes.2018.06.030 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Experimental studies in the literature have identified the powder-bed compaction density as an important parameter, governing the quality of additively manufactured parts. For example, in laser powder-bed fusion (LPBF), the powder-bed compaction density directly affects the effective powder thermal conductivity and consequently the temperature distribution in melt pool. In this study, this physical parameter in a LPBF build compartment is measured using a new methodology. A UV curable polymer is used to bind powder-bed particles at various locations on the powder-bed compartment when Hastelloy X was used. The samples are then scanned using a nano-computing tomography (CT) system at high resolution to obtain an estimation of the relative powder-bed compaction density. It is concluded that due to the interaction between the recoater and the variation in the powder volume accumulated ahead of the recoater across the build compartment, the relative powder-bed compaction density decreases along the recoater moving direction (from 66.4% to 52.4%.). This variation in the powder-bed compaction density affects the density and surface roughness of the final printed parts that is also investigated. Results show that the part density and surface quality decrease ~0.25% and ~20%, respectively, along the build bed in direction of the recoater motion.Natural Sciences and Engineering Research Council of Canada (NSERC) Federal Economic Development Agency for Southern Ontario (FedDev Ontario

Additively manufactured metallic biomaterials

Metal additive manufacturing (AM) has led to an evolution in the design and fabrication of hard tissue substitutes, enabling personalized implants to address each patient's specific needs. In addition, internal pore architectures integrated within additively manufactured scaffolds, have provided an opportunity to further develop and engineer functional implants for better tissue integration, and long-term durability. In this review, the latest advances in different aspects of the design and manufacturing of additively manufactured metallic biomaterials are highlighted. After introducing metal AM processes, biocompatible metals adapted for integration with AM machines are presented. Then, we elaborate on the tools and approaches undertaken for the design of porous scaffold with engineered internal architecture including, topology optimization techniques, as well as unit cell patterns based on lattice networks, and triply periodic minimal surface. Here, the new possibilities brought by the functionally gradient porous structures to meet the conflicting scaffold design requirements are thoroughly discussed. Subsequently, the design constraints and physical characteristics of the additively manufactured constructs are reviewed in terms of input parameters such as design features and AM processing parameters. We assess the proposed applications of additively manufactured implants for regeneration of different tissue types and the efforts made towards their clinical translation. Finally, we conclude the review with the emerging directions and perspectives for further development of AM in the medical industry.National Institutes of Health || The Natural Sciences and Engineering Research Council of Canada || Network for Holistic Innovation in Additive Manufacturin

Rapid Thermo-mechanical Modeling of Laser Powder-Bed Fusion Additive Manufacturing using an Effective Heat Source

Laser powder-bed fusion (LPBF) is one of the most common types of additive manufacturing (AM) processes that has gained a lot of attraction by industries. This process induces a high magnitude of a temperature gradient within the fabricated part due to fast thermal and cooling cycles. Therefore, the existence of residual stresses and deformation of produced parts is inevitable. There are a tremendous number of process parameters involved in LPBF that affect the quality of final products, such as laser power, scanning speed, layer thickness, hatching distance, etc. Modeling and simulation of LPBF provide an opportunity for predicting residual stresses and deformation of LBPF-made parts. Therefore, optimizing process parameters for minimizing residual stresses and deformation is required. Extensive computational time and implementing a proper heat source model are some of the existing challenges in the modeling and simulation of LPBF. Due to micro-scale features of the LPBF melt pool zone, as well as the high-speed process (up to 5 m/s), the computational cost of the simulation process of making macro-scale large parts is highly expensive. On the other hand, extremely fine mesh is required for capturing heat transfer in the laser interaction zone accurately. Consequently, a large number of elements need to be analyzed for solving the problem, which requires a strong resource for computation. This work presents the multi-scale modeling approach based on two groups of micro/mesoscale and macroscale simulations. Firstly, melt pool dimensions of Hastelloy X material single tracks were measured experimentally. Afterward, the micro/mesoscale simulation of LPBF single track was conducted, while implementing a volumetric heat source model (conical-Gaussian) to extract the transient temperature profile and melt pool dimensions. The percentage difference of melt pool depth and width dimensions derived from simulation results and experimental ones are 13% and 6%, respectively. The validated model was then used for multi-track multi-layer simulation. The effect of thin-wall thicknesses on the melt pool dimensions has been studied as an application of the multi-track simulation process. In the macroscale simulation, the thermo-mechanical model was developed for obtaining residual stresses and deformation of the fabricated part. As a major contribution, novel effective heat flux is proposed and applied for accelerating the simulation. Thermo-mechanical modeling of the cube building process is carried out using an effective heat flux. The residual stress is experimentally measured using an X-Ray analyzer machine. The simulation results show a good agreement with experimental ones while a significant reduction in computational costs is achieved. The average percentage difference in predicting residual stress in longitudinal and transverse directions was 11% and the total computational time was 90 minutes

Experimental and numerical investigation on the effect of layer thickness during laser powder-bed fusion of stainless steel 17-4PH

Layer thickness is one of the most important input process parameters in laser powder-bed fusion (LPBF) additive manufacturing (AM) since it directly affects the defects found in the printed products, such as porosity, cracks, and manufacturing rate. In this work, three-dimensional finite element heat transfer model was employed to compare and evaluate two different powder layer thicknesses (20 μm and 40 μm) at varying laser power and scanning speeds. A layer-thickness dependent laser absorptivity approach was considered to improve the prediction accuracy of the proposed model. Single track experiments with stainless steel 17-4PH were conducted to validate the simulation model. Simulation results show good agreement with the experimental results with different layer thicknesses. The corresponding averaged melt pool error for width and depth were 4.2% and 9.1%, respectively. It is found that the melt pool dimensions with different layer thicknesses are similar for the most part with slight variations in the melt pool dimensions using varying laser power and scanning speed. However, the morphology of the melt pool track shows visible changes between different thicknesses

Topology optimization for metal additive manufacturing: current trends, challenges, and future outlook

Metal additive manufacturing is gaining immense research attention. Some of these research efforts are associated with physics, statistical, or artificial intelligence-driven process modelling and optimisation, structure–property characterisation, structural design optimisation, or equipment enhancements for cost reduction and faster throughputs. In this review, the focus is drawn on the utilisation of topology optimisation for structural design in metal additive manufacturing. First, the symbiotic relationship between topology optimisation and metal additive manufacturing in aerospace, medical, automotive, and other industries is investigated. Second, support structure design by topology optimisation for thermal-based powder-bed processes is discussed. Third, the introduction of capabilities to limit manufacturing constraints and generate porous features in topology optimisation is examined. Fourth, emerging efforts to adopt artificial intelligence models are examined. Finally, some open-source and commercial software with capabilities for topology optimisation and metal additive manufacturing are explored. This study considers the challenges faced while providing perceptions on future research directions