Powder Bed Metal Additive Manufacturing Research Articles

Development of Analysis Method for Laser Metal Powder Bed Additive Manufacturing Process by Lattice Boltzmann・Multi-Phase Field Methods

Development of Analysis Method for Laser Metal Powder Bed Additive Manufacturing Process by Lattice Boltzmann・Multi-Phase Field Methods

Effect of powder properties on mesoscale thermal FEM simulations of powder bed additive manufacturing

Numerous studies have used FEM simulations to assess the effects of the heat source parameters on the melt pool volume during metal powder bed additive manufacturing. However, considerable debate still exists on how to incorporate the evolution of the thermophysical properties used to describe the powder as it undergoes heating, melting, consolidation and finally solidification. For single layer studies, since powder volume is much smaller compared to the substrate volume, highly detailed, computationally expensive powder property descriptions may not provide a commensurate increase in accuracy of simulation results. This study aims to quantify the effect of powder properties on the melt pool volume created during electron beam melting of Ti6Al4V powder using predictions from a FEM-based heat conduction model. The dependence of thermal conductivity, specific heat, and density of the powder on temperature and beam power density will be studied. Additionally, the relevance of the powder properties with changing layer height and beam power and speed will also be quantified.

A review: Finishing technologies of parts made by metal powder-bed additive manufacturing

A review: Finishing technologies of parts made by metal powder-bed additive manufacturing

A review: finishing technologies of parts made by metal powder-bed additive manufacturing

A review: finishing technologies of parts made by metal powder-bed additive manufacturing

A practicable and reliable test for metal powder spreadability: development of test and analysis technique

A crucial step in the powder bed metal additive manufacturing process is the formation of a thin layer of powder on top of the existing material. The propensity of the powder to form thin layers under the conditions used in additive manufacturing is critically important, but no test method has yet been established to measure this characteristic, which is sometimes referred to as spreadability. The current work spreads a single layer of powder using commercial equipment from the paint and food industries and derives the density of a layer of powder, which is of a similar thickness to that in additive manufacturing. Twenty-four powders from eight suppliers have been tested and the density of the layers has been measured as a function of various parameters. Twenty-two of the powders successfully form thin layers, with a density of at least 40% of each powder’s apparent density. Hall flow time did not correlate with the spread layer density, although the two powders that did not spread did not pass through the Hall funnel. The roughness of the plate onto which the powder was spread, the recoater speed, the layer thickness, particle size and aspect ratio all affect the measured layer density. Results of the new test are repeatable and reproducible. These findings can be used to develop a test for spreadability for metal powders that can be used for additive manufacturing, which will help to improve the quality of printed components.

Part and supports optimization in metal powder bed additive manufacturing using simplified process simulation

This paper is concerned with shape and topology optimization of parts and their supports, taking into account constraints coming from the metal powder bed additive manufacturing process. Despite the high complexity of this process, it is represented by the simple inherent strain model, which has the advantage of being computationally cheap. Three optimization criteria, evaluated with this model, are proposed to minimize defects caused by additive manufacturing: vertical displacements, residual stresses and deflection of the part after baseplate separation. Combining these criteria with a constraint on the compliance for the final use of the part leads to optimization problems which deliver optimized manufacturable shapes with only a slight loss on the final use performance. The numerical results are assessed by manufacturing some optimized and reference geometries. These experimental results are also used to calibrate the inherent strain model by an inverse analysis. The same type of optimization is applied to supports in the case of a fixed non-optimizable part. For our 3-d numerical tests we rely on the level set method, the notion of shape derivatives and an augmented Lagrangian algorithm for optimization.

Optimizing Quality Inspection and Control in Powder Bed Metal Additive Manufacturing: Challenges and Research Directions

One of the key targets of Industry 4.0 and digital production, in general, is the support of faster, cleaner, and increasingly customizable manufacturing processes. Additive manufacturing (AM) is a natural fit in this context, as it offers the possibility to produce complex parts without the design constraints of traditional manufacturing routes, typically reducing both material waste and time to market. Nonetheless, the lack of repeatability of the manufacturing process, which typically translates into a lack of reproducibility and reliability of the quality of the final products compared to traditional subtractive technologies, is currently one of the major barriers to the widespread adoption of AM in mass production. To overcome this limitation, there are growing efforts in recent years toward better integration of advanced information technologies into AM, exploiting the layer-by-layer nature of the build. The consequence of these efforts is twofold: 1) the integration of advanced sensing technologies into the AM systems, making possible the in situ monitoring of huge amounts of data at multiple time scales and resolutions and 2) the ever-increasing role of data-driven approaches [especially machine learning (ML)] in the analysis of such data to provide real-time quality monitoring and process optimization. This article introduces and reviews the key technological developments of this phenomenon, with a special focus on metal powder bed fusion (PBF) technologies that are attracting the highest attention by the industrial AM community. After introducing the main manufacturing quality issues and needs that have to be developed and optimized, we provide a wide overview of the latest progress of in situ monitoring and control in metal PBF, with special regards to sensing technologies and ML approaches. Finally, we identify the open challenges and future research directions in this field.

Simulation of powder bed metal additive manufacturing microstructures with coupled finite difference-Monte Carlo method

Grain-scale microstructure evolution during additive manufacturing is a complex physical process. As with traditional solidification methods of material processing (e.g. casting and welding), microstructural properties are highly dependent on the solidification conditions involved. Additive manufacturing processes however, incorporate additional complexity such as remelting, and solid-state evolution caused by subsequent heat source passes and by holding the entire build at moderately high temperatures during a build. We present a three-dimensional model that simulates both solidification and solid-state evolution phenomena using stochastic Monte Carlo and Potts Monte Carlo methods. The model also incorporates a finite-difference based thermal conduction solver to create a fully integrated microstructural prediction tool. The three modeling methods and their coupling are described and demonstrated for a model study of laser powder-bed fusion of 300-series stainless steel. The investigation demonstrates a novel correlation between the mean number of remelting cycles experienced during a build, and the resulting columnar grain sizes.

The Environmental Impacts of Metal Powder Bed Additive Manufacturing

AbstractAdditive manufacturing (AM) is widely recognized as a critical pillar of advanced manufacturing and is moving from the design shop to the factory floor. As AM processes become more popular, it is paramount that engineers and policymakers understand and then reduce their environmental impacts. This article structures the current work on the environmental impacts of metal powder bed processes: selective laser melting (SLM), direct metal laser sintering (DMLS), electron beam melting (EBM), and binder jetting (BJ). We review the potential benefits and pitfalls of AM in each phase of a part's lifecycle and in different application domains (e.g., remanufacturing and hybrid manufacturing). We highlight critical uncertainties and future research directions throughout. The environmental impacts of AM are sensitive to the specific production and use-phase context; however, several broad lessons can be extracted from the literature. Unlike in conventional manufacturing, powder bed production impacts are dominated by the generation of the direct energy (electricity) required to operate the AM machines. Combined with a more energy-intensive feedstock (metal powder), this means that powder bed production impacts are higher than in conventional manufacturing unless production volumes are very small (saving tool production impacts), and/or there are significant material savings through part light weighting or improved buy-to-fly ratios.

Metallic additive manufacturing for bone-interfacing implants

This review investigates the available metallic powder bed additive manufacturing technologies with respect to their basic principles and capabilities in terms of developing orthopedic implants. Detailed descriptions of commonly used metallic alloys employed for orthopedic applications are also presented. The relationship between implant surface properties and cellular attachment and the formation of bacterial colonies are also discussed. Accordingly, we show how different surface modification techniques have been applied to improve both the biointerface of metallic implants for enhanced osseointegration and to control the formation of biofilm to protect against implant infection. In addition, the future direction of metallic additive manufacturing in the case of improving bone interface has been discussed. This review aids in the design of bone-interfacing metallic implants fabricated by additive manufacturing processes, specifically accommodating enhanced biointerfaces for the next generation patient-specific orthopedic implants.

Comparison of residual stresses obtained by the crack compliance method for parts produced by different metal additive manufacturing techniques and after friction stir processing

Metal additive manufacturing (AM) techniques are promising to build complex components in automotive, aerospace and biomedical industries. However, as built AM parts generally present residual stresses which may degrade the fatigue resistance of the material. Although the AM techniques have been substantially studied, few data about the residual stress level and distribution are available in literature. This paper presents residual stress measurements and analysis on the metal powder bed AM parts using the crack compliance method. Both electron beam melting (EBM) and selective laser melting (SLM) processes are investigated for two manufactured alloys, i.e., Ti6Al4V and AlSi10Mg. It is found that: (i) the EBM process results in negligible residual stresses; (ii) the SLM leads to compressive stresses in the middle, accompanied by tensile stresses at the bottom and the top of the built part; (iii) preheating the build platform in the SLM process significantly reduces the residual stresses and effectively mitigates the porosity. Moreover, we show that post-treatment by friction stir processing inverts the residual stress distribution compared to the SLM process while significantly reducing the porosity.

Layer-wise anomaly detection and classification for powder bed additive manufacturing processes: A machine-agnostic algorithm for real-time pixel-wise semantic segmentation

Increasing industry acceptance of powder bed metal Additive Manufacturing requires improved real-time detection and classification of anomalies. Many of these anomalies, such as recoater blade impacts, binder deposition issues, spatter generation, and some porosities, are surface-visible at each layer of the building process. In this work, the authors present a novel Convolutional Neural Network architecture for pixel-wise localization (semantic segmentation) of layer-wise powder bed imaging data. Key advantages of the algorithm include its ability to return segmentation results at the native resolution of the imaging sensor, seamlessly transfer learned knowledge between different Additive Manufacturing machines, and provide real-time performance. The algorithm is demonstrated on six different machines spanning three technologies: laser fusion, binder jetting, and electron beam fusion. Finally, the performance of the algorithm is shown to be superior to that of previous algorithms presented by the authors with respect to localization, accuracy, computation time, and generalizability.

Predictive Manufacturing: Subtractive and Additive

Manufacturing is the key to today’s industrial competitiveness, and it is broadly classified into two categories, subtractive and additive. In current study, the ability to predictively model manufacturing performance attributes in both categories is introduced. In subtractive manufacturing, modeling of laser-assisted and ultrasonic vibration-assisted milling are presented. In laser-assisted milling, the laser preheating temperature field is predicted, and the dynamic recrystallization as well as grain growth triggered under high temperature is considered, which enhances the accuracy of force and residual stress prediction. In ultrasonic vibration-assisted milling, the intermittent effect is considered through tool-workpiece separation criteria. And the force reduction in ultrasonic vibration-assisted milling is accurately predicted. In additive manufacturing, laser-assisted metal additive manufacturing is introduced. And the predictive modeling of temperature field in powder bed metal additive manufacturing is presented. The model considers heat transfer boundary including heat loss from convection and radiation at the part boundary. Through the comparison between measured and calculated molten pool dimensions, the model is proven to have high computational efficiency and high prediction accuracy.

Analytical Thermal Modeling of Powder Bed Metal Additive Manufacturing Considering Powder Size Variation and Packing.

This work presents a computationally efficient predictive model based on solid heat transfer for temperature profiles in powder bed metal additive manufacturing (PBMAM) considering the heat transfer boundary condition and powder material properties. A point moving heat source model is used for the three-dimensional temperature prediction in an absolute coordinate. The heat loss from convection and radiation is calculated using a heat sink solution with a mathematically discretized boundary considering non-uniform temperatures and heat loss at the boundary. Powder material properties are calculated considering powder size statistical distribution and powder packing. The spatially uniform and temperature-independent material properties are employed in the temperature prediction. The presented model was tested in PBMAM of AlSi10Mg under different process conditions. The calculations of material properties are needed for AlSi10Mg because of the significant difference in thermal conductivity between powder form and solid bulk form. Close agreement is observed upon experimental validation on the molten pool dimensions.

Analytical modeling of part distortion in metal additive manufacturing

This work presents an analytical modeling methodology for the prediction of part distortion in powder bed metal additive manufacturing (PBMAM). The presented model consists of analytical thermal modeling, thermal stress modeling, residual stress modeling, and distortion modeling. It has promising short computational efficiency without resorting to finite element analysis or any iteration-based simulations. The temperature profile is calculated using a moving point heat source solution and heat sink solution with consideration of heat input from a moving laser and heat loss from boundary heat transfer. The thermal stress is calculated from the temperature calculation using a thermal stress model considering thermal load, surface tension, and hydrostatic pressure. The residual stress is calculated from the thermal stress calculation using an elastoplastic relaxation procedure. The residual stress–induced part distortion is finally calculated from the calculated residual stress and residual strain using a surface displacement model. The calculated part distortion was validated to experimental measurement on a twin-cantilever part produced by PBMAM of Ti6Al4V. Close agreements were observed. The computational time was recorded less than 10 s. The high predictive accuracy and high computational efficiency allow the process parameter planning for distortion control and elimination through inverse analysis.

Analytical modeling of part porosity in metal additive manufacturing

Porosity is a frequently observed defect in metal additive manufacturing due to the large thermal gradients caused by the repeated rapid melting and solidification. This work presents a physics-based model to predict the part porosity in powder bed metal additive manufacturing (PBMAM) per given process parameters, materials properties, powder size distribution. The molten pool dimensions were first calculated by a closed-form temperature solution considering the laser heat input and part boundary heat loss. The porosity evolution was calculated by subtracting the volume fraction of the preprocessed powder bed void from the postprocessed porosity. The volume fractions of powder and void in packed powder bed were calculated by advancing front approach (AFA) and image analysis considering powder size variation and statistical distribution. The porosity model was developed based on the correlation between molten pool dimensions and porosity evolution using regression analysis. Experimental validation of porosity values under different process conditions was included in PBMAM of Ti6Al4V. A close agreement was observed upon validation. Different from the previous statistical model, the presented model was developed based on the physical relationship between thermal behaviors porosity formation, which leads to an improved prediction accuracy with a small number of calibration data. Moreover, the short computational time of the closed-form temperature solution allows a fast porosity prediction for various processing window.

Analytical modeling of lack-of-fusion porosity in metal additive manufacturing

This work presents a physics-based analytical modeling methodology for the prediction of the lack-of-fusion porosity in powder bed metal additive manufacturing (PBMAM) considering the molten pool geometry, powder size variation, and packing. The presented model has promising short computational time without resorting to the finite element method or any iteration-based simulations. The temperature profiles were calculated using a closed-form temperature solution. Multiple transverse sectional areas of the molten pool geometry were plotted on a cross-sectional area of the part based on hatch space and layer thickness to calculate the lack-of-fusion area. The powder bed porosity was calculated using advancing front approach with consideration of powder statistical distribution and powder packing. The part porosity was converted from the calculated lack-of-fusion area by multiplying the calculated powder bed porosity. Acceptable agreements were observed upon validation against experimental measurements under various process conditions in PBMAM of Ti6Al4V. The computational time was recorded less than 26 s for the porosity calculation of five consecutive layers. The presented model has high prediction accuracy and high computational efficiency, which allow the porosity calculation for large-scale parts and process parameters planning through inverse analysis, and thus improves the usefulness of analytical modeling in real applications.

Part-scale build orientation optimization for minimizing residual stress and support volume for metal additive manufacturing: Theory and experimental validation

Laser powder bed metal additive manufacturing (AM) has been widely accepted by the industry to manufacture end-use components with complex geometry to achieve desirable performance (i.e. conformal cooling). However, residual stress and large deformation introduced in the laser AM process lead to severe issues, such as cracks, delamination, and large deformation. These issues result in the stoppage of powder spreading and warpage of the component. To overcome these issues, a novel optimization framework based on fast process modeling is proposed to find the optimal build orientation by minimizing the maximum residual stress and support structure volume. For support generation, a voxel-based methodology is proposed to systematically capture support surfaces from STL file, form support structure, and generate Cartesian mesh for fast process modeling. Instead of using conformal mesh, the voxel-based fictitious domain method is used to calculate the stress distribution in the design domain including the support structure, which is represented by the homogenized model. This can circumvent time-consuming mesh generation for geometrically complex geometry and its support structure during the optimization iterations, thus making it possible to minimize residual stress through orientation optimization based on process modeling. Due to its self-supporting and open-cell nature, lattice structure is employed as the support structure to anchor the overhangs to the substrate to prevent distortion resulting from residual stress. Asymptotic homogenization (AH) method is employed to compute the effective properties of lattice structure, while a multiscale model is proposed to compute the yield strength. In particular, the multi-objective optimization including both the residual stress and support volume is discussed and investigated in this work. Experimental validation is conducted on a realistic component with some geometric complexity. By comparing the component and support structure without build orientation optimization, it is found that the proposed framework can significantly reduce the influence of the residual stress on the printed part, ensure the manufacturability of the design, and decrease the material consumption for the sacrificial support structure simultaneously.

Design and Fabrication of a 3-D Printed Metallic Flexible Joint for Snake-Like Surgical Robot

Snake-like robots have numerous applications in minimally invasive surgery. One important research topic of snake-like robots is the flexible joint mechanism and its actuation. This letter describes the design and fabrication of a new type of flexible joint mechanism that is enabled by metal powder bed additive manufacturing technique. Kinematics and static models of the flexible joint are presented, which can help in designing and controlling the flexible joint. As a compliant mechanism, the fatigue characteristics of the flexible joint is investigated. Finite element analysis (FEA) is performed aiming for optimizing the design process. In the experiment section, model estimation, FEA, and experimental validation are conducted for further understanding the characteristics of the flexible joint. An example design that can survive after 100 000 full loading cycles is demonstrated. Finally, different design variations of the proposed method and a multi-section flexible endoscope using the proposed design are introduced. The proposed flexible joint has the potential not only in reducing the cost of manufacturing and assembling a snake-like surgical robot, but also benefits for developing of more sophisticated three-dimensional snake robotic structure that has an optimized space for embedded sensing and actuation.

On utilizing topology optimization to design support structure to prevent residual stress induced build failure in laser powder bed metal additive manufacturing

Metal additive manufacturing (AM) as an emerging manufacturing technique has been gradually accepted to manufacture end-use components. However, one of the most critical issues preventing its broad applications is build failure resulting from residual stress accumulation in manufacturing process. The goal of this work is to investigate the feasibility of using topology optimization to design support structure to mitigate residual stress induced build failure. To make topology optimization computationally tractable, the inherent strain method is employed to perform fast prediction of residual stress in an AM build. Graded lattice structure optimization is utilized to design the support structure due to the open-celled and self-supporting nature of periodic lattice structure. The objective for the optimization is to minimize the mass of sacrificial support structure under stress constraint. By limiting the maximum stress under the yield strength, cracking resulting from residual stress can be prevented. To show the feasibility of the proposed method, the support structure of a double-cantilever beam and a hip implant is designed, respectively. The support structure after optimization can achieve a weight reduction of approximately 60%. The components with optimized support structures no longer suffer from stress-induced cracking after the designs are realized by AM, which proves the effectiveness of the proposed method.