Abstract Additive Manufacturing Research Articles

Review of the method design and additive manufacturing of gears

Additive manufacturing technology is one of the main components of the fourth industrial revolution known as Industry 4.0. Over the last decades, the increase in the dynamics of the development of this technology manifests itself in the form of a wide spectrum of implementations of the methods in the production processes of many elements. This paper presents the latest achievements in the production of gears where the additive technologies have been applied with the use of polymers and metallic materials. The most frequently used methods in this field were indicated, as well as problems related to geometric accuracy or fatigue life of elements manufactured with the use of the methods mentioned. In addition, future directions of gear design, the implementation of which was possible thanks to the use of AM, as well as the scope of research that should be undertaken in this area in the future, were defined. Keywords: mechanical engineering, manufacturing technologies, additive manufacturing, gears

Microstructure and strain rate-dependent deformation behavior of PBF-EB Ti6Al4V lattice structures

Abstract Additive manufacturing techniques enable the fabrication of new lightweight components with tailored mechanical properties. Considering current application fields, components are often over-dimensioned since a lack of data regarding the mechanical properties under compression or tensile loading at high strain rates is present. In this work, the influence of various strain rates on the mechanical properties of electron beam powder bed fusion Ti6Al4V lattice structures was investigated. In order to capture the damage mechanisms that occurred, a single unit cell plane was considered. In terms of mechanical characterization, high-speed tensile tests at nominal strain rates from 0.025 to 250 s-1 were carried out. By the additional use of a high-speed camera system and subsequent digital image correlation, an investigation of material reactions during shortest test times was enabled. Based on the results, a positive strain rate dependency was identified for yield and ultimate tensile strength for both investigated lattice types. In detail, an increase in ultimate tensile strength of 16 % for BCC- and 20 % for F2CCZ-specimens could be detected.

Pyramid Ensemble Convolutional Neural Network for Virtual Computed Tomography Image Prediction in a Selective Laser Melting Process

Abstract Additive manufacturing (AM) is a type of advanced manufacturing process that enables fast prototyping to realize personalized products in complex shapes. However, quality defects existed in AM products can directly lead to significant failures (e.g., cracking caused by voids) in practice. Thus, various inspection techniques have been investigated to evaluate the quality of AM products, where X-ray computed tomography (CT) serves as one of the most accurate techniques to detect geometric defects (e.g., voids inside an AM product). Taking a selective laser melting (SLM) process as an example, voids can be detected by investigating CT images after the fabrication of products with limited disturbance from noises. However, limited by the sensor size and scanning speed issue, CT is difficult to be used for online (i.e., layer-wise) voids detection, monitoring, and process control to mitigate the defects. As an alternative, optical cameras can provide layer-wise images to support online voids detection. The intricate texture of the layer-wise image restricts the accuracy of void detection in AM products. Therefore, we propose a new method called pyramid ensemble convolutional neural network (PECNN) to efficiently detect voids and predict the texture of CT images using layer-wise optical images. The proposed PECNN can efficiently extract informative features based on the ensemble of the multiscale feature-maps (i.e., image pyramid) from optical images. Unlike deterministic ensemble strategies, this ensemble strategy is optimized by training a neural network in a data-driven manner to learn the fine-grained information from the extracted feature-maps. The merits of the proposed method are illustrated by both simulations and a real case study in a SLM process.

Extending High Value Components Performances with Additive Manufacturing: Application to Naval Applications

Additive Manufacturing (AM), consists of depositing material in successive layers to obtain the desired part. The parts produced by AM can thus adopt geometries inaccessible by conventional manufacturing means, for example hollow or lattice structures which considerably reduce their weight while keeping or even improving their mechanical properties. Among the many existing processes, Wire Arc Additive Manufacturing (WAAM) is particularly well suited to the manufacture of large metallic parts. It is characterized by a supply of heat in the form of an electric arc (produced by a welding generator) and a supply of material in the form of wire. This paper will discuss the impact of additive manufacturing to enhance the performances of high value components, based on naval application: the manufacturing of a hollow propeller blade demonstrator of 1.5 m high realized in the laboratory.

Design and Development of a Mold for Patternless Casting Using AM/3D Printing

Additive manufacturing is a technology that is influencing every facet of manufacturing such as casting. 3D printing in particular has the potential to revolutionize castings in terms of precision and time taken in production. Patternless molds increase the efficiency of the casting process for large scale manufactured components. Therefore, ceramic based molds can be utilized for low temperature alloy parts such as mounting brackets. Nowadays, 3D printing technologies allow the direct printing of these molds. This is possible with the aid of CAD modelling of the casting mold which allows instant printing of patternless molds. The aim of this work is to introduce an approach to prepare a 3D design for a casting mold that can be manufactured using 3D printing technology. Mold design was made using Solidworks software according to standardized calculations from which cope and drag components were extracted. Candidates for potential mold material are highlighted along with advantages & limitations of utilizing 3D printing methodology.

Effect of Line Energy Conditions on Mechanical and Fatigue Properties of Ti6Al4V Fabricated by Electron Beam Additive Manufacturing

Additive manufacturing has many process variables and requires additive process optimization. Line energy and scan speed are the main process variables. The objective of this work aims to investigate the effect of changes in line energy and scan speed among the process variables on the mechanical and fatigue properties of the Ti6Al4V specimens fabricated by electron beam additive manufacturing method. The size of the pore inside the specimen was 40~60 μm with the exception of the condition of 0.2 kJ/m, and the specimen with poor fusion of more than 100 μm and gas pore was found to have lower room temperature tensile and fatigue properties compared to the optimal process conditions. As line energy increased, strain hardening occurred, and yield strength and tensile strength increased. The EL:0.3 kJ/m and 800 mm/s condition is a process condition that shows no defects such as unmelted powder and poor fusion, and it represents the best fatigue strength of 400 MPa. The fatigue strength of the specimen performed with hot isostatic pressing after additive manufacturing was measured at 550 MPa, an increase of 150 MPa, which resulted in high fatigue strength enhancement. The crack initiation site and propagation behavior were analyzed by observing the fatigue fracture section of the specimen according to the line energy.

A Cost-Efficient Data-Driven Approach to Design Space Exploration for Personalized Geometric Design in Additive Manufacturing

Abstract Additive manufacturing (AM) is considered as a key to personalized product realization as it provides great design flexibility. As the flexibility radically expands the design space, current design space exploration methods for personalized geometric designs become time-consuming due to the use of physically based computer simulations (e.g., finite element analysis or computational fluid dynamics). This poses a significant challenge in design for an efficient personalized product realization cycle, which imposes a tight computation cost constraint to timely respond to every new requirement. To address the challenge, we propose a cost-efficient data-driven design space exploration method for personalized geometric design in AM, enabling feasible design regions under the computation constraint. Specifically, the proposed method adopts surrogate modeling of efficient voxel model-based design rules to identify feasible design regions considering both manufacturability and personalized needs. Since design rules take much less time for evaluation than physically based simulations, the proposed method can contribute to timely providing feasible design regions for an efficient personalized product realization cycle. Moreover, we develop a cost-based experimental design for surrogate modeling, which enables the evaluation of additional design points to provide more precise feasible design regions under the computation cost constraint. The merits of the proposed method are elaborated via additively manufactured microbial fuel cell (MFC) anode design.

A New General Methodology to Create Continuous Cooling Transformation Diagrams for Materials Fabricated by Additive Manufacturing

Abstract Additive manufacturing (AM) is a manufacturing method that can build high-strength materials layer-by-layer to form complex geometries. Previous studies have reported large variations in the mechanical properties of materials made by this process. One of the key factors that may contribute to variations within and among parts made by this process is a difference in the material’s microstructural phase and composition. A continuous cooling transformation (CCT) diagram is a useful tool that can be used with a thermal model for microstructure design and manufacturing process control. However, traditional CCT diagrams are developed based on slow and monotonic cooling processes such as furnace cooling and air cooling, which are greatly different from the repetitive heating and cooling processes in AM. In this study, a new general methodology is presented to create CCT diagrams for materials fabricated by AM. We showed that the effect of the segmented duration within the critical temperature range, which induced precipitate formation, could be cumulative. As multiple cooling processes occurred in a short time, and the temperature drops at a high cooling rate, a constant average cooling rate was assumed when constructing the CCT diagram. Inconel 718 parts fabricated by selective laser melting were analyzed. The accumulated duration required for γ′/γ″ precipitate formation was found to shift from at least 432 s to about 83.38 s. The large difference in the duration (around 518%) highlights the importance of creating CCT diagrams specifically for AM materials. The key factor contributing to phase transformation was identified as the accumulated duration within the critical temperature range. The presented methodology demonstrated the capability of combining a thermal model and experimental observation to quantitatively predict phase transformation and could be used to design microstructures and control AM processes.

Implicit-based computer-aided design for additively manufactured functionally graded cellular structures

Abstract Additive manufacturing has enabled the fabrication of complex structures such as cellular structures. Although numerous design frameworks have been proposed for cellular structures, their effectiveness was limited owing to the use of B-rep-based representation. To address the limitations in previous research, this study proposes an implicit-based computer-aided design framework customized for additively manufactured functionally graded cellular structures (AM-FGCSs). The proposed design framework effectively aids in both single- and multiscale structural optimization for designing FGCSs. Moreover, implicit-based modeling afforded a reliable geometric representation that could efficiently assist computation tasks such as visualization, validation, and process planning for fabrication. In addition, two case studies were conducted to demonstrate the effectiveness of the proposed framework for designing FGCSs. The first case study on a three-point bending beam design problem proved the practicality of implicit-based representation in multiscale structural design. Meanwhile, the second case study validated the proficiency of the proposed framework in process planning for fabrication and engineering analysis, the two most vital computation tasks in designing cellular structures.

Study of the Surface and Dimensional Quality of the AlSi10Mg Thin-Wall Components Manufactured by Selective Laser Melting

Additive manufacturing (AM), a 3D printing technique that manufactures components by sequential addition of powder, has massively reshaped the manufacturing and engineering sectors from batch production to manufacturing customized, innovative, state-of-the-art, and sustainable products. Additive manufacturing of aluminum alloys by selective laser melting (SLM) is one of the latest research trends in this field due to the fact of its advantages and vast applications in manufacturing industries such as automobiles and aerospace. This paper investigated the surface and dimensional quality of SLM-built AlSi10Mg parts using a response surface method (RSM) and found the influence of the wall thickness and process parameters (i.e., laser power, scanning speed, hatch distance) on the pieces. Thin-walled test specimens of AlSi10Mg alloy were manufactured with different combinations of process parameters at three wall thicknesses: 1.0 mm, 2.0 mm, and 3.0 mm. The Minitab DOE module was used to create 27 different configurations of wall thickness and process parameters. The samples’ surface roughness and dimensional accuracy were investigated, and the findings were evaluated using the ANOVA technique. The regression model and the ANOVA technique showed high precision and had a particular reference value for practical engineering applications.

Multiscale topology optimisation with nonparametric microstructures using three-dimensional convolutional neural network (3D-CNN) models

ABSTRACT Additive manufacturing enables the fabrication of parts with complex geometries, thereby opening up the design space from part scale to microarchitecture scale. By optimising the structure in the expanded design space, structural performance can be improved. Topology optimisation is commonly used as the tool to optimise the structures according to specific application requirements. However, multiscale topology optimisation can be computationally expensive and with limited choices in microscale structures. Therefore, we propose a surrogate model based on three-dimensional convolutional neural networks (3D-CNN) to model the effective elasticity tensor and its gradients for general voxel-based nonparametric microstructures. The proposed 3D-CNN-based surrogate model greatly extends the flexibility over existing surrogate-based methods which can only be applied in relatively simple parametric microstructures. Given the microscale structure, the proposed 3D-CNN-based model can effectively predict its material properties. Furthermore, being able to estimate the gradient of the material properties with respect to microscale structure changes makes the proposed 3D-CNN-based surrogate readily adaptive to existing multiscale topology optimisation frameworks. Through extensive simulations, by comparing with both SIMP and existing surrogate-based methods, we demonstrate the advantages of the proposed 3D-CNN-based surrogate model.

An Integrated Computational Materials Engineering Predictive Platform for Fatigue Prediction and Qualification of Metallic Parts Built With Additive Manufacturing

Abstract Additive manufacturing (AM) processes create material directly into a functional shape. Often the material properties vary with part geometry, orientation, and build layout. Today, trial-and-error methods are used to generate material property data under controlled conditions that may not map to the entire range of geometries over a part. Described here is the development of a modeling tool enabling prediction of the performance of parts built with AM, with rigorous consideration of the microstructural properties governing the nucleation and propagation of fatigue cracks. This tool, called DigitalClone® for additive manufacturing (DCAM), is an Integrated Computational Materials Engineering (ICME) tool that includes models of crack initiation and damage progression with the high-fidelity process and microstructure modeling approaches. The predictive model has three main modules: process modeling, microstructure modeling, and fatigue modeling. In this paper, a detailed description and theoretical basis of each module is provided. Experimental validations (microstructure, porosity, and fatigue) of the tool using multiple material characterization and experimental coupon testing for five different AM materials are discussed. The physics-based computational modeling encompassed within DCAM provides an efficient capability to fully explore the design space across geometries and materials, leading to components that represent the optimal combination of performance, reliability, and durability.

Features of the Formation of Structure and Geometry during Direct Laser Deposition of 321L Stainless Steel on Complex Groove Using the LENS Method

Additive manufacturing is a new approach to solving the problems of creating high-performance instrument components. This article is devoted to the solution of the narrowly directed problem of surfacing metal powder 321L using the LENS method on the solenoid valve core. The core is made of electrical steel, in which a complex groove is machined. The difficulty of this work was to accurately perform the geometry of the melt layer that is not standard for this method. As a result, the product was obtained according to a completely new approach to its design, which subsequently gave increased operational characteristics of the final product. The article presents the problems that can be encountered when performing complex crevice surfacing on a complex groove. The structure of the deposited layer is considered, defects characteristic of this method in the form of interlayer pores, unalloyed powder particles are identified and described. The LENS process control modes are presented in order to reduce defects and improve the development of surfacing geometry.

Fatigue Performance of Polyamide 12 Additively Manufactured Structures Designed with Topology Optimization

Abstract Additive manufacturing technologies can produce structures with complex geometry, generally as time goes on at higher levels of quality and repeatability. Complex geometry is attractive because it allows for mass redistribution to structural regions where stress is highest, which is comparatively advantageous to solid section designs. Additively manufactured structures, however, are sensitive to stress concentrations either from geometry or defects reducing the overall strength and fatigue performance. It is, therefore, important to address stress concentrations from a design perspective, formulating a process that ensures fatigue performance. A topology optimization process is presented for designing a beam subjected to three-point loading with objectives to reduce structural volume/mass while representing the expected fatigue life as a function of the stress. The design process produced structures with between 28 and 67 % of the original volume of material for a given volumetric constraint with material redistributed to reduce stress concentrations. Two designs were fabricated of polyamide 12 using selective laser sintering and tested mechanically to failure under quasistatic and cyclic fatigue loading. Experimentally, digital image correlation was used to observe the onset of failure and strain response in the structure, which showed that vertical members typically failed by buckling and material yielding. Load, displacement, cycles to failure, and heat generation were measured during fatigue loading, capturing significant viscoelastic self-heating and plasticity behavior in the vertical structural members.

Wireless Ventilation Measurement in 3D Printed Sand Molds

Additive Manufacturing is enabling the casting of complex geometries directly from digital design data, including 3D-scanned and reverse-engineered structures and even functionally graded lattices. By ink jetting binder into a bed of sand layer-by-layer, dimensionally precise sand molds and cores can be printed to serve as soft tooling for sand casting. However, the related increase in geometry complexity can lead to challenges in ensuring casting quality and yield. One recently explored remedy is to introduce sensors (the Internet of Things) to enable the collection of a diversity of data at difficult-to-access locations in molds in order to measure temperature, pressure, moisture, and core shift. This effort has explored measuring barometric pressure at strategic locations to evaluate the ventilation design of internal cores. Optimized and measurable ventilation can be leveraged to improve the quality of castings by reducing porosity and improving surface finish. By measuring the pressures that accumulate within cores due to binder decomposition, new ventilation designs and strategies—enabled with complex, 3D printed fluidic channels—can be explored. In this work, two castings with different metal temperatures were poured and internal pressures were measured and compared to simulations demonstrating that wireless disposable sensors can be used to measure pressure and that the measured pressure correlated with venting strategies now possible with 3D printed sand cores.

Surface modification of additively manufactured parts by forming

Additive manufacturing (AM) has many advantages compared to conventional processes. Of particular interest is the tool-less manufacturing of components, which allows one component to differ from the next completely and has the possibility of producing complex geometries. At the same time, however, AM has deficits such as long production times, low production tolerances and low surface qualities compared to conventional processes. Therefore, a finishing process using machining is often necessary, which extends the manufacturing time and produces waste. Thus, the avoidance of machining rework is of high interest, especially with expensive materials such as stainless steel or titanium. One approach to avoid machining processes is to use forming technology. By applying a forming operation, surfaces can be smoothened and geometrical aspects can be defined more sharply. Especially for functional surfaces, this procedure is favorable because of the work hardening, which in turn increases the strength of the material. Using the example of laser-based powder bed fusion (PBF-LB) followed by a cup backward extrusion process, two materials, which are frequently used in AM are investigated. On the one hand, the titanium alloy Ti-6Al-4V, as a material with low machinability and low formability at room temperature, and the stainless steel 316 L. Compared to Ti-6Al-4V, 316 L has a higher formability. Cylinders are built using PBF-LB and then formed to smoothen the surface and achieve a higher geometrical accuracy concerning edges. Formed, additively made parts have a more defined geometry, namely sharp edges and a surface roughness reduced by up to 90 %.

Ceramics Processing by Additive Manufacturing

Additive manufacturing (AM) can be a game changer of ceramic industry by opening up new avenues in terms of reduction in cost and ushering in to the domain of designing complicated structure without having dependence on exotic tools. Unlike polymers and metals where AM technology is growing up rapidly, growth of the same in ceramic industry is rather slow due to several technical challenges. However, AM of ceramics is growing steadily as there is considerable urge for producing fully dense defect-free ceramic components with complex geometry for which AM technology comes very handy. Consequently, many AM technologies have been used to fabricate ceramic components with varying results depending upon the nature of the material, AM technology, and part complexity. Thus, beside the nature of the processed ceramic, it is of paramount interest to select right kind of AM process for a particular application in terms of complexity, size density, and surface finish. This review gives present status in AM of ceramics along with some relevant properties of parts produced by AM technology followed by challenges and future prospects of ceramic AM.

Topology and FEA modeling and optimization of a patient-specific zygoma implant

Additive manufacturing has proven to be a very beneficial production technology in the medical and healthcare industries. While existing for over four decades, recent work has seen great improvements in the quality of products; particularly in medical devices such as implants. Improved customization reduced operating time and increased cost-effectiveness associated with Metal AM for these products offers a new value proposition. This paper investigates and evaluates modelling methods for the zygoma bone (human jawbone) and explores the most suitable material and optimum design for this critical biomedical implant. This paper proposes an innovative and efficient pre-process methodology that includes modelling, design validation, topological optimization, and numerical analysis. The method includes the generation of the model using reverse engineering of CT scan data and a topology optimization technique which makes the implant lightweight without causing excessive stress concentration. Static structural Finite Element Analysis was conducted to test three different biocompatible materials (Ti6Al4V, stainless steel 316L and CoCr alloys) which are commonly available for metal additive manufacturing. The stresses and conditions in the analysis were that of the human mastication process and all the implant design were tested with the three material types. The Taguchi method was used to determine the optimum design which was found to result in the highest mass reduction of 25% with Ti6Al4V as the implant material.

Increasing Throughput in Fused Deposition Modeling by Modulating Bed Temperature

Abstract Additive manufacturing (AM) techniques, such as fused deposition modeling (FDM), are able to fabricate physical components from three-dimensional (3D) digital models through the sequential deposition of material onto a print bed in a layer-by-layer fashion. In FDM and many other AM techniques, it is critical that the part adheres to the bed during printing. After printing, however, excessive bed adhesion can lead to part damage or prevent automated part removal. In this work, we validate a novel testing method that quickly and cheaply evaluates bed adhesion without constraints on part geometry. Using this method, we study the effect of bed temperature on the peak removal force for polylactic acid (PLA) parts printed on bare borosilicate glass and polyimide (PI)-coated beds. In addition to validating conventional wisdom that bed adhesion is maximized between 60 and 70 °C (140 and 158 °F), we observe that cooling the bed below 40 °C (104 °F), as is commonly done to facilitate part removal, has minimal additional benefit. Counterintuitively, we find that heating the bed after printing is often a more efficient process for facile part removal. In addition to introducing a general method for measuring and optimizing bed adhesion via bed temperature modulation, these results can be used to accelerate the production and testing of AM components in printer farms and autonomous research systems.

Additive Manufacturing of 316L Stainless Steel by a Printing-Debinding-Sintering Method: Effects of Microstructure on Fatigue Property

Abstract Additive manufacturing (AM) technology has been broadly applied to the fabrication of metallic materials. However, current approaches consume either high energy or large investment that considerably elevates their entry threshold. An economic extrusion-based AM method followed by debinding and sintering could efficiently produce the metal parts with relatively low cost and high material utilization. However, an in-depth analysis of the fatigue performance of the component built by such a technology has been little documented so far. Herein, the 316L stainless steel was fabricated throughout the printing-debinding-sintering (PDS) pathway and its fatigue properties were comprehensively assessed. Tensile and flexural fatigue tests were conducted to reveal the fatigue strength and fractural behaviors under different loading conditions, while the fatigue crack growth (FCG) test was performed to quantify the crack propagation. The results indicated the number of 105 cycles can be reached for the tensile specimens under the fatigue loading of 120 MPa, whereas 1.37 × 105 cycles were endured by the flexural specimens under 150 MPa. The fractural morphology indicated an adverse impact of the pore-induced voids on the tensile fatigue crack propagation, but such a drawback could be alleviated in the flexural loading condition. The FCG test unveiled the crack growth rate with the number of cycles and determined the material-related coefficients in the fatigue crack growth model. The research findings provided valuable insights into the effects of the PDS process and microstructures on the resultant fatigue properties of the metal component.