Additive Manufacturing Process Parameters Research Articles

Nondestructive investigation on close and open porosity of additively manufactured parts using an X-ray computed tomography

Additive manufacturing (AM) has been gradually adopted by different industrial sectors including aerospace, automotive and medical industries etc. The layer by layer AM process has its unique advantage to fabricate complex parts using a computer aided design. Selective laser melting (SLM) and binder jet printing (BJP) are two important methods in AM family. SLM and BJP introduce closed pores affecting AM parts’ mechanical property and open pores affecting gas purification while AM part as a filter, respectively. It is important to quantify those internal pores to optimize AM process parameters’ setting. In this paper, a close porosity (CP) of AM parts by SLM with remelting process and different printing parameters (laser power and scanning speed) and an open porosity (OP) of AM parts by BJP have been characterized. X-ray computed tomography (XCT) has been successfully employed to inspect CP and OP related to either closed pores or open pores in AM parts accordingly. It is found that: 1) For given parts by SLM, the CP values can be varied in a range from 0.07% to 0.34%, subjected to different printing parameters’ setting and 2) For given parts by BJP, the OP value can be controlled over 50% for gas purification. It demonstrated that XCT is capable of conducting non-destructive quality testing and related printing parameters optimization in metallic AM.

Process parameter optimization of metal additive manufacturing: a review and outlook

The selection of appropriate process parameters is crucial in metal additive manufacturing (AM) as it directly influences the defect formation and microstructure of the printed part. Over the past decade, research efforts have been devoted to identifying "optimal" processing regimes for different materials to achieve defect-free manufacturing, which mostly involve costly trial-and-error experiments and computationally expensive mechanistic simulations. Hence, it is apropos to critically review the methods used to achieve the optimal process parameters in AM. This work seeks to provide a structured analysis of current methodologies and discuss systematic approaches toward general optimization work in AM and the process parameter optimization of new AM alloys. A brief review of process-induced defects due to process parameter selection is given and the current methods for identifying "optimal processing windows" are summarized. Research works are analyzed under a standard optimization framework, including the design of experiments and characterization, modelling and optimization algorithms. The research gaps that preclude multi-objective optimization in AM are identified and future directions toward optimization work in AM are discussed. With growing capabilities in AM, we should reconsider what the definition of the "optimal processing region".

Influence of process parameters in additive manufacturing of highly reinforced 316L / SiCp composites

Highly reinforced 316 L / SiCp metal matrix composites (MMC) walls have been additively manufactured by Direct Laser Deposition (DLD) using mixtures with different percentages of 316 L stainless steel powder and SiC particles (SiCp). The effect of the percentages of SiCp of up to 80 wt.% and DLD conditions on the geometry, microstructure, and hardness of the fabricated structures have been analyzed. SiC particles react with the molten 316 L steel and chromium and iron carbides are formed, and also graphite flakes and nodules are formed in the matrix. This reaction is also responsible for the dilution of the SiCp, which lose their initial shape and size. The incorporation of higher percentages of SiCp reduces the degradation of the particles as the matrix saturates in Si and C, and also enhances the size of the graphite precipitated phases. The different phases formed help to increase the hardness of the composites, and a maximum value of 1085 HV 0.1 was obtained for 40 vol.% of SiCp. Increasing further the amount of SiCp reduced the hardness to values of ∼890 HV 0.1 as the matrix is saturated in carbides and bigger and more brittle graphite precipitates are formed. The characteristics of the manufactured samples for the different reinforcement percentages used and for the different manufacturing conditions have been analyzed. From this study, the DLD processing maps for the 316 L/SiC MMC metal matrix composites with different content of reinforcement have been developed, and the optimal values of laser power, scanning speed, and layer height have been determined, and it has been observed that the optimum fabrication conditions displace to lower scanning speed for higher SiCp contents in the 316 L matrix. Showing that it is possible to manufacture cermets based on steel with lower density and greater hardness than the steel used.

A Survey on Additively Manufactured Nanocomposite Biomaterial for Orthopaedic Applications

The sturdy demands in orthopaedics are not yet fulfilled, in both bone replacement and joint substitution and also in other repairs of bone defects. Metals are used for bone repair and replacement for a long period of time instead of biomaterial. Most of the countries are still now practising bone replacement with metals like titanium and stainless steel only due to the lag of technology advancement. A lot of research and development paves the way to composite biomaterial, as it is evolving in the domain of treatment of orthopaedics. There was massive research undergoing in the biomaterial field recently, and various methods of fabrication have been tested and implemented. Nanocomposites provide a higher surface‐to‐volume ratio, surface chemistry and nanoscale reinforcement, flexible production techniques, good corrosion and erosion resistance, and cheaper costs. The nanocomposite biomaterials were intended for biomedical purposes. Because of the intricacy of biological structures (tissue or organ) and additive manufacturing techniques for tissue engineering, scaffolding has wide scope for instant remedies in bone implant. The design restrictions and physical attributes of fast prototyping structures are then evaluated in terms of input factors like design elements, material choices, and additive manufacturing processing parameters. As a result of this survey, the needs and application importance of additively manufactured implants for the regeneration of various biomaterial types as well as the attempts undertaken to mitigate their medical impairment are suggested.

Effect of additive manufacturing process parameters on the titanium alloy microstructure, properties and surface integrity

Damage initiation and propagation are widely impacted by pores distribution induced during additive manufacturing (AM) processes. More specifically, the porosity can strongly affect the thermomechanical behavior of titanium alloys in the case of Selective Laser Melting (SLM). The present work examined the impact of SLM process parameters on microstructure characteristics, mechanical properties, and surface integrity of the Ti6Al4V titanium alloy. Different metallographic analyses (i.e., Scanning Electron Microscopy (SEM), and X-Ray Diffraction (XRD)), density measurement and surface characterizing were carried out to investigate the effect of various process parameters (laser power and scanning speed) on the damage behaviors in terms of fracture surface and cracks initiation sites of the alloy.

Study on the Influence of Additive Manufacturing Process Parameters on Internal-channel Part Geometrical Quality

Nowadays Additive Manufacturing processes are increasingly utilized for manufacturing parts with complex shapes in numerous fields such as internal-channel parts in aerospace and automobile industries. However, the geometrical quality of internal channel is still an unsolved problem. In order to provide guidance for the manufacturing of internal-channel parts via Selective Laser Melting process, this paper studies the influence of process parameters, such as build orientation, laser power, scanning speed, layer thickness and scanning strategy. A group of simulations are carried out with Design of Experiments, and the results show the build orientation is the parameter with significant impact. The impact trend and the optimal parameter combination for internal-channel part geometrical quality are analyzed, which is beneficial for high-fidelity internal-channel parts manufacturing.

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.

A dimensionless number for high-throughput design of multi-principal element alloys in directed energy deposition

The present study demonstrates the development of a dimensionless number to predict the build height in the additive manufacturing technique of directed energy deposition (DED). The build height can also be used to estimate the dendrite arm spacing and, thus, the cooling rate in the fabrication of samples. A baseline sample, 316L stainless steel, was used to fit the build height to the dimensionless number. A range of process parameters, including laser power, laser feed rate, powder flow rate, layer thickness, and hatch spacing, were varied. Based upon dendrite arm spacing, the estimated cooling rate varied between 102 and 104 K/s. Using the fitted relationship for the stainless steel, high-throughput (HT) processing of multi-principal element alloys (MPEAs) was performed. For this study, HT is the ability to fabricate a batch of 25 bulk samples (∼1 cm3) with different compositions within a 5-h period with ±10 at. % accuracy. A range of compositions using in situ alloying of elemental powders in the Fe–Ni–Cr–Mo system were made. The MPEAs' build height followed the same relationship to the dimensionless number as the 316L alloy. The dimensionless number predicts both macro and meso-scale features in HT processing, thus offering a design tool for choosing process parameters in DED additive manufacturing. Also, the ability to control or increase cooling rates can enhance the ability to promote metastability as well as control meso-scale chemical distributions of alloy samples.

Kinetic interface condition phase diagram for the rapid solidification of multi-component alloys with an application to additive manufacturing

Solid-liquid Interface response function or the analysis of kinetic interfacial conditions under rapid solidification setting occupies a central role in solidification research. With the concepts of solute drag and solute trapping and on the base of irreversible thermodynamics, the calculation of kinetic interface condition phase diagram for binary alloys has been well established owing to the continuous growth model by Aziz et al. Motivated by the fast development of various additive manufacturing (AM) technologies, clear needs to extend the kinetic phase diagram calculation towards multi-component alloy systems emerge to meet the challenges of processing parameter optimization encountered under those sub-rapid or rapid AM solidification conditions for new alloys. In this paper the irreversible thermodynamics analysis and the binary continuous growth model are reformulated into a form suitable for the coupling with the multi-component CALPHAD databases. Then the numerical solution to the CALPHAD-coupled multi-component model is described. The model and its numerical solution are verified by comparing its calculation results with those reported in literature for A-B hypothetical ideal solution phases, Ag–Cu and Al–Be alloys. The model predictive power is demonstrated by calculating the kinetic diagram of Al–Ti, Fe–Cr–Ni and Al–Cu–Mg–Si–Zn alloys. To illustrate one of the practical values of the proposed model, kinetic growth restriction factor is calculated from the predicted Al–Ti and Al–Cu–Mg–Si–Zn kinetic phase diagram. It is concluded that the proposed multi-component model and its numerical solution can calculate kinetic phase diagram of any multi-component alloys. Moreover, the proposed model can be used in evaluating solute effect for grain refinement under sub-rapid or rapid solidification conditions, which is great value to understand the solidification phenomenon in AM. The model is expected to be useful in many scenarios to guide the optimization of AM processing parameters and alloys design.

A High-Throughput Method to Define Additive Manufacturing Process Parameters: Application to Haynes 282

A High-Throughput Method to Define Additive Manufacturing Process Parameters: Application to Haynes 282

Evaluation of a modified void descriptor function to uniquely characterize pore networks and predict fracture-related properties in additively manufactured metals

Subtle differences among additive manufacturing (AM) processing parameters lead to variations in pore networks and complicate the prediction of void-sensitive mechanical behaviors, including location of fracture. The current work expands upon a recently developed pore metric, the void descriptor function (VDF), by accounting for interactions among neighboring pores and stress concentrations induced by non-spherical pores or voids. The modified VDF is evaluated against 120 computationally generated fracture simulations and six physical tensile specimens of as-built laser powder bed fused IN718. The latter set of experiments, which include X-ray computed tomography measurements before and after deformation, enables evaluation against pore populations that are representative of defects commonly observed in AM metals. The modified VDF accurately predicts fracture location (within ±5% tolerance) for 94 out of 120 simulated specimens, representing 3.3%, 62.1%, and 59.3% increases in the number of accurate predictions in comparison to predictions based on the original VDF, the location of maximum cross-sectional area reduction, and the largest-pore location, respectively. In the experimental data set, the modified VDF accurately predicts the location of fracture in five out of six specimens compared to only two out of six using the original VDF, maximum cross-sectional area reduction, or largest-pore location. Also, the maximum value of the modified VDF was found to be more highly correlated than fraction porosity, pore size, reduced-cross section area, and total number of pores to the ultimate tensile strength, elongation to failure, and toughness modulus, suggesting that the modified VDF presented in this work could serve as a promising metric to assist with characterizing unique pore networks and predicting fracture-related properties in AM components.

Effect of build orientation and raster pattern on the fracture behavior of carbon fiber reinforced polymer composites fabricated by additive manufacturing

Additive manufacturing (AM) process parameters highly affect the mechanical and fracture properties of additively manufactured carbon fiber reinforced polymer (CFRP) composites. In this work, we study the effect of two process parameters, including build orientation (XYZ, XZY, and ZXY) and raster pattern ( ± 45º and 0º/90º), on the fracture behavior of the CFRP composites fabricated by fused filament fabrication (FFF). The mode I fracture toughness and effective fracture energy of the CFRP composite materials are measured on compact tension samples. The crack growth direction in the FFF-fabricated CFRP composites is found to be controlled by both build orientation and raster pattern. The fracture toughness of the examined CFRP material is sensitive to build orientation, while the effective fracture energy reveals significant dependence on both build orientation and raster pattern. The samples fabricated with the XYZ and ZXY build orientations exhibit 5-fold greater effective fracture energy than the ones with the XZY build orientation. A wide range of fracture energy dissipation mechanisms, including fiber-bridging and fiber pull-out, are identified depending on the process parameters. The reported fracture trends in this study have the potential to provide guidelines for the development of FFF-fabricated CFRP composites with high fracture resistance.

Breaking the mould: achieving high-volume production output with additive manufacturing

PurposeThe study aims to examine a discrepant industrial case that demonstrates how to achieve economies of scale with additive manufacturing (AM), thereby expanding the scope of AM beyond high-variety, customised production contexts.Design/methodology/approachAbductive reasoning is applied to analyse a case of using AM to compete with conventional production, winning a contract to supply 7,700,000 products. Comparing this case to existing theories and contemporary practices reveals new research directions and practical insights.FindingsEconomies of scale were realised through a combination of technological innovation and the adoption of operations management practices atypical of AM shops (e.g. design for volume, low-cost resource deployment and material flow optimisation). The former improved AM process parameters in terms of time, cost and dependability; the latter improved the entire manufacturing system, including non-AM operations/resources. This system-wide improvement has been largely overlooked in the literature, where AM is typically viewed as a disruptive technology that simplifies manufacturing processes and shortens supply chains.Originality/valueIt is empirically shown that an AM shop can achieve economies of scale and compete with conventional manufacturing in high-volume, standardised production contexts.

Modelling of Microstructure Formation in Metal Additive Manufacturing: Recent Progress, Research Gaps and Perspectives

Microstructures encountered in the various metal additive manufacturing (AM) processes are unique because these form under rapid solidification conditions not frequently experienced elsewhere. Some of these highly nonequilibrium microstructures are subject to self-tempering or even forced to undergo recrystallisation when extra energy is supplied in the form of heat as adjacent layers are deposited. Further complexity arises from the fact that the same microstructure may be attained via more than one route—since many permutations and combinations available in terms of AM process parameters give rise to multiple phase transformation pathways. There are additional difficulties in obtaining insights into the underlying phenomena. For instance, the unstable, rapid and dynamic nature of the powder-based AM processes and the microscopic scale of the melt pool behaviour make it difficult to gather crucial information through in-situ observations of the process. Therefore, it is unsurprising that many of the mechanisms responsible for the final microstructures—including defects—found in AM parts are yet to be fully understood. Fortunately, however, computational modelling provides a means for recreating these processes in the virtual domain for testing theories—thereby discovering and rationalising the potential influences of various process parameters on microstructure formation mechanisms. In what is expected to be fertile ground for research and development for some time to come, modelling and experimental efforts that go hand in glove are likely to provide the fastest route to uncovering the unique and complex physical phenomena that determine metal AM microstructures. In this short Editorial, we summarise the status quo and identify research opportunities for modelling microstructures in AM. The vital role that will be played by machine learning (ML) models is also discussed.

Machine learning in predicting mechanical behavior of additively manufactured parts

Although applications of additive manufacturing (AM) have been significantly increased in recent years, its broad application in several industries is still under progress. AM also known as three-dimensional (3D) printing is layer by layer manufacturing process which can be used for fabrication of geometrically complex customized functional end-use products. Since AM processing parameters have significant effects on the performance of the printed parts, it is necessary to tune these parameters which is a difficult task. Today, different artificial intelligence techniques have been utilized to optimize AM parameters and predict mechanical behavior of 3D-printed components. In the present study, applications of machine learning (ML) in prediction of structural performance and fracture of additively manufactured components has been presented. This study first outlines an overview of ML and then summarizes its applications in AM. The main part of this review, focuses on applications of ML in prediction of mechanical behavior and fracture of 3D-printed parts. To this aim, previous research works which investigated application of ML in characterization of polymeric and metallic 3D-printed parts have been reviewed and discussed. Moreover, the review and analysis indicate limitations, challenges, and perspectives for industrial applications of ML in the field of AM. Considering advantages of ML increase in applications of ML in optimization of 3D printing parameters, prediction of mechanical performance, and evaluation of 3D-printed products is expected.

Material extrusion and sintering of binder-coated zirconia: Comprehensive characterizations

Material extrusion processes have been increasingly employed in the fabrication of advanced ceramics in the aerospace, automotive, and biomedical fields. Such processes—which involve printing compositions containing ceramic powders and sacrificial binder, debinding the binders, and sintering the parts to obtain pure ceramics—can be used to produce more complex structures than traditional ceramic manufacturing techniques. In this study, a rod-shaped feedstock comprised of binder-coated zirconia containing 87 wt% zirconia was supplied to a motorized piston extruder in a customized 3D printer to fabricate green ceramic structures, and the structures were then subjected to debinding and sintering. A comprehensive set of characteristics were examined: the thermal and rheological properties of the feedstock, the printability of the feedstock, the influence of the layer thickness and raster angle on the surface roughness and flexural/tensile/compressive strengths of the sintered zirconia, Vickers hardness, relative density and porosity, shrinkage behavior, and the micro/macro structure of green and sintered 3D-printed zirconia structures. The mechanical properties and relative density were found to increase as the layer thickness decreased. The maximum flexural, tensile, and compressive strengths of the sintered zirconia were 435, 36.13, and 689 MPa, respectively, and the relative density was 98.85% at a layer thickness of 200 µm. The comprehensive characteristics of 3D printed green and sintered zirconia that were obtained in this study can facilitate the successful 3D printing of ceramics. Moreover, the characterization contributes to an understanding of the effects of additive manufacturing process parameters on the physical and mechanical properties of ceramic materials and can aid in optimizing the performance and properties of 3D printed ceramic structures. • Comprehensive characterizations of green and sintered zirconia parts was conducted. • A binder-coated zirconia was developed for material extrusion additive manufacturing. • A piston-based extrusion process was developed to fabricate highly dense zirconia parts. • Defect-free zirconia structures were produced using solvent and thermal debinding, followed by sintering.

Quantitative distribution characterization of gradient composition of additive-manufactured stainless steel using micro-beam X-ray fluorescence

The fabrication of the compositionally graded metals provides a new frontier in additive manufacturing and the characterization of the elemental distribution of graded materials is strongly desired for the quality control of powder mixing and improvement of additive manufacturing process parameters. In this study, Micro-beam X-ray fluorescence (μ-XRF) was used to characterize the elemental distribution of additive-manufactured compositionally graded stainless steel on a large area. A poly-capillary X-ray optics unit was used to obtain a high spatial resolution with the spot size of 20-μm. The quantitative distribution characterization of Ni, Mo, Cr, Mn, Si and Cu elements in the whole deposition direction have been realized through the improvement of μ-XRF quantitative calibration method by using certified reference materials with composition and microstructure similar to the graded sample. The composition distribution variation in different cladded layer was also investigated with the pixel distance decreased to 20 μm. Moreover, laser-induced breakdown spectroscopy was used to analyze the composition distribution of the composition-graded steels to verify the reliability of quantitative distribution characterization results by μ-XRF method. The elemental distribution results from two methods have good agreement, but the results determined by μ-XRF can give more details about the composition distribution in different cladded layers because of its higher spatial resolution, which is closely related to additive manufacturing process. The microstructure content varied linearly with the change of composition. The austenite volume fraction increased with an increase in Ni content and the evolution rule of the structure agreed with the calculated thermodynamics model results. Cracks appeared in the martensite and austenite transformation region with a large volume change when the Ni content ranged between 6.5% and 8.5% for the gradient stainless steels.

Phase Composition of Austenitic Stainless Steels in Additive Manufacturing: A Review

Additive manufacturing (AM) is among the novel industrial technologies for fast prototyping of complex parts made from different constructional and functional materials. This review is focused on phase composition of additively manufactured chromium-nickel austenitic stainless steels. Being produced by conventional methods, they typically have single-phase austenitic structure, but phase composition of the steels could vary in AM. Comprehensive analysis of recent studies shows that, depending on AM technique, chemical composition, and AM process parameters, additively manufactured austenitic stainless steels could be characterized by both single-phase austenitic and multiphase structures (austenite, ferrite, σ-phase, and segregations of alloying elements). Presence of ferrite and other phases in AM steels strongly influences their properties, in particular, could increase strength characteristics and decrease ductility and corrosion resistance of the steels. Data in review give a state-of-art in mutual connection of AM method, chemical composition of raw material, and resultant phase composition of AM-fabricated Cr-Ni steels of 300-series. The possible directions for future investigations are discussed as well.

Correlations between Process and Geometric Parameters in Additive Manufacturing of Austenitic Stainless Steel Components Using 3DPMD

The additive manufacturing of components is characterized by a layered build-up. The stability of the build-up process with regard to the component geometry and the layer thickness is essential for the success of the entire system. A prerequisite for this is the exact knowledge of the interrelationships between the process, construction parameters and the resulting component geometry, respectively. These correlations are determined within the study using the 3D Plasma Metal Deposition Process (3DPMD). For this purpose, the process is first subjected to a system analysis. Possible influencing variables were identified with regard to the question “Which system parameters influence the component geometry?” and then prioritized. Then, the influence of control factors (welding current intensity, welding speed, and powder mass flow) was investigated according to the specifications of the Design of Experiments (DOE) method by means of a full-factorial experimental design and evaluated on the basis of metallographic cross-sections. As a result, it was determined that the system parameter powder mass flow only influences the layer thickness and not the wall thickness and is, therefore, available as a process control variable. In sum, comprehensive knowledge of complex relationships between the control parameters and the component geometry in additive manufacturing using 3DPMD was achieved and forms the basis for further scientific work.

Artificial neural network (ANN) based prediction of process parameters in additive manufacturing

In recent years, selective laser melting (SLM), a part of additive manufacturing (AM) is one of the most encouraging ones that permit fabricating metallic parts from metal powder with complex geometry. Diversities in thesecycle boundaries become an imperative system to improve the nature of the outcome.Cycleboundaries, for example, laser power, scan speed, hatch spacing, layer height used as input parameters and have a significant impact on the mechanical property taken as an output parameter of the manufactured part. The Artificial Neural Network (ANN) model includes a multi-layer perceptron (MLP) learning algorithm named as Levenberg-Marquardt and tangent sigmoid function consider as preparing and testing functions respectively utilizing MATLAB toolkit. Ideal cycle boundaries are attained dependent on the mean square error function (MSE) and correlation coefficient(R2).