Metal Additive Manufacturing Processes Research Articles

Tribo-technological features of laser powder bed fusion process: scratch and wear resistance of AlSi10Mg aluminium alloy

Mechanical systems, regardless of their complexity, very often require that different parts must move relative to each other by sliding their surfaces, therefore appropriate tribological properties are needed. This request appears particularly evident for components fabricated through Metal Additive Manufacturing processes, due to their typical high surface roughness. In the current study, the Laser Powder Bed Fusion technique with optimized parameters is used to produce samples made of AlSi10Mg alloy. Their tribo-technological properties are investigated through progressive load scratch and dry ball-on-plate wear tests. Along with a global characterization, a local analysis has been performed to identify any variations induced by the building direction. The friction coefficient and the wear rate are generally higher than as-cast specimens. Finally, local trends suggest that the central parts of the samples average offer higher resistance to wear and scratch than the outer areas.

Assessment of local mechanical properties of laser powder bed fused aluminium alloy by non-destructive testing based on FIMEC indentation

Laser powder bed fusion process is a versatile metal additive manufacturing process. Although significant progress has been made so far, there is still limited large-scale adoption of this technique by the industry. The main problems are repeatability and lack of proper knowledge. In this work, an innovative and non-destructive testing methodology, based on flat-top cylinder indentation, was used to define the mechanical properties of laser powder bed fused aluminium alloy to highlight any variations induced by the combination of process parameters, for global characterization, and by the building direction, for local characterization. Results show similar or improved global mechanical properties of the laser powder bed fused specimens when compared to traditional die-casted ones. Indentation tests highlight a local dependence of properties along the building direction in favor of the upper part of the samples.

An image-based nonparametric multivariate EWMA control chart for metal additive manufacturing process monitoring

Additive manufacturing offers considerable promise in terms of manufacturing flexibility, but print quality remains a major obstacle to its widespread adoption. Consequently, timely and accurate detection of process variations or anomalies is critical for implementing corrective actions. Melt pool characteristics are crucial to final product quality, and current state-of-the-art studies on melt pool image flow have primarily focused on in-situ sensing, feature extraction, and their relationship with process settings and material properties. This study aims to develop a statistical process control approach to detect process variations immediately using a predefined distribution of monitoring statistics. Two main challenges are addressed: 1) the unknown distribution of melt pool image features, rendering traditional parametric control charts unreliable and 2) the autocorrelation of melt pool image flow features, violating the traditional control chart assumption of independent and identically distributed monitoring data. To overcome these challenges, we propose a multivariate nonparametric Exponential Weighted Moving Average (EWMA) control chart that can appropriately accommodate stationary serial data correlation to monitor process stability by tracking physical features extracted from the original images. Key features of the proposed control chart include its ability to eliminate serial correlation effects and its distribution-free nature, requiring no prior information about the data distribution. Comparative analysis of simulated and real data demonstrates that the proposed approach outperforms baseline methods in anomaly detection accuracy. An actual case study in laser metal deposition (LMD) further validates the effectiveness of the proposed method.

Design and Assessment of an Austenitic Stainless Alloy for Laser Powder Bed Additive Manufacturing

Recent developments in metallic additive manufacturing (AM) processes for the production of high-performance industrial pieces have been hampered by the limited availability of reliably processable or printable alloys. To date, most of the alloys used in AM are commercial grades that have been previously optimized for different manufacturing techniques. This study aims to design new alloys specifically tailored for AM processes, to minimize defects in the final products and to optimize their properties. A computational approach is proposed to design novel and optimized austenitic alloy compositions. This method integrates a suite of predictive tools, including machine learning, calculation of phase diagrams (CALPHAD) and physical models, all piloted by a multi-objective genetic algorithm. Within this framework, several material-dependent criteria are examined and their impact on properties and on the occurrence of defects is identified. To validate our approach, experimental tests are performed on a selected alloy composition: powder is produced by gas atomization and samples are fabricated by laser powder bed fusion. The microstructure and mechanical properties of the alloys are evaluated and its printability is compared with a commercial 316L stainless steel taken as a reference. The optimized alloy performs similarly to 316L in terms of coefficient of thermal expansion, hardness and elongation, but has a 17% lower yield strength and ultimate tensile strength (UTS), indicating that further optimization is required.

Thermal Conductivity of 3D Printed Metal using Extrusion-based Metal Additive Manufacturing Process

Abstract In this study, the thermal conductivity of 3D-printed 316L stainless steel parts using the bound metal deposition (BMD) method, an extrusion-based 3D printing technology, were examined experimentally and validated numerically using finite element analysis (FEA). Various critical printing parameters were examined, including infill density, skin overlap percentage, and print sequence to study their effect on the printed thermal conductivity. A heat conduction experiment was performed on the 3D-printed samples of 316L stainless steel followed by a FEA. The results from this investigation revealed that an increase in 3D printing infill density correlated with a rise in effective thermal conductivity. Conversely, a substantial decrease in thermal conductivity was observed as porosity increased. For instance, at a porosity level of 16.5%, the thermal conductivity experienced a notable 33% reduction compared to the base material. The skin overlap percentage, which governs how much the outer shell of adjacent layers overlaps, was found to impact heat transfer across the overall part surface. A higher overlap percentage was associated with improved thermal conductivity, although it could affect the surface finish of the part. Furthermore, the study explored the print sequence, focusing on whether the outer wall or infill was printed first. Printing the outer wall first resulted in higher thermal conductivity values than that obtained from printing the infill first. Therefore, it is crucial to carefully consider these factors during the BMD 3D printing process to achieve the desired thermal conductivity properties.

Integrated framework of multi−physics mechanism models for gas−powder flow, molten pool evolution and part deformation in high alloy steel additive manufacturing process

Multi−physics numerical models for metal additive manufacturing (MAM) could replace the expensive trial−and−error experiments, reveal the mechanisms of printing process, and provide an effective means of optimizing process parameters and mitigating part defects. However, the existing simulations of MAM process seldom consider the coupling effects between multiple recursive processes. There is an urgent need to overcome the dilemma of limiting MAM process simulation to a localized single sub−process. For directed energy deposition (DED) additive manufacturing process, the integrated simulation framework proposed in this study consists of a gas−powder flow model, a heat and fluid flow model, and a sequentially coupled thermal–mechanical model, which can be used to predict the feedstock feeding process, the evolution of molten pool, and the residual deformations of metal part, respectively. The accurate results of the three recursive sub−processes can be obtained by delivering the validated parameters between models. Corresponding experiments are conducted to verify the accuracy of the integrated framework based on 12CrNi2 alloy powders. The deviation of the predicted focal distance of powder streams is 5.14 %, and the deviations of the predicted molten pool height and width are all less than 8.5 %. The integrated framework could rapidly and accurately predict the residual deformation tendency and the position where the maximum deformation occurs. For single−track and eight−layer line deposition, the deviations of the predicted maximum deformation are all within 0.1 mm. The integrated framework could efficiently provide accurate and comprehensive solutions for optimizing DED process.

Ultrasonic field-assisted metal additive manufacturing (U-FAAM): Mechanisms, research and future directions

Metal additive manufacturing (AM) is a disruptive technology that provides unprecedented design freedom and manufacturing flexibility for the forming of complex components. Despite its unparalleled advantages over traditional manufacturing methods, the existence of fatal issues still seriously hinders its large-scale industrial application. Against this backdrop, U-FAAM is emerging as a focus, integrating ultrasonic energy into conventional metal AM processes to harness distinctive advantages. This work offers an up-to-date, specialized review of U-FAAM, articulating the integrated modes, mechanisms, pivotal research achievements, and future development trends in a systematic manner. By synthesizing existing research, it highlights future directions in further optimizing process parameters, expanding material applicability, etc., to advance the industrial application and development of U-FAAM technology.

A New Zero Waste Design for a Manufacturing Approach for Direct-Drive Wind Turbine Electrical Generator Structural Components

An integrated structural optimization strategy was produced in this study for direct-drive electrical generator structures of offshore wind turbines, implementing a design for an additive manufacturing approach, and using generative design techniques. Direct-drive configurations are widely implemented on offshore wind energy systems due to their high efficiency, reliability, and structural simplicity. However, the greatest challenge associated with these types of machines is the structural optimization of the electrical generator due to the demanding operating conditions. An integrated structural optimization strategy was developed to assess a 100-kW permanent magnet direct-drive generator structure. Generated topologies were evaluated by performing finite element analyses and a metal additive manufacturing process simulation. This novel approach assembles a vast amount of structural information to produce a fit-for-purpose, adaptative, optimization strategy, combining data from static structural analyses, modal analyses, and manufacturing analyses to automatically generate an efficient model through a generative iterative process. The results obtained in this study demonstrate the importance of developing an integrated structural optimization strategy at an early phase of a large-scale project. By considering the typical working condition loads and the machine’s dynamic behavior through the structure’s natural frequencies during the optimization process coupled with a design for an additive manufacturing approach, the operational range of the wind turbine was maximized, the overall costs were reduced, and production times were significantly diminished. Integrating the constraints associated with the additive manufacturing process into the design stage produced high-efficiency results with over 23% in weight reduction when compared with conventional structural optimization techniques.

Expanding the horizons of metal additive manufacturing: A comprehensive multi-objective optimization model incorporating sustainability for SMEs

Metal Additive Manufacturing (MAM) has seen significant growth in recent years, with sub-processes like Metal Material Extrusion (MEX) reaching industrial readiness. MEX, known for its cost-effectiveness and ease of integration, targets a distinct market segment compared to established high-end MAM processes. However, despite technological improvements, its overall integration into the industry as a viable manufacturing technology remains incomplete. This paper investigates the competitiveness of MEX, specifically its integration into the supply chain and the implications on cost and carbon emissions. Utilizing real-world data, the research develops a multi-objective optimization (MOO) model for a four-echelon supply chain including suppliers, airports, production facilities, and customers. The optimization model is combined with a previously developed cost model for MEX to optimize facility location in Norway using the NSGA-II algorithm. Employing a case study approach, the paper examines the production of an industrial part using stainless steel 17-4PH, detailing concrete process costs and system-level costs across four different production scenarios: 10, 100, 1,000, and 10,000 parts. The findings indicate MEX’s potential for cost-effective production at low and diversified volumes, supporting the trend towards customization and manufacturing flexibility. However, the study also identifies significant challenges in maintaining competitiveness at higher production volumes. These challenges underline the necessity for further advancements in MEX technology and process optimization to enhance its applicability and efficiency in larger-scale production settings.

Effect of Powder Reuse on Powder Characteristics and Properties of DED Laser Beam Metal Additive Manufacturing Process with Stellite® 21 and UNS S32750

In this work, the influence of powder reuse up to three times on directed energy deposition (DED) with laser processing has been studied. The work was carried out on two different gas atomized powders: a cobalt-based alloy type Stellite® 21, and a super duplex stainless steel type UNS S32750. One of the main findings is the influence of oxygen content of the reused powder particles on the final quality and densification of the deposited material and the powder catch efficiency of the laser deposition process. There is a direct relationship between a higher surface oxidation of the particles and the presence of oxygen content in the particles and in the as-built materials, as well as oxides, balance of phases (in the case of the super duplex alloy), pores and defects at the micro level in the laser-deposited material, as well as a decrease in the amount of material that actually melts, reducing powder catch efficiency (more than 12% in the worst case scenario) and the initial bead geometry (height and width) that was obtained for the same process parameters when the virgin powder was used (without oxidation and with original morphology of the powder particles). This causes some melting faults, oxides and formation of undesired oxide compounds in the microstructure, and un-balance of phases particularly in the super duplex stainless steel material, reducing the amount of ferrite from 50.1% to 37.4%, affecting in turn material soundness and its mechanical properties, particularly the hardness. However, the Stellite® 21 alloy type can be reused up to three times, while the super duplex can be reused only once without any major influence of the particles’ surface oxidation on the deposited material quality and hardness.

Photopolymerization of Stainless Steel 420 Metal Suspension: Printing System and Process Development of Additive Manufacturing Technology toward High-Volume Production

As the metal additive manufacturing (AM) field evolves with an increasing demand for highly complex and customizable products, there is a critical need to close the gap in productivity between metal AM and traditional manufacturing (TM) processes such as continuous casting, machining, etc., designed for mass production. This paper presents the development of the scalable and expeditious additive manufacturing (SEAM) process, which hybridizes binder jet printing and stereolithography principles, and capitalizes on their advantages to improve productivity. The proposed SEAM process was applied to stainless steel 420 (SS420) and the processing conditions (green part printing, debinding, and sintering) were optimized. Finally, an SS420 turbine fabricated using these conditions successfully reached a relative density of 99.7%. The SEAM process is not only suitable for a high-volume production environment but is also capable of fabricating components with excellent accuracy and resolution. Once fully developed, the process is well-suited to bridge the productivity gap between metal AM and TM processes, making it an attractive candidate for further development and future commercialization as a feasible solution to high-volume production AM.

Exploring spatial beam shaping in laser powder bed fusion: High-fidelity simulation and in-situ monitoring

Laser beam shaping is a novel and relatively unexplored method for controlling the melt pool conditions during metal additive manufacturing (MAM) processes, but even so it still holds good promise for achieving site-specific tailored properties. In this work, a comprehensive numerical and experimental campaign is carried out to explore this subject within metal laser powder bed fusion (LPBF). More specifically, a multiphysics numerical model is implemented for simulating the heat and fluid flow conditions during LPBF of Ti6Al4V using arbitrary circular beam shapes with various power distributions spanning from a pure Gaussian beam to a pure ring beam profile. The model is subsequently coupled with cellular automata to describe the beam shape effects on the microstructure evolution. Model validation is carried out in a two-fold manner. First, we compare the predicted melt pool cross-section with the one from ex-situ single track experiments, and we find a deviation of less than 9 % in melt pool dimensions. Secondly, advanced in-situ X-ray monitoring is carried out to unravel the melt pool dynamics and we find that the predicted morphology closely matches the in-situ X-ray results. Moreover, it is shown that at lower laser power, a bulge of liquid metal forms at the center of the melt pool when employing ring profiles, and this is ascribed to the absence of recoil pressure at the center of the ring beam. Furthermore, increasing the laser power seems to destabilize the melt pool regime, as the central bulge transforms into a liquid metal jet that periodically collapses and breaks up into hot spatter. Based on the results, we believe that our multiphysics modelling methodology, opens up new pathways for predicting how laser beam shaping influences porosity, surface roughness as well as microstructure formation in LPBF processes.

Sub-surface thermal measurement in additive manufacturing via machine learning-enabled high-resolution fiber optic sensing

Microstructures of additively manufactured metal parts are crucial since they determine the mechanical properties. The evolution of the microstructures during layer-wise printing is complex due to continuous re-melting and reheating effects. The current approach to studying this phenomenon relies on time-consuming numerical models such as finite element analysis due to the lack of effective sub-surface temperature measurement techniques. Attributed to the miniature footprint, chirped-fiber Bragg grating, a unique type of fiber optical sensor, has great potential to achieve this goal. However, using the traditional demodulation methods, its spatial resolution is limited to the millimeter level. In addition, embedding it during laser additive manufacturing is challenging since the sensor is fragile. This paper implements a machine learning-assisted approach to demodulate the optical signal to thermal distribution and significantly improve spatial resolution to 28.8 µm from the original millimeter level. A sensor embedding technique is also developed to minimize damage to the sensor and part while ensuring close contact. The case study demonstrates the excellent performance of the proposed sensor in measuring sharp thermal gradients and fast cooling rates during the laser powder bed fusion. The developed sensor has a promising potential to study the fundamental physics of metal additive manufacturing processes.

Metal additive manufacturing adoption in SMEs: Technical attributes, challenges, and opportunities

Recent advancements in Metal Additive Manufacturing (MAM) are transforming manufacturing. Most research and market adoption of MAM have focused on Powder Bed Fusion (PBF), with less attention given to Directed Energy Deposition (DED), Binder Jetting (BJ), and Metal Material Extrusion (MEX), which are only now reaching industrial readiness. This increased availability of MAM processes provides SMEs with a wider range of options, opening up new opportunities that were previously inaccessible. However, despite recent technological improvements that broaden potential applications, the suitability of these processes for industrial use by SMEs is not yet well understood. SMEs currently face difficulties in adopting MAM due to complexities and costs. Moreover, existing literature often overlooks the distinct characteristics and needs of SMEs, making it challenging for them to identify the most suitable MAM processes. This study addresses this gap by using a fuzzy logic approach to evaluate the technical characteristics of PBF, DED, BJ, and MEX, focusing on their compatibility with SME requirements. Each process is ranked based on criteria including costs, complexity, energy consumption, mechanical quality, geometrical quality, speed, and market demand. This evaluation is refined through logarithmic normalization and scaling, resulting in a comprehensive scoring system from 1 to 5. Based on these findings, an SME-focused evaluation matrix is proposed to guide SMEs in selecting the most appropriate MAM process for their specific contexts. This matrix promotes informed and effective adoption strategies, supported by practical examples illustrating the application of each MAM process in SME environments.

Towards an automated framework for numerical prediction of residual stresses and deformations in metal additive manufacturing

Metal additive manufacturing (AM) revolutionizes design with complex structures and customizable material properties. However, high-energy heat source utilized in metal AM creates non-uniform temperature gradients, which usually leads to significant residual stresses and deformations. The present study proposes a line source based semi-analytical thermo-mechanical approach. The accuracy of the proposed line source based model is thoroughly examined by comparing with corresponding experiments in the literature and other numerical models. Besides, an automated framework for fast construction of a thermo-mechanical FE model for the metal AM process is introduced. A numerical tool AM-builder is developed to bridge the gap between AM process parameters and the FE model. The proposed automated framework is followed by utilization of the AM-builder and the line source based model. A purely numerical example is conducted to demonstrate the effectiveness of the proposed automated framework in rapidly constructing AM thermo-mechanical models for various geometries.

Modelling of powder catchment efficiency in micro-plasma transferred arc metal additive manufacturing process

Modelling of powder catchment efficiency in micro-plasma transferred arc metal additive manufacturing process

Criticality of volumetric defect structure on fatigue properties of stainless steel 316L fabricated by laser powder direct energy deposition

As one of the significant metal additive manufacturing (AM) processes, laser powder direct energy deposition (LP-DED) has established its role in specific applications, including component repair, coatings, and multi-material structure. However, one of the limiting factors for the wide adoption of LP-DED is the formation of volumetric defects during the printing process. These defects are one of the crucial causes of poor and scattered mechanical properties, especially fatigue performance, by accelerating fatigue crack initiation, growth, and final failure. Unraveling defect structure-fatigue correlations can help understand how defects are affecting fatigue properties, how critical they are, and, finally, what solutions can be utilized to diminish their harmful effects. In this research study, novel defect features, including size features, shape descriptors, and location features, were proposed and investigated regarding fatigue properties in LP-DED. Accordingly, stainless steel 316L (SS 316L) blocks were fabricated by LP-DED. After cutting out the dog-bone samples by wire electric discharge machining (EDM), X-ray Computed Tomography (XCT) and subsequent fatigue tests were conducted. The defect features and their statistical descriptors were extracted from XCT data. By statistical analysis of the features, valuable insights regarding their correlations with fatigue life were realized.

A novel solid-state metal additive manufacturing process – Laser-induced Supersonic Impact Printing (LISIP): Exploration of process capability

Solid-state metal additive manufacturing (AM) techniques offer unique capabilities for direct printing of metallic materials towards a variety of applications. In this work, we developed a novel solid-state metal AM process, named laser-induced supersonic impact printing (LISIP), in which the laser shock-induced impact loading was utilized to trigger the adiabatic shearing phenomenon at metal-metal interfaces towards solid-state three-dimensional (3D) printing of metallic materials. The design of LISIP was inspired by cold spray, explosive welding, and laser impact welding. An experimental investigation was conducted to explore the process capability of LISIP, with a focus on 3D micro-lamination of metallic structure, AM of dissimilar materials, and printing-on-demand direct writing at various length scales from micrometer to centimeter. Steel, copper, aluminum, titanium, and magnesium alloys were used as foil and/or substate for experimentation. Moreover, the microstructure at bonding interfaces were characterized to understand the microstructure evolution as induced by adiabatic shearing. The bonding quality was evaluated using the lap shear test. The mechanisms involved in LISIP including the laser-matter interaction and adiabatic shearing bonding were investigated using first-principles modeling and finite element method simulation. In addition, the technical challenges, scientific knowledge gaps, future research directions, and potential applications of LISIP were deliberated. We envision that the findings and knowledge gained in this work will serve as the first milestone towards the establishment of LISIP for broader impacts.

Physics-based modeling of metal additive manufacturing processes: a review

Physics-based modeling of metal additive manufacturing processes: a review

Mechanical Properties and Cooperation Mechanism of Corroded Steel Plates Retrofitted by Laser Cladding Additive Manufacturing under Tension.

As a metal additive manufacturing process, laser cladding (LC) is employed as a novel and beneficial repair technology for damaged steel structures. This study employed LC technology with 316 L stainless steel powder to repair locally corroded steel plates. The influences of interface slope and scanning pattern on the mechanical properties of repaired specimens were investigated through tensile tests and finite element analysis. By comparing the tensile properties of the repaired specimens with those of the intact and corroded specimens, the effectiveness of LC repair technology was assessed. An analysis of strain variations in the LC sheet and substrate during the load was carried out to obtain the cooperation mechanism between the LC sheet and substrate. The experimental results showed that the decrease in interface slope slightly improved the mechanical properties of repaired specimens. The repaired specimens have similar yield strength and ultimate strength to the intact specimens and better ductility as compared to the corroded specimen. The stress-strain curve of repaired specimens can be divided into four stages: elastic stage, substrate yield-LC sheet elastic stage, substrate hardening-LC sheet elastic stage, and plastic stage. These findings suggest that the LC technology with 316 L stainless steel powder is effective in repairing damaged steel plates in civil engineering structures and that an interface slope of 1:2.5 with the transverse scanning pattern is suitable for the repair process.