Metal Additive Manufacturing Research Articles

Machine Learning Framework for Hybrid Clad Characteristics Modeling in Metal Additive Manufacturing

Metal additive manufacturing (MAM) has advanced significantly, yet accurately predicting clad characteristics from processing parameters remains challenging due to process complexity and data scarcity. This study introduces a novel hybrid machine learning (ML) framework that integrates validated multi-physics computational fluid dynamics simulations with experimental data, enabling prediction of clad characteristics unattainable through conventional methods alone. Our approach uniquely incorporates physics-aware features, such as volumetric energy density and linear mass density, enhancing process understanding and model transferability. We comprehensively benchmark ML models across traditional, ensemble, and neural network categories, analyzing their computational complexity through Big O notation and evaluating both classification and regression performance in predicting clad geometries and process maps. The framework demonstrates superior prediction accuracy with sub-second inference latency, overcoming limitations of purely experimental or simulation-based methods. The trained models generate processing maps with 0.95 AUC (Area Under Curve) accuracy that directly guide MAM parameter selection, bridging the gap between theoretical modeling and practical process control. By integrating physics-based simulations with ML techniques and physics-aware features, our approach achieves an R2 of 0.985 for clad geometry prediction and improved generalization over traditional methods, establishing a new standard for MAM process modeling. This research advances both theoretical understanding and practical implementation of MAM processes through a comprehensive, physics-aware machine learning approach.

A Comparison Between the Residual Stresses of Ti6Al4V and Ti-6Al-2Sn-4Zr-6Mo Processed by Laser Powder Bed Fusion

Metal additive manufacturing processes induce residual stress in as-built components. These residual stresses are detrimental to part quality as they can induce defects such as warping and delamination. In some cases, when complex components are built, residual stress can even cause a build job to fail due to the recoater crashing into the distorted part. In this paper, the residual stress values of Ti6Al4V and Ti-6Al-2Sn-4Zr-6Mo alloys were evaluated by the cantilever approach and by the X-ray diffraction sin2(Ψ) method. The results showed that, as expected, Ti6Al4V as-built cantilevers displayed high distortion and von Mises equivalent stress values up to 494 MPa. On the contrary, as-built Ti-6Al-2Sn-4Zr-6Mo cantilevers were characterized by almost null warping and a residual stress value in the as-built state of 191 MPa. This different behavior is mainly due to the different properties of the hexagonal α’ martensite in Ti6Al4V and the soft orthorhombic α’’ martensite in Ti6246. The post-processing heat treatment significantly reduced the residual stress in Ti6Al4V, lowering it to 44 MPa, while, in the case of Ti-6Al-2Sn-4Zr-6Mo, the post-processing heat treatment did not affect the residual stress conditions. These findings suggest that Ti-6Al-2Sn-4Zr-6Mo could be a suitable candidate for the additive manufacturing production of extremely complex parts, as it could reduce the risks associated with recoater crashes and job failures.

Multi-Robot Scan-n-Print for Wire Arc Additive Manufacturing

Abstract Robotic Wire Arc Additive Manufacturing (WAAM) is a metal additive manufacturing technology offering flexible 3D printing while ensuring high-quality near-net-shape final parts. However, WAAM also suffers from geometric imprecision, especially for low melting-point metal such as aluminum alloys. In this paper, we present a multi-robot framework for WAAM process monitoring and control. We consider a three-robot setup: a 6-dof welding robot, a 2-dof trunnion platform, and a 6-dof sensing robot with a wrist-mounted laser line scanner measuring the printed part height profile. The welding parameters, including the wire feed rate, are held constant based on the materials used, so the control input is the robot path speed. The measured output is the part height profile. The planning phase decomposes the target shape into slices of uniform height. During runtime, the sensing robot scans each printed layer, and the robot path speed for the next layer is adjusted based on the deviation from the desired profile. The adjustment is based on an identified model correlating the path speed to changes in height. The control architecture coordinates the synchronous motion and data acquisition between all robots and sensors. Using a three-robot WAAM testbed, we demonstrate significant improvements of the closed loop scan-n-print approach over the current open loop result on both a flat wall and a more complex turbine blade shape.

A Frugal Approach Toward Modeling of Defects in Metal 3D Printing Through Statistical Methods in Finite Element Analysis

Metal additive manufacturing has emerged as a revolutionary technology for the fabrication of high-complexity components. However, this technique presents unique challenges related to the structural integrity and final strength of the parts produced due to inherent defects, such as porosity, cracks, and geometric deviations. These defects significantly impact the fatigue life of the material by acting as stress concentrators that accelerate failure under cyclic loading. On the one hand, this type of model is very complicated in its approach, since, even with encouraging results, the complexity of the calculation with these variables makes it difficult to obtain a simple result that allows for a generalized interpretation. On the other hand, using more familiar methods, it is possible to qualitatively guess the behavior that helps obtain results with better applicability, even at limited levels of precision. This paper presents a simplified finite element method combined with a statistical approach to model the presence of porosity in metal components produced by additive manufacturing. The proposed model considers a two-dimensional square plate subjected to tensile stress, with randomly introduced defects characterized by size, shape, and orientation. The percentage of porosity that affects each aspect determines the adjustment of the mechanical properties of finite elements. A series of simulations were performed to generate multiple models with random defect distributions to estimate maximum stress values. This approach demonstrates that complex models are not always necessary for a preliminary practical estimate of the effects of new manufacturing techniques. Furthermore, it demonstrates the potential for the extension of frugal computational techniques, which aim to minimize computational and experimental costs in the engineering field. The article discusses future research directions, particularly those related to potential business applications, including commercial uses. This follows a discussion of the existing limitations of this study.

A review of strategies to control process-induced cracks in metal additive manufacturing and remanufacturing

A review of strategies to control process-induced cracks in metal additive manufacturing and remanufacturing

A novel deep learning model for the real-time prediction of emissivity and thermal history in metal additive manufacturing processes

A novel deep learning model for the real-time prediction of emissivity and thermal history in metal additive manufacturing processes

Study on Polishing Technologies for Additive Manufacturing Parts

Additive manufacturing has a good application prospect in aerospace, medical implantation and other fields, but the molding surface quality is poor, without post processing can not meet the requirements of high service, polishing processing is a key link in the high-performance metal additive manufacturing technology chain. This paper summarizes the characteristics of the ladder effect, the high roughness of the forming surface. In recent years, additive manufacturing technology, also known as 3D printing, has been highly valued by aviation enterprises for its unique advantage in rapid prototyping, particularly for complex metal parts. However, due to the layer-by-layer growing process of 3D printing, the built parts often have poor surface roughness and are not suitable for practical use without post-treatment. Based on this foundation, the primary focus of research in the field of additive manufacturing for metal parts polishing includes electrochemical, laser, and abrasive flow polishing technologies. The progress in these areas is examined with consideration given to various manufacturing processes, different types of metal powder materials, and diverse structures (such as porous structure and high aspect flow channels) found in additive manufacturing samples. This review summarizes the research findings related to surface roughness, material removal, surface residual stress, profile accuracy retention, and other technical indicators associated with the polishing process for additive manufacturing metal parts. Finally, the paper discusses potential future developments in polishing technology for 3D printed metal parts.

Determining the Parameters of Gurson–Tvergaard–Needleman Model for Predicting the Failure of Wrought and Fused Filament Fabricated 17-4 PH Stainless Steel

Abstract Metal additive manufacturing is an emerging technology for creating metallic parts, with metal fused filament fabrication (FFF) rapidly gaining popularity due to its cost-effectiveness. Despite the acceptable mechanical properties of additively manufactured metals using FFF, a significant technical challenge is the presence of undesirable porosity, which affects material performance. This study aims to model the material behavior of FFF 17-4 PH stainless steel, considering its porosity, using the Gurson–Tvergaard–Needleman (GTN) damage model. The GTN model, which incorporates the micromechanical behavior of ductile metals, shows great potential for failure prediction. The GTN model parameters were identified for both wrought and FFF 17-4 PH stainless steel through a series of proposed methods. Initial void volume fractions were determined using density measurements. The evolution of void volume fractions was experimentally assessed through interrupted uniaxial tensile tests, leading to the analytical derivation of three void nucleation parameters based on continuum damage mechanics. Additional GTN model parameters related to material failure were determined through microscopic analysis of rupture surfaces and finite element (FE) trial-and-error methods. FE simulations using the GTN damage model, represented as porous metal plasticity in abaqus, were conducted to verify the identified parameters. The results demonstrated that the numerical calculations of the FE model are in good agreement with the experimental data. The use of experimentally derived GTN model parameters from the proposed methods effectively predicts material behavior, particularly in the post-necking region where traditional FE modeling fails to simulate the realistic material response.

Metal Additive Manufacturing: Design, Performance, and Applications

The rapid evolution of metal additive manufacturing (AM) has led to transformative advancements across various industries, including aerospace, healthcare, automotive, and energy [...]

Study of the Hibridation of Ablation Casting and Laser Wire Metal Deposition for Aluminum Alloy 5356

The rapidly growing field of metal additive manufacturing (AM) has enabled the fabrication of near-net-shape components with complex 3D structures in a more reliable, productive, and sustainable way compared to any other manufacturing process. The productivity of AM could be significantly increased combining conventional and AM technologies. However, the application at an industrial level requires the validation of the AM process itself and the assurance of the soundness of the junction between the substrate and the deposited metal at a sufficiently rapid metal deposition rate. In this work, the validation of additively manufactured samples of Al-5356 alloy was performed. These were manufactured partially via an ablation casting process and partially via laser metal deposition using a metallic wire (LMwD). The deposited material showed low porosity levels, i.e., below 0.04%, and a small number of lack-of-union defects, which are detrimental to the mechanical properties. In the tensile samples centred at the junction between the ablated and deposited materials, it was found that when the AM part of the sample exhibited no lack-of-union defects, the region manufactured using LMwD showed higher strength than the ablation-cast part. These results suggest that the combination of ablation casting and LMwD is a competitive technique for the manufacturing of Al-5356 alloy parts with complex geometries.

Metal Additive Manufacturing and Molten Pool Dynamic Characterization Monitoring: Advances in Machine Learning for Directed Energy Deposition

Directed energy deposition (DED) has progressively emerged as a highly promising technology for the rapid, cost-effective, and high-performance fabrication of hard-to-process metal components with shorter production cycles. Recognized as one of the most widely utilized metal additive manufacturing (AM) techniques, DED has found extensive applications in critical industrial sectors such as aerospace and aviation. Despite its potential, challenges such as inconsistent part quality and low process repeatability continue to restrict its broader adoption. The core issue underlying these challenges is the complex, dynamic nature of the DED process, which involves the coupling of multiple physical fields. Within this context, the molten pool plays a pivotal role, serving as a key carrier that encapsulates abundant process characteristic information. The dynamic characteristics of the molten pool are intrinsically linked to the final part quality and the repeatability of the process. Consequently, integrating machine learning (ML) methodologies into the monitoring framework can offer robust data-driven support for enhancing both product quality and process consistency. This paper provides a comprehensive review of the research advancements and prospective trends in the dynamic monitoring and control of molten pool characteristics within DED processes underpinned by machine learning techniques. The review is structured around five key areas: an overview and fundamental principles of DED technology, methods for process information sensing during part monitoring, approaches for dynamically monitoring molten pool characteristics, the primary challenges currently faced in intelligent monitoring systems, and the potential future directions for further research and development. Through this detailed examination, the paper aims to shed light on the pivotal role of intelligent monitoring systems in advancing DED technology, ultimately contributing to more reliable and repeatable additive manufacturing processes.

Effect of Volumetric Energy Density on the Evolution of the Microstructure and the Degradation Behavior of 3D-Printed Fe-Mn-C Alloys from Water-Atomized Powders

Additive manufacturing of metals opens new doors for innovation in custom-based productions in a wide range of fields, including medicine, even if it introduces new challenges that need to be addressed to guarantee the properties are equal to or superior to those of conventional fabrication processes. In this research, porous, biodegradable Fe-Mn-C alloys were fabricated using a 3D printing technique with four different printing energy densities ranging from 62.5 to 125.0 J/mm3. The effect of printing energy density on the microstructure and degradation behavior was investigated. Lower energy densities resulted in higher pore density and the presence of unmelted powder particles, while the alloy printed at 104.2 J/mm3 exhibited the lowest pore density and the smallest grain size. Degradation tests revealed that the highest pore density in the sample printed at 62.5 J/mm3, and the lowest grain size in the sample printed at 104.2 J/mm3 contributed to faster degradation rates. The alloy printed at the highest energy density, 125.0 J/mm3, demonstrated the largest grain size and the slowest degradation rate. Energy-dispersive spectroscopy and Fourier transform infrared spectroscopy analyses identified manganese carbonate as the primary degradation product, with calcium phosphate forming as a secondary product. These findings provide a significant understanding of the relationship between printing parameters, microstructure, and degradation behavior, which are essential for optimizing the performance of Fe-Mn-C alloys in biodegradable material applications.

Correction: A comprehensive review of residual stress in metal additive manufacturing: detection techniques, numerical simulation, and mitigation strategies

Correction: A comprehensive review of residual stress in metal additive manufacturing: detection techniques, numerical simulation, and mitigation strategies

A metal 3D-printed non-assembly mandrel for profile bending

Abstract In the overall regime of production technologies, bending processes allow production of complex parts and profiles in a precise, flexible, and economical way. It is important to manufacture tools which are universally applicable and easy to manufacture, since modern industries demand flexibility in terms of larger varieties or smaller batch sizes. In the past, it was difficult and time-consuming to manufacture bending tools, especially mandrels, due to their high geometric complexity and tight assembly of swivel joints. In this paper, we present a new design approach for the bending mandrel which is suitable for metal additive manufacturing that incorporates non-assembly mechanisms of joints. The hinges are systematically developed with particular focus on achieving a minimized gap and resultant clearance. As a test case, a mandrel is 3D printed by using the laser powder bed fusion technique and successfully tested in the rotary draw bending process parameterized for narrow bends in stainless steel tubes. In the light of these results, a solution is formulated for ease in manufacturing and faster rate of tool production, in this case bending mandrel. This methodology facilitates the production of small batch sizes and large product variants in bending processes. The research presented in this paper serves as a forward step towards economical production of complex parts in a highly flexible manner.

A Review of Modeling, Simulation, and Process Qualification of Additively Manufactured Metal Components via the Laser Powder Bed Fusion Method

Metal additive manufacturing (AM) has grown in recent years to supplement or even replace traditional fabrication methods. Specifically, the laser powder bed fusion (LPBF) process has been used to manufacture components in support of sustainment issues, where obsolete components are hard to procure. While LPBF can be used to solve these issues, much work is still required to fully understand the metal AM technology to determine its usefulness as a reliable manufacturing process. Due to the complex physical mechanisms involved in the multiscale problem of LPBF, repeatability is often difficult to achieve and consequently makes meeting qualification requirements challenging. The purpose of this work is to provide a review of the physics of metal AM at the melt pool and part scales, thermomechanical simulation methods, as well as the available commercial software used for finite element analysis and computational fluid dynamics modeling. In addition, metal AM process qualification frameworks are briefly discussed in the context of the computational basis established in this work.

Sustainable Additive Manufacturing: An Overview on Life Cycle Impacts and Cost Efficiency of Laser Powder Bed Fusion

This overview study investigates integrating advanced manufacturing technologies, specifically metal additive manufacturing (AM) and laser powder bed fusion (LPBF) processes, within Industry 4.0 and Industry 5.0 frameworks, to enhance sustainability and efficiency in industrial production and prototyping. The manufacturing sector, a significant contributor to global greenhouse gas emissions and resource consumption, is increasingly adopting technologies that reduce environmental impact while maintaining economic growth. Selective laser melting (SLM), as the subsection LPBF technologies, is highlighted for its capability to produce high-performance, lightweight, and complex components with minimal material waste, thus aligning with circular economy goals for metal alloys. Life cycle assessment (LCA) and life cycle costing (LCC) analyses are essential methods for evaluating the sustainability of any new technology. Sustainable technologies could support the concepts of the factory of the future (FoF), fulfilling the requirements of digital transformation and digital twins. This overview study reveals that implementing AM—specifically SLM—has the potential to reduce the environmental impact of manufacturing. It underscores the ability of these technologies to promote sustainable and efficient manufacturing practices, thereby accelerating the shift from Industry 4.0 to Industry 5.0.

Smart-Alloying for tailored microstructures in laser-based metal additive manufacturing

Smart-Alloying for tailored microstructures in laser-based metal additive manufacturing

Elastic Residual Strain Measurements of 3D Additively Manufactured Builds of Nickel Alloy 718 AM Bench 2022 Artifacts Using Energy Dispersive Synchrotron X-ray Diffraction

AbstractResidual elastic strains and the resulting stresses are key aspects of most metal additive manufacturing (AM) approaches and strongly affect both the build process and part performance. Computational modeling of these stresses allows the manufacturer to compensate for the distortions that occur during the build process and to predict where part failures may occur. Such simulations must be validated against rigorous measurement datasets. Here, energy dispersive synchrotron X-ray diffraction is used to characterize the location-specific elastic strains within additively manufactured laser powder bed fusion (PBF-LB) nickel Alloy 718 test artifacts. Elastic strains are measured along two orthogonal directions from 2248 measurement locations and the measurement uncertainties are assessed. Comparisons are made between the measurement results and modeling predictions submitted by the AM modeling community. These measurements are part of the Additive Manufacturing Benchmark Test Series (AM Bench), a broad effort to produce measurement datasets for validating AM computer simulations across the range of processing, structure, and properties, for many AM build methods and material classes. All the measurement data are available online with download links at www.nist.gov/ambench.

Influence of Powder Feed Density on Mechanical Properties and Microstructure in L-DED Additive Manufactured STS316L

Laser Directed Energy Deposition (L-DED) is a notable additive manufacturing method in which metal powder is sprayed through a nozzle and then consolidated layer by layer using a laser. Unlike other additive manufacturing processes, DED has fewer constraints on the size of the fabricated parts, making it advantageous for the production of large-scale components. However, the use of DED in additive manufacturing requires careful optimization of various process parameters, including laser power, powder feed rate, nozzle scanning speed, and deposition path, as these parameters significantly influence the geometry and properties of the fabricated parts. Recent studies have extensively investigated the microstructure and properties of parts fabricated via DED at different energy densities, but research on variables related to powder feed is still lacking. In this study, using Powder Line-Density (PLD) as a parameter, changes in bead geometry, microstructure and mechanical property followed by variation in powder feed density while conducting DED additive manufacturing with STS316L were observed. Controlled by powder feed rate and scanning speed, the Powder Line-Density was utilized for 1-line deposition with STS316L alloy powder, enabling the observation of bead geometry and melt-pool shape during the deposition process. Additionally, square-shaped specimens were manufactured via DED with controlled Powder LineDensity to observe the resulting microstructure and mechanical properties. It was observed that, even for the same energy density, specimens exhibited distinct grain morphologies, microstructure, and mechanical properties depending on the Powder Line-Density, with a particularly significant change in anisotropy. This highlights the importance of powder feed density as a key variable alongside energy density to optimize DED additive manufacturing processes. The findings of this study are expected to contribute to the control of anisotropy and strength of fabricated components in metal additive manufacturing processes by the regulation of powder feed density.

Efficient layer-building approach for promising cold spray additive manufacturing

Cold spray has demonstrated superior manufacturing efficiencies in geometric forming, which positions it as a significant process in metal additive manufacturing technology. Cold spray additive manufacturing (CSAM) achieves geometric forming by employing a layer-by-layer construction methodology. However, the current challenge lies in the lack of evaluation and management of the efficiency in layer-building, leading to potential conflicts between manufacturing efficiency and forming accuracy. To address this issue, this paper proposes a layer-building approach based on the forming characteristics of cold-sprayed single-track to enhance the efficiency of CSAM. The study initially analyzes the deposition efficiency and deposition rate resulting from various process parameters, as demonstrated by the geometry characteristics of the single-track. Furthermore, the geometry and efficiency models of CSAM single-track based on Gaussian distribution are developed. Subsequently, the study conducts the selection of efficient single-tracks and optimization of layer-building parameters based on the single-track model. Finally, both the simulation and experimental validations are performed. The results indicate that the proposed approach enables the continuous building of CSAM single-layers with high efficiency. The optimal deposition rate and deposition efficiency of the layer-building can reach 4.942 cm3/min and 73.79 %, respectively. Additionally, the model offers predictions and control of the geometrical shape during the CSAM. This implies that the proposed approach enhances CSAM as an efficient layer-by-layer additive manufacturing process for 3D shape forming.