Metal additive manufacturing: Principles and applications

Sinter-based metal additive manufacturing (AM) processes that decouple the geometric shaping and consolidation of AM have several advantages over melt-based AM technologies (one-step consolidation and shaping). The sinter-based AM technologies, led by binder jetting, has the potential to significantly increase the productivity and attain more isotropic microstructures in the final product. Sinter-based AM includes the ability to cover a wide spectrum of productivity, including prototyping, low volume serial production, and high-volume mass production. Recently, several non-traditional sinter-based metal AM processes (relatively less common) have emerged. This paper will review some of these emerging and lesser-known sinter-based metal AM technologies along with a few processes based on modifications of binder jet and material extrusion techniques that have gained traction.

Additive manufacturing (AM) of hot-work tool steels such as H13 offers unique opportunities for producing complex, conformally cooled tools with reduced production time and material waste. In this study, five metal AM technologies—Fused Deposition Modeling and Sintering (FDMS, Desktop Metal Studio System and Zetamix), Binder Jetting (BJ), Laser Powder Bed Fusion (LPBF), and Directed Energy Deposition (DED)—were compared in terms of microstructure, porosity, and post-processing heat treatment response. The as-printed microstructures revealed distinct differences among the technologies: FDMS and BJ exhibited high porosity (6–9%), whereas LPBF and DED achieved near-full densification (<0.1%). Samples with sufficiently low porosity (BJ, LPBF, DED) were subjected to tempering and quenching treatments to evaluate hardness evolution and microstructural transformations. The satisfactory post-treatment hardness was observed in both tempered and quenched and tempered BJ samples, associated with secondary carbide precipitation, while LPBF and DED samples retained stable martensitic structures with hardness around 600 HV0.5. Microstructural analyses confirmed the dependence of phase morphology and carbide distribution on the thermal history intrinsic to each AM process. The study demonstrates that while FDMS and BJ are more accessible and cost-effective for low-density prototypes, LPBF and DED offer superior density and mechanical integrity suitable for functional tooling applications.

Continuous 3D printing of metal structures using ultrafast mask video projection initiated vat photopolymerization

In recent years, Additive Manufacturing (AM), also called 3D printing, has been expanding into several industrial sectors due to the technology providing opportunities in terms of improved functionality, productivity, and competitiveness. While metal AM technologies have almost unlimited potential, and the range of applications has increased in recent years, industries have faced challenges in the adoption of these technologies and coping with a turbulent market. Despite the extensive work that has been completed on the properties of metal AM materials, there is still a need of a robust understanding of processes, challenges, application-specific needs, and considerations associated with these technologies. Therefore, the goal of this study is to present a comprehensive review of the most common metal AM technologies, an exploration of metal AM advancements, and industrial applications for the different AM technologies across various industry sectors. This study also outlines current limitations and challenges, which prevent industries to fully benefit from the metal AM opportunities, including production volume, standards compliance, post processing, product quality, maintenance, and materials range. Overall, this paper provides a survey as the benchmark for future industrial applications and research and development projects, in order to assist industries in selecting a suitable AM technology for their application.

Purpose The development of additive manufacturing (AM) techniques has significantly expanded the design space for engineering structures and facilitated the practical application of novel concepts, such as meta-materials with diverse microstructures. However, in load-bearing applications—particularly in aerospace and aeroengine fields—additively manufactured (AMed) metallic materials and structures still face limitations due to the presence of inherent defects. For meta-materials, the geometric accuracy of microstructural cells is also difficult to ensure using current metal AM technologies. These defects and geometric inaccuracies can markedly affect the mechanical properties of mechanical meta-materials. Consequently, a substantial body of research has focused on investigating the mechanical behavior and performance of AMed mechanical meta-materials. This paper presents a comprehensive review of recent advancements in metal AM technologies, with a particular focus on defect characterization methods and the evaluation of strength and fatigue properties in AMed mechanical meta-materials. Design/methodology/approach This paper provides a state-of-the-art review on the AM techniques for mechanical meta-materials, defects and defect characterization methods in AMed structures, and evaluation methods of strength and fatigue properties of mechanical meta-materials. Findings Metal AM techniques for mechanical meta-materials, like selective laser melting, wire-arc AM, etc, and recently developed technologies like, online inspection during the AM process, are reviewed. The defects in AMed meta-materials, along with the corresponding characterization methods, are systematically summarized. Additionally, this paper presents a comprehensive overview of evaluation approaches for the strength and fatigue properties of mechanical meta-materials, encompassing experimental testing, theoretical modeling, numerical simulation, and machine learning techniques. Future perspectives on manufacturing and the mechanical property study of mechanical meta-materials are also given. Originality/value A systematic summary of metal AM techniques, as well as defect detection and characterization methods in AMed mechanical meta-materials, is provided. Furthermore, the paper presents a comprehensive review of the mechanical properties of mechanical meta-materials, with a particular focus on strength and fatigue performance.

This study investigates the need for a quality strategy in metal additive manufacturing (AM) technology effective for batch production. We carried out two surveys aimed at gathering data from industry experts to understand the challenges and the requirements of quality strategies in metal AM. Survey 1 investigated general quality aspects in metal technology, revealing key areas where quality strategies are required, such as process control, material consistency, and post-processing. It also highlighted challenges that directly impact part quality, including process variability, material inconsistencies, and surface finish issues. Survey 2, focused on batch production, showed that 75% of participants strongly agreed with the need for a specific quality strategy to address challenges specific to metal AM. The respondents highlighted critical processes requiring quality strategies, including powder quality, process optimisation, and defect detection, while identifying ongoing issues with process variability and material inconsistency. Both surveys indicate a need for a standardised and effective quality strategy to enhance production consistency, efficiency, and regulatory compliance. Regardless of a limited sample size of 59 respondents, these results emphasise the need for improved quality strategies in metal AM to reduce defects effectively, meet customer expectations, and ensure scalable production. This study provides insights into the strategic development of quality strategies significant for advancing metal AM technology.

Metal Powder Production for Additive Manufacturing

The lack of a reliable theoretical model of the processing-microstructure relationship of AM (Additive Manufacturing) material is preventing AM technology from being widely adopted by the manufacturing community. The goal of this work is to establish the link between the microstructure (texture) and the process parameters of metal AM processes. A quantitative method based on the epitaxial growth of columnar grains within and across melt pools is proposed to predict the texture formation during a metal AM process. The state-of-the-art CALPHAD-informed FEM (finite element method) simulation has been used to predict the geometry and thermal profile of the quasi-steady melt pool. The thermal gradient distribution within the 3D melt pool determines the crystallography direction and growth direction of the columnar grains within each deposited single tracks. The single tracks with the predicted geometry are amalgamated together to represent the bulk part, and the epitaxial growth of grains across the boundary of neighboring tracks are quantitatively modeled. The proposed method is calibrated and validated by experimental studies of metal AM processed Alloy 718.

The goal of this work is to predict the effect of part geometry and process parameters on the instantaneous spatiotemporal distribution of temperature, also called the thermal field or temperature history, in metal parts as they are being built layer-by-layer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theory-based approach for predicting the temperature distribution in metal AM parts. This objective is consequential to overcome the current poor process consistency and part quality in AM. One of the main reasons for poor part quality in metal AM processes is ascribed to the nature of temperature distribution in the part. For instance, steep thermal gradients created in the part during printing leads to defects, such as warping and thermal stress-induced cracking. Existing nonproprietary approaches to predict the temperature distribution in AM parts predominantly use mesh-based finite element analyses that are computationally tortuous—the simulation of a few layers typically requires several hours, if not days. Hence, to alleviate these challenges in metal AM processes, there is a need for efficient computational models to predict the temperature distribution, and thereby guide part design and selection of process parameters instead of expensive empirical testing. Compared with finite element analyses techniques, the proposed mesh-free graph theory-based approach facilitates prediction of the temperature distribution within a few minutes on a desktop computer. To explore these assertions, we conducted the following two studies: (1) comparing the heat diffusion trends predicted using the graph theory approach with finite element analysis, and analytical heat transfer calculations based on Green’s functions for an elementary cuboid geometry which is subjected to an impulse heat input in a certain part of its volume and (2) simulating the laser powder bed fusion metal AM of three-part geometries with (a) Goldak’s moving heat source finite element method, (b) the proposed graph theory approach, and (c) further comparing the thermal trends predicted from the last two approaches with a commercial solution. From the first study, we report that the thermal trends approximated by the graph theory approach are found to be accurate within 5% of the Green’s functions-based analytical solution (in terms of the symmetric mean absolute percentage error). Results from the second study show that the thermal trends predicted for the AM parts using graph theory approach agree with finite element analyses, and the computational time for predicting the temperature distribution was significantly reduced with graph theory. For instance, for one of the AM part geometries studied, the temperature trends were predicted in less than 18 min within 10% error using the graph theory approach compared with over 180 min with finite element analyses. Although this paper is restricted to theoretical development and verification of the graph theory approach, our forthcoming research will focus on experimental validation through in-process thermal measurements.

Chapter 1 - Metal additive manufacturing

Cutting fluids in metal AM: A review of sustainability and efficiency

&lt;div&gt;As the advancement of metal additive manufacturing (AM) technology persists, so will the expansion of its capabilities and applications. In particular, the automotive industry can benefit from the advantages provided by AM, such as flexibility in design and customized products. In this avenue, one potential application of AM is in internal combustion engines (ICEs). As a first step, this effort explores the feasibility of using AM to produce working ICE components for an air-cooled engine. The cylinder head and crankcase of an 11 cm&lt;sup&gt;3&lt;/sup&gt; displacement volume Saito FG-11 engine were the components identified for metal AM. They were manufactured through Laser Powder Bed Fusion (LBPF) and post machined to achieve the necessary tolerances. Engine testing encompassed both propeller and dynamometer setups with corresponding data collection to measure and compare engine performance. Each engine was monitored at the same specific set points during operation for speed, torque, temperatures, pressures, airflow, and mass flow rate of fuel. The results show that the performance parameters of the AM engine were marginally degraded with produced torques of 0.05-0.10 N⋅m (7-14%) lower for dynamometer testing, and wide-open throttle engine speeds that were 500-700 rpm (6-13%) slower during propeller testing. Despite these diminished outputs, the AM engine was operable and ran without failure or damage for over 3.5 hours during testing. The major influencers behind the reduced performance were hypothesized to be either variations in assembly or increased friction from an insufficient hypereutectic honing procedure. Overall, the AM process was not considered a cause of the diminished output, thus highlighting the potential of AM for major ICE parts.&lt;/div&gt;

Metal additive manufacturing (MAM) with its high material utilization efficiency to achieve the manufacturing of metal parts of complex shapes has great potential in the field of aerospace, medical, automotive and energy fields. MAM process generates large residual stress in the fabricated sample due to extreme mechanical, thermal and metallurgical interaction. Residual stress has a significant effect on dimensional stability. Thermal modelling of the MAM process is required to evaluate and implement the correct optimized process parameters for the quality fabrication module of industry. Thermal modelling is utmost necessary to fulfil the design for additive manufacturing (AM) of direct energy deposition (DED). Here, the fast design feedback loop is compiled by thermal modelling which reduces error in manufacturing. Thermal modelling of the DED AM process is carried out. As the material is temperature dependent, this depicts all the mechanical properties of the material given in the thermal modelling. Residual stress is generated, which has a significant effect on the dimension stability, corrosion resistance, crack growth resistance and mechanical properties of the fabricated sample through DED MAM. The present study performs a numerical simulation modelling for temperature-dependent M2 steel material as powder and SS304 as the substrate for the residual stress and temperature distribution of the defined laser power. The heat generated by laser power, i.e. Double Ellipsoidal Goldak's heat model is defined using Python script in the study. The study uses 800 W laser powers which generated a maximum residual stress of 179.9 MPa and yield stress of 321.7 MPa for the time interval of 50 s and also gives a maximum nodal temperature of 2233 K. The simulation will give us the details about the stresses of the material prior to development, i.e. without printing the actual component and will help to remove defects or printing failure to produce high-quality parts.

Decision support for repair with DED AM processes based on sustainability and techno-economical evaluation

Heterogeneous sensor-based condition monitoring in directed energy deposition