Overview of Additive Manufacturing Informatics: “A Digital Thread”

PurposeDue to the complexity of and variations in additive manufacturing (AM) processes, there is a level of uncertainty that creates critical issues in quality assurance (QA), which must be addressed by time-consuming and cost-intensive tasks. This deteriorates the process repeatability, reliability and part reproducibility. So far, many AM efforts have been performed in an isolated and scattered way over several decades. In this paper, a systematically integrated holistic view is proposed to achieve QA for AM.Design/methodology/approachA systematically integrated view is presented to ensure the predefined part properties before/during/after the AM process. It consists of four stages, namely, QA plan, prospective validation, concurrent validation and retrospective validation. As a foundation for QA planning, a functional workflow and the required information flows are proposed by using functional design models: Icam DEFinition for Function Modeling.FindingsThe functional design model of the QA plan provides the systematically integrated view that can be the basis for inspection of AM processes for the repeatability and qualification of AM parts for reproducibility.Research limitations/implicationsA powder bed fusion process was used to validate the feasibility of this QA plan. Feasibility was demonstrated under many assumptions; real validation is not included in this study.Social implicationsThis study provides an innovative and transformative methodology that can lead to greater productivity and improved quality of AM parts across industries. Furthermore, the QA guidelines and functional design models provide the foundation for the development of a QA architecture and management system.Originality/valueThis systematically integrated view and the corresponding QA plan can pose fundamental questions to the AM community and initiate new research efforts in the in-situ digital inspection of AM processes and parts.

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.

The flexibility offered by additive manufacturing (AM) technologies to fabricate complex geometries poses several challenges to non-destructive evaluation (NDE) and quality control (QC) techniques. Existing NDE and QC techniques are not optimized for AM processes, materials, or parts. Such lack of reliable means to verify and qualify AM parts is a significant barrier to further industrial adoption of AM technologies. Electromechanical impedance measurements have been recently introduced as an alternative solution to detect anomalies in AM parts. With this approach, piezoelectric wafers bonded to the part under test are utilized as collocated sensors and actuators. Due to the coupled electromechanical characteristics of piezoelectric materials, the measured electrical impedance of the piezoelectric wafer depends on the mechanical impedance of the part under test, allowing build defects to be detected. This paper investigates the effectiveness of impedance-based NDE approach to detect internal porosity in AM parts. This type of build defects is uniquely challenging as voids are normally embedded within the structure and filled with unhardened model or supporting material. The impact of internal voids on the electromechanical impedance of AM parts is studied at several frequency ranges.

The widespread adoption of additive manufacturing (AM) in different industries has accelerated the need for quality control of these AM parts. Some of the complex and labor-intensive challenges associated with qualification and certification of AM parts are addressed by modeling and monitoring process conditions. Quantifying melt-track process conditions remains a significant computational challenge due to the large-scale differential between melt pool and part volumes. This work explores a novel point field (PF) driven AM model-based process metric (AM-PM) approach for calculating melt track resolved process conditions with maximal computational speed. A cylindrical Ti-6Al-4V test article with 16 equiangular zones having varied process parameters was built. The melt-track resolved AM-PMs were calculated and mapped to porosity existence for the 5.8-million-point PF of the test article. AM-PMs were calculated in 6.5 min, ~ 665 × faster than a similarly sized finite element calculation. This approach enables efficient prediction, assessment, and adjustment of AM builds.Graphical abstract

Additive manufacturing (AM) has significant advantages, including design freedom, mass customization, and complex-structure manufacturing capabilities. As reducing the manufacturing energy is challenging in terms of the industrial sustainability, the AM energy consumption must be further explored. Existing energy consumption quantitation methods for AM require complex models that considering the energy characteristics of equipment. Therefore, we propose a feature-based energy consumption quantitation method for complex AM parts using simple models and suitable for different AM technologies. First, a feature segmentation method is proposed to divide complex AM parts into typical AM features. Then, the energy consumption model for each AMF is developed for energy consumption quantification during part fabrication. Finally, the energy consumption of a typical mechanical part manufactured via three different AM processes is investigated using the proposed method and measured experimentally. The results show that the proposed method can effectively and rapidly predict the energy consumption of AM processes with an accuracy of more than 95 %. Furthermore, the efficiency of the three AM processes is compared and discussed to address suitable efficiency improvement methods. In general, the proposed method can be integrated into three-dimensional AM models, providing a reference for the structural optimization of AM parts and sustainable manufacturing.

Additive manufacturing (AM) has been explored by the automotive, aerospace and medical industries for many years but has yet to achieve wide-spread acceptance. This is partially due to the lack of standard frameworks for the exchange of data related to design, modeling, build plan, monitoring, control, and verification. Here, a unified paradigm, built on Extensible Markup language (XML) -based file formats and influenced by the ASTM F291 standard, is proposed, to record and transmit data at every stage of the AM process. This digital thread contains all essential parameters, from design to testing of metal-based AM parts while remaining accessible, traceable and extensible.

Additive manufacturing (AM) has recently been accorded considerable interest by manufacturers. Many manufacturing industries, amongst others in the aerospace sector, are already using AM parts or are investing in such manufacturing methods. Important material properties, such as microstructures, residual stress, and surface topography, can be affected by AM processes. In addition, a subtractive manufacturing (SM) process, such as machining, is required for finishing certain parts when accurate tolerances are required. This finish machining will subsequently affect the surface integrity and topography of the material. In this research work, we focused on the surface integrity of Ti-6Al-4V parts manufactured using three different types of AM and finished using an SM step. The aim of this study was to gain an understanding on how each process affects the resulting surface integrity of the material. It was found that each AM process affects the materials’ properties differently and that clear differences exist compared to a reference material manufactured using conventional methods. The newly generated surface was investigated after the SM step and each combination of AM/SM resulted in differences in surface integrity. It was found that different AM processes result in different microstructures which in turn affect surface integrity after the SM process.

Additive manufacturing (AM) is a modern manufacturing technique that facilitates the production of components layer by layer from CAD files, with more recent developments in the field leading to the ability to create these components from metal. Laser powder bed fusion (LPBF) is one of the many techniques used to manufacture metallic components and has drawn significant attention for its ability to create parts with high degrees of complexity, exceptional strength-to-weight ratios and internal structures. However, parts produced by AM are documented to suffer from build defects such as porosity, which can negatively affect not only its mechanical properties but its corrosion resistance, particularly its resistance to pitting corrosion. Whilst the mechanical properties of components produced through metal AM have been well documented since the technology’s inception, there are significant knowledge gaps in understanding the corrosion behaviour of metals produced in this way. This thesis aims to expand upon the current understanding of this manufacturing method with a particular focus on its corrosion resistance. High Entropy Alloys (HEAs) are a class of advanced materials that differ from conventional alloys in composition. Traditional alloys usually consist of one or two principal elements with smaller amounts of additional elements to impart specific properties. In contrast, HEAs are characterised by the presence of multiple principal elements in roughly equal proportions. HEAs' complex and disordered structure can result in unique mechanical, thermal, and magnetic properties. HEAs have shown promise in exhibiting high strength, hardness, and corrosion resistance, making them attractive for various engineering applications. Studies of HEAs have been increasing over recent years; however, significant knowledge gaps are still associated with this classification of materials, especially concerning their corrosion resistance. This lack of knowledge is intensified when discussing the properties of these alloys when manufactured by AM methods. LPBF was used to produce parts in 316L with process induced porosity by manipulating the process parameters to investigate the effect density has on the corrosion resistance of AM parts. The corrosion resistance of these parts were compared to their wrought counterpart using potentiodynamic polarisation. It was observed that increasing the porosity in the AM parts resulted in poorer corrosion resistance, both by weaker performance across key metrics and a greater degree of unreliability. It was also found that the AM parts proved to have a greater corrosion resistance than the wrought material. However, the decreased consistency in this resistance is often cited as a barrier these components must overcome to supplace conventionally manufactured components.316L was also produced through induction casting as well as a schedule more representative of industry that consisted of a solution anneal at 1080 °C followed by water quenching followed by a cold rolling reduction by 70 %, and a final anneal at 900 °C. The microstructures and corrosion resistance of these were investigated using SEM-EDS, XRD and potentiodynamic polarisation, and whilst the corrosion resistance of the cold rolled sample had increased, it was less than expected due to the formation of detrimental chromium carbides. A Swansea University developed AlCrFeMnNi HEA was put through the same 3 manufacturing processes to investigate their effect on the microstructure and corrosion resistance. It was found that, unlike 316L, the HEA suffered less from pitting corrosion and more from a generalised corrosion attack. Very similar corrosion results were seen across the manufacturing methods; however, the cast sample was observed to have the most consistent display of corrosion resistance. Based on the pitting resistance equivalent number, which relates the amount of Cr and Mo by wt.% in a stainless steel to its corrosion resistance, it was theorised that the addition of Mo to this HEA could also increase its corrosion resistance. The results were inconclusive; however, better corrosion resistance was seen in the AM sample of the HEA with the addition than in the AM sample without.

This study investigates the application of electroless nickel deposition on additively manufactured stainless steel samples. Current additive manufacturing (AM) technologies produce metal components with a rough surface. Rough surfaces generally exhibit fatigue characteristics, increasing the probability of initiating a crack or fracture to the printed part. For this reason, the direct use of as-produced parts in a finished product cannot be actualized, which presents a challenge. Post-processing of the AM parts is therefore required to smoothen the surface. This study analyzes chempolish (CP) and electropolish (EP) surface finishing techniques for post-processing AM stainless steel components CP has a great advantage in creating uniform, smooth surfaces regardless of size or part geometry EP creates an extremely smooth surface, which reduces the surface roughness to the sub-micrometer level. In this study, we also investigate nickel deposition on EP, CP, and as-built AM components using electroless nickel solutions. Electroless nickel plating is a method of alloy treatment designed to increase manufactured component’s hardness and surface resistance to the unrelenting environment. The electroless nickel plating process is more straightforward than its counterpart electroplating. We use low-phosphorus (2–5% P), medium-phosphorus (6–9% P), and high-phosphorus (10–13% P). These Ni deposition experiments were optimized using the L9 Taguchi design of experiments (TDOE), which compromises the prosperous content in the solution, surface finish, plane of the geometry, and bath temperature. The pre- and post-processed surface of the AM parts was characterized by KEYENCE Digital MicroscopeVHX-7000 and Phenom XL Desktop SEM. The experimental results show that electroless nickel deposition produces uniform Ni coating on the additively manufactured components up to 20 μm per hour. Mechanical properties of as-built and Ni coated AM samples were analyzed by applying a standard 10 N scratch test. Nickel coated AM samples were up to two times scratch resistant compared to the as-built samples. This study suggests electroless nickel plating is a robust viable option for surface hardening and finishing AM components for various applications and operating conditions.

In this study, we present the first results of a newly developed melt pool monitoring tool for selective laser melting, called DMP-meltpool. A manual data analysis method is given, and the events indicated by the analysis (DMP-meltpool events) are shown to correlate to the static tensile properties of the samples built. These events indicate the probability of material discontinuities (defects) in the metal additive manufacturing (AM) parts. In order to do so, cylindrical bars of Ti-6Al-4V ELI were built and monitored using DMP-meltpool. The tensile properties of the printed cylinders were correlated with the events detected by DMP-meltpool. An inverse relation between plastic elongation and the DMP-meltpool event density was observed. These results show that DMP-meltpool can be used to predict the quality of AM parts by detecting variations in the signals and tagging these events throughout the build as defects. Thus the technique can be employed for first stage in-line quality control of AM parts and for sorting out parts with potential defects non-destructively. The DMP-meltpool events could have significant correlations with other mechanical properties (like fatigue, hardness, fracture toughness, and crack propagation) since such properties are influenced by defects originating from the process instabilities.

The rapid growth of Additive Manufacturing (AM) in the past decade has demonstrated a significant potential in cost-effective production with a superior quality product. A numerical simulation is a steep way to learn and improve the product quality, life cycle, and production cost. To cope with the growing AM field, researchers are exploring different techniques, methods, models to simulate the AM process efficiently. The goal is to develop a thermo-mechanical weld model for the Directed Energy Deposition (DED) process for 316L stainless steel at an efficient computational cost targeting to model large AM parts in residual stress calculation. To adapt the weld model to the DED simulation, single and multi-track thermal simulations were carried out. Numerical results were validated by the DED experiment. A good agreement was found between predicted temperature trends for numerical simulation and experimental results. A large number of weld tracks in the 3D solid AM parts make the finite element process simulation challenging in terms of computational time and large amounts of data management. The method of activating elements layer by layer and introducing heat in a cyclic manner called a thermal cycle heat input was applied. Thermal cycle heat input reduces the computational time considerably. The numerical results were compared to the experimental data for thermal and residual stress analyses. A lumping of layers strategy was implemented to reduce further computational time. The different number of lumping layers was analyzed to define the limit of lumping to retain accuracy in the residual stress calculation. The lumped layers residual stress calculation was validated by the contour cut method in the deposited sample. Thermal behavior and residual stress prediction for the different numbers of a lumped layer were examined and reported computational time reduction.

Additive manufacturing (AM) has revolutionized the production of complex geometrical parts with metals; however, the usual layer-by-layer deposition results in poor surface quality and unpredictable surface integrity. Abrasive machining and finishing techniques play vital roles in counteracting these challenges and qualifying AM parts for practical applications. This review aims to present recent research developments concerning the machining of additively manufactured metal parts via both conventional and nonconventional abrasive machining methods. Conventional methods such as grinding, milling, polishing, honing, and sandblasting have been widely investigated for their ability to enhance the surface finish, dimensional accuracy, and mechanical properties of AM metal components. However, the characteristic features of various AM processes, such as porosity, microstructural features, and residual stresses, can significantly influence the machinability of the produced parts. Nonconventional methods such as abrasive flow machining, electrochemical machining, magnetic abrasive finishing, and vibratory bowl finishing, on the other hand, have shown potential in addressing the difficulties associated with internal machining geometries and hard-to-machine material combinations that are typical for many AM parts. This review also highlights some challenges and future trends in the machining of AM metal parts and emphasizes that further research is required in the direction of combinations of various postprocessing techniques, machinability regarding new alloy compositions, and the integration of AI for process optimization. As the demand for high-precision AM parts grows across various industries, the advancement of abrasive machining and finishing techniques is crucial for driving the wider adoption of AM technologies.

Additive manufacturing (AM) has gained notoriety for offering advantages over traditional manufacturing methods, such as increased design complexity and flexibility. However, it has not found widespread use beyond rapid prototyping. One hindrance to the acceptance of AM processes in industry is the time and cost of fabrication per component. While metal AM by itself can be inexpensive, extra manufacturing steps in the form of subtractive manufacturing (SM) may need to be performed to reach final part tolerances, leading to hybrid additive-subtractive manufacturing (HASM) of a part, which increases time and cost. A potential area to reduce cost is through increasing the efficiency of the HASM process by conducting additive and subtractive manufacturing simultaneously. Usually, HASM is performed in a process where AM is completed in one machine or cell and transferred to another machine or cell for SM in a sequential assembly line process. This efficiency decreases part cost, but high aspect ratio parts or parts with internal geometry that require interleaved additive deposition and machining cannot be produced. One unexplored solution to simultaneous HASM that allows for interleaved operations is to operate the deposition head and machining spindle concurrently within the same machine envelope, known as concurrent HASM (CHASM). In this type of process, both AM and SM occur simultaneously on a batch of small parts or a single large part, maintaining a high efficiency without sacrificing the full range of complex geometries that AM allows for. A potential approach to the single-machine method could be to combine a robot and mill within the same envelope. A challenge to this approach, however, is control of both systems. Most machine controllers have limited external communication or, if a robot has been integrated, only offer movement of either the robot or mill at any given time. As a result, systems must pause either the AM or SM process to switch between them rather than working simultaneously. The present work investigates the positional accuracy of such a CHASM system comprised of a robotic arm and a 3-axis mill. Open-loop tests with limited communication between machines are performed on the system to verify positional error during concurrent robot-mill movements. Under certain conditions, it is demonstrated that position error can stay within 2 mm for the duration of a single layer; however, these tests show that, generally, the open-loop positioning performance of the system is inadequate for CHASM without part-specific hand-tuning of parameters. Based on these results, a set of requirements for successful robot-CNC CHASM is proposed for future integrations.

Additive manufacturing (AM) has revolutionized the manufacturing industry by offering flexibility, customization, and rapid prototyping capabilities. Complex geometries and low volume parts can be produced via AM in much lower lead time and cost compared to traditional manufacturing methods. However, ensuring the quality and reliability of AM parts remains a significant challenge due to variations in material properties and process parameters. Traditional material property analysis methods, that require experimental testing and computational modeling, are often costly and time-consuming and often not applicable to the complex geometries of AM components. These challenges are further magnified when repetitive sampling is required. To address these challenges, this paper presents a novel approach that combines experimental findings with Finite Element Modeling (FEM) to construct a digital twin (DT) model of AM parts. The DT model will serve as a virtual representation along with real-time monitoring, simulation, and optimization capabilities. Numerical simulations are conducted to correlate the resonance frequency of parts at different process conditions with their material characteristics, represented as RF Z-score values. Hence, the results suggest that such an approach provides manufacturers with cost-effective and efficient means of analyzing material properties and optimizing AM processes. Moreover, by leveraging digital twin technology, manufacturers can achieve greater process control and quality assurance in additive manufacturing. The findings of this study contribute to advancing the understanding and implementation of digital twin technology in AM; therefore, paving the way for enhanced productivity and reliability in additive manufacturing processes.

This research utilizes linear and nonlinear ultrasonic techniques to establish a linkage between microstructure and macroscale mechanical properties of additively manufactured (AM) stainless steel 316L samples. The specimens are manufactured using two methods: laser-powder bed fusion and traditional wrought manufacturing. Using the nonlinear ultrasonic method of second harmonic generation, the acoustic nonlinearity parameter is estimated in samples with different heat treatment levels intended to alter microstructural and mechanical properties. Linear ultrasonic parameters including wave speed and resonant frequency are additionally measured. Mechanical properties are obtained through tensile testing of coupons corresponding to the test samples. Microstructural information for the samples is obtained using electron backscatter diffraction to help elucidate the relationships between microstructure, mechanical properties, and ultrasonic response. Results indicate correlations between the nonlinearity parameter and both ultimate tensile strength and yield strength, where nonlinearity generally decreases as sample strength increases, particularly in the AM samples. We hypothesize that microstructural evolution of grain characteristics across different heat treatments influences trends in measured nonlinearity, as well as substructures at smaller scales such as dislocations. These results show promising evidence for the feasibility of AM parts qualification using nondestructive nonlinear ultrasonic testing.