Multi-laser scan assignment and scheduling optimization for large-scale metal additive manufacturing

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.

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

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 microstructure of metals depends on the additive manufacturing (AM) process and the process parameters. However, experimentation on different process parameters for different materials is costly and time-consuming. To overcome these challenges, numerical simulations provide insights that allow prediction of microstructure formed under different process parameters. Microstructure modelling requires coupling of macro-scale thermal model or experimentally measured temperature profiles with either a meso-scale or micro-scale microstructure model. In this review, the commonly used AM techniques for metals are introduced, followed by a discussion on the microstructure of parts fabricated using these processes. This review then presents the latest models used in simulating different microstructural aspects of metal AM parts. In addition, the models were compared and the potential and challenges in microstructure modelling were discussed.

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 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 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.

Nondestructive evaluation of micro-oxide inclusions in additively manufactured metal parts using nonlinear ultrasonic technique

Uncertainty quantification (UQ) in metal additive manufacturing (AM) has attracted tremendous interest in order to dramatically improve product reliability. Model-based UQ, which relies on the validity of a computational model, has been widely explored as a potential substitute for the time-consuming and expensive UQ solely based on experiments. However, its adoption in the practical AM process requires overcoming two main challenges: (1) the inaccurate knowledge of uncertainty sources and (2) the intrinsic uncertainty associated with the computational model. Here, we propose a data-driven framework to tackle these two challenges by combining high throughput physical/surrogate model simulations and the AM-Bench experimental data from the National Institute of Standards and Technology (NIST). We first construct a surrogate model, based on high throughput physical simulations, for predicting the three-dimensional (3D) melt pool geometry and its uncertainty with respect to AM parameters and uncertainty sources. We then employ a sequential Bayesian calibration method to perform experimental parameter calibration and model correction to significantly improve the validity of the 3D melt pool surrogate model. The application of the calibrated melt pool model to UQ of the porosity level, an important quality factor, of AM parts, demonstrates its potential use in AM quality control. The proposed UQ framework can be generally applicable to different AM processes, representing a significant advance toward physics-based quality control of AM products.

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.

Additive manufacturing (AM) offers a number of benefits over conventional manufacturing, giving an increased design freedom and the opportunity to integrate multiple components, saving weight. The rigorous standard for material integrity set by the Aerospace sector necessitates the development of systems to ensure quality. Laser ultrasonic testing (LU) is a non-contact inspection technique which has been proposed as suitable for in-situ monitoring of additive manufacturing processes, as measurements can be taken at elevated temperatures. This paper will show the capability of this technique for assuring material quality in metal AM parts and compare LU with x-ray computed tomography (XCT).

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.

Electrochemical finishing of metallic surfaces is a scalable, relatively cost-effective technique that is also versatile. One of the main detractions is that the current distribution is uneven and unspecific, often leading to a lack of shape retention and, if it goes too far, inducing corrosion. Pulsing the electrical input creates an unsteady state during electropolishing, controlling the current distribution across the part. The pulse wave prevention of general non-uniform current distribution across the entire geometry instead produces local non-uniformities in the current distribution at given surface features. Many pulse electropolishing parameters can be varied to target individual surface features, including the electrolyte, pulse height, pulse width (equally and unequally distributed), time of polishing as well as the overall waveform.Additive manufacturing (AM) can produce unique, complex structures rapidly. The fast local melting of metal AM parts cool quickly due to the small area, causing unique microstructures, pores, and larger surface roughness compared to cast. AM microstructures change mechanical and chemical properties, such as strength and corrosion, compared to traditionally manufactured parts. Variations in build parameters and composition creates a complicated, feature dependent, corrosion response. By polishing the surface, some of the features, namely the corrosion resistance, wear, and aesthetics can be standardized and enhanced.In this work, we explore the underlying electrochemistry that governs pulse electropolishing. We show that controlling the electrolyte with environmentally benign chemicals ultimately influences the part dimensions that can be polished, the ability to polish, and the propensity to corrode. The pulse height, pulse width (Fig. 1), and total charge passed have an impact on different surface feature removal. We will demonstrate impact of the pulse parameters on the polishing of additively manufactured stainless steel. We go beyond simple planar geometry and apply our understanding to larger scale complex geometry parts, while maintaining near-net shape and minimizing mass loss. Figure 1

With growing interest in metal additive manufacturing (AM) for aerospace and defence applications, it is important to understand the influence of processing conditions on high strain rate behaviour. This paper presents research on as-built stainless steel 316L AM parts processed using laser powder bed fusion (L-PBF) also referred to as selective laser melting (SLM). Two sets of varied process parameters were examined as well as different build orientations. Laser scan speed and point-to-point distance are shown to have influenced the high strain rate mechanical properties of 316L AM parts. Additionally, both wrought and SLM produced 316L showed strain rate sensitivity (hardening) at strain rates of 103 s1, but this effect was more pronounced in the wrought material. Findings from this study can be used to determine appropriate part orientations and AM process parameters and their influence on the high strain rate behaviour and impact performance of L-PBF AM parts.

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.