Metal additive manufacturing (AM) presents advantages such as increased complexity for a lower part cost and part consolidation compared to traditional manufacturing. The multiscale, multiphase AM processes have been shown to produce parts with non-homogeneous microstructures, leading to variability in the mechanical properties based on complex process–structure–property (p-s-p) relationships. However, the wide range of processing parameters in additive machines presents a challenge in solely experimentally understanding these relationships and calls for the use of digital twins that allow to survey a larger set of parameters using physics-driven methods. Even though physics-driven methods advance the understanding of the p-s-p relationships, they still face challenges of high computing cost and the need for calibration of input parameters. Therefore, data-driven methods have emerged as a new paradigm in the exploration of the p-s-p relationships in metal AM. Data-driven methods are capable of predicting complex phenomena without the need for traditional calibration but also present drawbacks of lack of interpretability and complicated validation. This review article presents a collection of physics- and data-driven methods and examples of their application for understanding the linkages in the p-s-p relationships (in any of the links) in widely used metal AM techniques. The review also contains amore »
Nondestructive Testing for Metal Parts Fabricated Using Powder Based Additive Manufacturing
Additive manufacturing (AM) presents unique challenges to the nondestructive testing community, not least in that it requires inspection of parts with complex forms that are not possible using subtractive manufacturing. The drive to use AM for parts where design approaches include damage tolerance and retirement-for-cause with high quality and where safety criticality imposes new
QA/QC requirements is growing. This article reviews the challenges faced to enable reliable inspection and characterization in metal powderbased
AM processes, including issues due to geometric and microstructural features of interest, the limitation on existing and emerging NDT techniques,
and remaining technology gaps. The article looks at inspection from powder to finished part, but focuses primarily on monitoring and characterization during the build. In-process, quantitative characterization and monitoring is anticipated to be transformational in advancing adoption of metal AM parts, including offering the potential for inprocess repair or early part rejection during part
fabrication.
- Award ID(s):
- 1661146
- Publication Date:
- NSF-PAR ID:
- 10088566
- Journal Name:
- Materials evaluation
- Volume:
- 76
- Page Range or eLocation-ID:
- 514-524
- ISSN:
- 0025-5327
- Sponsoring Org:
- National Science Foundation
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Abstract -
Nondestructive Evaluation of Additively Manufactured Metal Components with an Eddy Current TechniqueThe ability of Additive Manufacturing (AM) processes to ensure delivery of high quality metal-based components is somewhat limited by insufficient inspection capabilities. The inspection of AM parts presents particular challenges due to the design flexibility that the fabrication method affords. The nondestructive evaluation (NDE) methods employed need to be selected based on the material properties, type of possible defects, and geometry of the parts. Electromagnetic method, in particular Eddy Current (EC), is proposed for the inspections. This evaluation of EC inspection considers surface and near-surface defects in a stainless steel (SS) 17 4 PH additively manufactured sample and a SS 17 4 PH annealed plates manufactured traditionally (reference sample). The surfaces of the samples were polished using 1 micron polishing Alumina grit to achieve a mirror like surface finish. 1.02 mm (0.04”), 0.508 mm (0.02”) and 0.203 mm (0.008”) deep Electronic Discharge Machining (EDM) notches were created on the polished surface of the samples. Lift off and defect responses for both additive and reference samples were obtained using a VMEC-1 commercial instrument and a 500 kHz absolute probe. The inspection results as well as conductivity assessments for the AM sample in terms of the impedance plane signature were compared tomore »
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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 insteadmore »
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Purpose Due 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/approach A 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. Findings The 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/implications A 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.more »
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Abstract Laser-based additive manufacturing (LBAM) provides unrivalled design freedom with the ability to manufacture complicated parts for a wide range of engineering applications. Melt pool is one of the most important signatures in LBAM and is indicative of process anomalies and part defects. High-speed thermal images of the melt pool captured during LBAM make it possible for in situ melt pool monitoring and porosity prediction. This paper aims to broaden current knowledge of the underlying relationship between process and porosity in LBAM and provide new possibilities for efficient and accurate porosity prediction. We present a deep learning-based data fusion method to predict porosity in LBAM parts by leveraging the measured melt pool thermal history and two newly created deep learning neural networks. A PyroNet, based on Convolutional Neural Networks, is developed to correlate in-process pyrometry images with layer-wise porosity; an IRNet, based on Long-term Recurrent Convolutional Networks, is developed to correlate sequential thermal images from an infrared camera with layer-wise porosity. Predictions from PyroNet and IRNet are fused at the decision-level to obtain a more accurate prediction of layer-wise porosity. The model fidelity is validated with LBAM Ti–6Al–4V thin-wall structure. This is the first work that manages to fuse pyrometermore »
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