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1. Inexpensive Verification via Electromechanical Impedance for Additively Manufactured Parts

   S. M. Strutner, C. Tenney (January 2018, SAMPE Conference)

   Currently, verifying additively manufactured (AM) parts requires time consuming and expensive nondestructive evaluation (NDE) processes such as computed tomography (CT) x-ray scanning. While such methods provide details on flaw type and location, they require significant cost and time. Often, in production environments, significant value is gained by rapidly screening part specimens for flaws at all. Cost-effective per-specimen testing for production runs of AM parts is important for their use to be economically justified. In this work, Northrop Grumman Corporation and Virginia Tech explored impedance-based testing as a means to evaluate AM titanium specimens. Specimens with and without manually-designed flaws were fabricated through a metal- based AM process and evaluated using the impedance-based technique. CT scans confirmed that the intended flaws in the experimental specimens were present. Impedance-based examination also showed the presence of unintended defects. After machining away the unintended defective regions, the flaw-containing defective specimen had a clearly different impedance ‘signature’ than non-flawed baseline specimens. Additional analysis confirmed that the impedance test method required cheaper capital equipment and required less technician time to examine test results. Taken together, this means that the impedance-based this method can reduce the total cost of utilizing AM for metal part manufacturing. 

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2. Nondestructive Testing for Metal Parts Fabricated Using Powder Based Additive Manufacturing

   Koester, L; Taheri, H; Bigelow, T; Bond, L (April 2018, Materials evaluation)

   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. 

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3. Rapid Development of Metal Additive Manufacturing Using Artificial Intelligence/Machine Learning and High-Throughput Material Testing

   https://doi.org/10.1146/annurev-matsci-080423-121436  

   Adapa, Venkata_Surya Karthik; Kalidindi, Surya R; Saldana, Christopher J (July 2025, Annual Review of Materials Research)

   Metal additive manufacturing (AM) holds immense potential for developing advanced structural alloys. However, the complex, heterogeneous nature of AM-produced materials presents significant challenges to traditional material characterization and optimization methods. This review explores the integration of artificial intelligence (AI) and machine learning (ML) with high-throughput material characterization protocols to rapidly establish the process–structure–property (PSP) relationships critically needed to dramatically accelerate the development of metal AM processes. Combinatorial high-throughput evaluations, including rapid material synthesis and nonstandard high-throughput testing protocols, such as spherical indentation and small punch tests, are discussed for their capability to rapidly assess mechanical properties and establish PSP linkages. Furthermore, the review examines the role of AI and ML in optimizing AM processes, particularly through Bayesian optimization, which offers new avenues for efficient exploration of high-dimensional design spaces. The review envisions a future where AI- and ML-driven, autonomous AM development cycles significantly enhance material and process optimization. 

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4. A printability assessment framework for fabricating low variability nickel-niobium parts using laser powder bed fusion additive manufacturing

   https://doi.org/10.1108/RPJ-01-2021-0024  

   Zhang, Bing; Seede, Raiyan; Whitt, Austin; Shoukr, David; Huang, Xueqin; Karaman, Ibrahim; Arroyave, Raymundo; Elwany, Alaa (July 2021, Rapid Prototyping Journal)

   Purpose There is recent emphasis on designing new materials and alloys specifically for metal additive manufacturing (AM) processes, in contrast to AM of existing alloys that were developed for other traditional manufacturing methods involving considerably different physics. Process optimization to determine processing recipes for newly developed materials is expensive and time-consuming. The purpose of the current work is to use a systematic printability assessment framework developed by the co-authors to determine windows of processing parameters to print defect-free parts from a binary nickel-niobium alloy (NiNb5) using laser powder bed fusion (LPBF) metal AM. Design/methodology/approach The printability assessment framework integrates analytical thermal modeling, uncertainty quantification and experimental characterization to determine processing windows for NiNb5 in an accelerated fashion. Test coupons and mechanical test samples were fabricated on a ProX 200 commercial LPBF system. A series of density, microstructure and mechanical property characterization was conducted to validate the proposed framework. Findings Near fully-dense parts with more than 99% density were successfully printed using the proposed framework. Furthermore, the mechanical properties of as-printed parts showed low variability, good tensile strength of up to 662 MPa and tensile ductility 51% higher than what has been reported in the literature. Originality/value Although many literature studies investigate process optimization for metal AM, there is a lack of a systematic printability assessment framework to determine manufacturing process parameters for newly designed AM materials in an accelerated fashion. Moreover, the majority of existing process optimization approaches involve either time- and cost-intensive experimental campaigns or require the use of proprietary computational materials codes. Through the use of a readily accessible analytical thermal model coupled with statistical calibration and uncertainty quantification techniques, the proposed framework achieves both efficiency and accessibility to the user. Furthermore, this study demonstrates that following this framework results in printed parts with low degrees of variability in their mechanical properties. 

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5. Challenges and Opportunities for Integrating Dealloying Methods into Additive Manufacturing

   https://doi.org/10.3390/ma13173706  

   Chuang, A.; Erlebacher, J. (September 2020, Materials)

   null (Ed.)

   The physical architecture of materials plays an integral role in determining material properties and functionality. While many processing techniques now exist for fabricating parts of any shape or size, a couple of techniques have emerged as facile and effective methods for creating unique structures: dealloying and additive manufacturing. This review discusses progress and challenges in the integration of dealloying techniques with the additive manufacturing (AM) platform to take advantage of the material processing capabilities established by each field. These methods are uniquely complementary: not only can we use AM to make nanoporous metals of complex, customized shapes—for instance, with applications in biomedical implants and microfluidics—but dealloying can occur simultaneously during AM to produce unique composite materials with nanoscale features of two interpenetrating phases. We discuss the experimental challenges of implementing these processing methods and how future efforts could be directed to address these difficulties. Our premise is that combining these synergistic techniques offers both new avenues for creating 3D functional materials and new functional materials that cannot be synthesized any other way. Dealloying and AM will continue to grow both independently and together as the materials community realizes the potential of this compelling combination. 

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