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  1. Free, publicly-accessible full text available June 1, 2025
  2. The reliability of additively manufactured flexible electronics or so-called printed electronics is defined as mean time to failure under service conditions, which often involve mechanical loads. It is thus important to understand the mechanical behavior of the printed materials under such conditions to ensure their applicational reliability in, for example, sensors, biomedical devices, battery and storage, and flexible hybrid electronics. In this article, a testing protocol to examine the print quality of additively nanomanufactured electronics is presented. The print quality is assessed by both tensile and electrical resistivity responses during in-situ tension tests. A laser based additive nanomanufacturing method is used to print conductive silver lines on polyimide substrates, which is then tested in-situ under tension inside a scanning electron microscope (SEM). The surface morphology of the printed lines is continuously monitored via the SEM until failure. In addition, the real-time electrical resistance variations of the printed silver lines are measured in-situ with a multimeter during tensile tests conducted outside of the SEM. The protocol is shown to be effective in assessing print quality and aiding process tuning. Finally, it is revealed that samples appearing identical under the SEM can have significant different tendencies to delaminate. 
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    Free, publicly-accessible full text available December 1, 2024
  3. Printed electronics are gaining significant interest due to their design flexibility, low fabrication cost, and rapid design-to-manufacturing turnaround. Conventional substrates for printed electronics are often based on nonbiodegradable polymers such as polyimide that pose high environmental challenges by creating massive e-waste and pollution. As the demand for printed electronics and sensors increases, the ability to print such devices on biodegradable substrates can provide a solution to such environmental problems. However, current printing technologies are based on liquids and inks that are incompatible with biodegradable substrates, such as paper. Here, we present a dry-printing process, namely, a dry additive nanomanufacturing (Dry-ANM) technique, for printing conductive silver lines and patterns on biodegradable papers for flexible hybrid papertronics. Pure and dry nanoparticles are generated by pulsed laser ablation of a silver target that is then transported through a nozzle and directed onto paper substrates, where they are deposited and laser-sintered in real time to form the desired pattern without damaging the paper. The effects of different printing parameters on the paper-burning threshold are investigated, and the electrical properties of the lines are characterized by using different line thicknesses and sintering laser power densities. In addition, the mechanical and electrical properties of the printed lines and patterns are evaluated by bending and twisting tests. Furthermore, the feasibility of printing silver on different paper types is demonstrated. This research can potentially lead to biodegradable and environmentally friendly printed electronics and sensors. 
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    Free, publicly-accessible full text available November 3, 2024
  4. Additively manufactured electronics (AMEs), also known as printed electronics, are becoming increasingly important for the anticipated Internet of Things (IoT). This requires manufacturing technologies that allow the integration of various pure functional materials and devices onto different flexible and rigid surfaces. However, the current ink-based technologies suffer from complex and expensive ink formulation, ink-associated contaminations (additives/solvents), and limited sources of printing materials. Thus, printing contamination-free and multimaterial structures and devices is challenging. Here, a multimaterial additive nanomanufacturing (M-ANM) technique utilizing directed laser deposition at the nano and microscale is demonstrated, allowing the printing of lateral and vertical hybrid structures and devices. This M-ANM technique involves pulsed laser ablation of solid targets placed on a target carousel inside the printer head for in-situ generation of contamination-free nanoparticles, which are then guided via a carrier gas toward the nozzle and onto the surface of the substrate, where they are sintered and printed in real-time by a second laser. The target carousel brings a particular target in engagement with the ablation laser beam in predetermined sequences to print multiple materials, including metals, semiconductors, and insulators, in a single process. Using this M-ANM technique, various multimaterial devices such as silver/zinc oxide (Ag/ZnO) photodetector and hybrid silver/aluminum oxide (Ag/Al2O3) circuits are printed and characterized. The quality and versatility of our M-ANM technique offer a potential manufacturing option for emerging IoT. 
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    Free, publicly-accessible full text available August 1, 2024
  5. Free, publicly-accessible full text available July 1, 2024
  6. Three typical types of defects, i.e., keyholes, lack of fusion (LoF), and gas-entrapped pores (GEP), characterized by various features (e.g., volume, surface area, etc.), are generated under different process parameters of laser beam powder bed fusion (L-PBF) processes in additive manufacturing (AM). The different types of defects deteriorate the mechanical performance of L- PBF components, such as fatigue life, to a different extent. However, there is a lack of recognized approaches to classify the defects automatically and accurately in L-PBF components. This work presents a novel hierarchical graph convolutional network (H-GCN) to classify different types of defects by a cascading GCN structure with a low-level feature (e.g., defect features) layer and a high-level feature (e.g., process parameters) layer. Such an H-GCN not only leverages the multi- level information from process parameters and defect features to classify the defects but also explores the impact of process parameters on defect types and features. The H-GCN is evaluated through a case study with X-ray computed tomography (CT) L-PBF defect datasets and compared with several machine learning methods. H-GCN exhibits an outstanding classification performance with an F1-score of 1.000 and reveals the potential effect of process parameters on three types of defects. 
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  7. Laser beam powder bed fusion (LB-PBF) is a widely-used metal additive manufacturing process due to its high potential for fabrication flexibility and quality. Its process and performance optimization are key to improving product quality and promote further adoption of LB-PBF. In this article, the state-of-the-art machine learning (ML) applications for process and performance optimization in LB-PBF are reviewed. In these applications, ML is used to model the process-structure–property relationships in a data-driven way and optimize process parameters for high-quality fabrication. We review these applications in terms of their modeled relationships by ML (e.g., process—structure, process—property, or structure—property) and categorize the ML algorithms into interpretable ML, conventional ML, and deep ML according to interpretability and accuracy. This way may be particularly useful for practitioners as a comprehensive reference for selecting the ML algorithms according to the particular needs. It is observed that of the three types of ML above, conventional ML has been applied in process and performance optimization the most due to its balanced performance in terms of model accuracy and interpretability. To explore the power of ML in discovering new knowledge and insights, interpretation with additional steps is often needed for complex models arising from conventional ML and deep ML, such as model-agnostic methods or sensitivity analysis. In the future, enhancing the interpretability of ML, standardizing a systemic procedure for ML, and developing a collaborative platform to share data and findings will be critical to promote the integration of ML in LB-PBF applications on a large scale. 
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  8. Abstract

    Volumetric defect types commonly observed in the additively manufactured parts differ in their morphologies ascribed to their formation mechanisms. Using high-resolution X-ray computed tomography, this study analyzes the morphological features of volumetric defects, and their statistical distribution, in laser powder bed fused Ti-6Al-4V. The geometries of three common types of volumetric defects; i.e., lack of fusions, gas-entrapped pores, and keyholes, are quantified by nine parameters including maximum dimension, roundness, sparseness, aspect ratio, and more. It is shown that the three defect types share overlaps of different degrees in the ranges of their morphological parameters; thus, employing only one or two parameters cannot uniquely determine a defect’s type. To overcome this challenge, a defect classification methodology incorporating multiple morphological parameters has been proposed. In this work, by employing the most discriminating parameters, this methodology has been shown effective when implemented into decision tree (>98% accuracy) and artificial neural network (>99% accuracy).

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