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1. Nature-inspired materials and structures using 3D Printing

   https://doi.org/doi.org/10.1016/j.mser.2021.100609  

   Bandyopadhyay, Amit; Traxel, Kellen; Bose, Susmita (July 2021, Materials science engineering)

   null (Ed.)

   Emulating the unique combination of structural, compositional, and functional gradation in natural materials is exceptionally challenging. Many natural structures have proved too complex or expensive to imitate using traditional processing techniques despite recent advances. Recent innovations within the field of additive manufacturing (AM) or 3D Printing (3DP) have shown the ability to create structures that have variations in material composition, structure, and performance, providing a new design-for-manufacturing platform for the imitation of natural materials. AM or 3DP techniques are capable of manufacturing structures that have significantly improved properties and functionality over what could be traditionally-produced, giving manufacturers an edge in their ability to realize components for highly-specialized applications in different industries. To this end, the present work reviews fundamental advances in the use of naturally-inspired design enabled through 3DP / AM, how these techniques can be further exploited to reach new application areas and the challenges that lie ahead for widespread implementation. An example of how these techniques can be applied towards a total hip arthroplasty application is provided to spur further innovation in this area. 

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2. 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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3. Genetic Algorithm-Based Data-Driven Process Selection System for Additive Manufacturing in Industry 4.0

   https://doi.org/10.3390/ma17184544  

   Aljabali, Bader Alwomi; Shelton, Joseph; Desai, Salil (September 2024, Materials)

   Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes. 

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4. Development of Embeddable Additive Manufacturing Microsensors for Structural Health Monitoring

   https://doi.org/10.1115/SMASIS2022-91047  

   Reed, Nicholas; Kim, Daewon (September 2022, American Society of Mechanical Engineers)

   Significant progress into the development and use of stretchable sensors for structural health monitoring (SHM) has been made in the last several years. The fusion of stretchable, adaptable sensing materials with highly specialized additive manufacturing techniques allows for the development of highly adaptive, customizable, and easily accessible sensing solutions. However, a significant portion of these works explore SHM topics at a macro level, and with a reduced focus on implementation. As such, little application or experimentation into practical sensing elements, especially those at the micro scale, have followed the advances in sensing technology. In this work, we demonstrate the application of recent developments in stretchable electronics, alongside multiple advanced additive manufacturing processes, to develop a novel flexible microscale sensor. A complex sensor is designed and printed utilizing Digital Light Processing (DLP) to directly fabricate the structure. The printed sensor is then filled with a piezoresistive sensing element of either PEDOT:PSS or carbon-based PDMS (cPDMS), which provided strain readings via resistance change. After being filled with a sensing mixture, the sensor is shown to operate as desired under large deformations. Additionally, the sensor is shown to work effectively when embedded into a separate additively manufactured part. A flexible test coupon is manufactured using the DLP AM process, and a microsensor is embedded inside the coupon structure. This sensing systems is tested in both tension and bending. These results show the feasibility of implementing both modern day AM processes and into current structural health monitoring developments into practical applications. 

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5. 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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