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This study models the temperature evolution during additive friction stir deposition (AFSD) using machine learning. AFSD is a solid-state additive manufacturing technology that deposits metal using plastic flow without melting. However, the ability to predict its performance using the underlying physics is in the early stage. A physics-informed machine learning approach, AFSD-Nets, is presented here to predict temperature profiles based on the combined effects of heat generation and heat transfer. The proposed AFSD-Nets includes a set of customized neural network approximators, which are used to model the coupled temperature evolution for the tool and build during multi-layer material deposition. Experiments are designed and performed using 7075 aluminum feedstock deposited on a substrate of the same material for 30 layers. A comparison of predictions and measurements shows that the proposed AFSD-Nets approach can accurately describe and predict the temperature evolution during the AFSD process.

Environment-induced cracking (EIC) research spanning the last 80 years for ferrous and non-ferrous metals in aqueous environments at ambient and elevated temperatures has concentrated on crack propagation. Studies clearly reveal EIC involves two differentiable processes, one controlling initiation and the other propagation. Utilization of advanced high-resolution electron microscopy over the last 20 years has enabled more focused studies of crack initiation for stainless steel and nickel-based alloys at elevated temperatures exposed to environments associated with the nuclear industry. More recently, when coupled with advancedin-situexperimental techniques such as time-lapse X-ray computed 3D-tomography, progress has also been made for aluminum alloys suffering EIC at ambient temperatures. Conventional wisdom states that chemical processes are typically rate-controlling during EIC initiation. Additionally, experimental evidence based on primary creep exhaustion ahead of the introduction of an aggressive environment indicates that time-dependent mechanically-driven local microstructural strain accommodation processes (resembling creep-like behavior) often play an important role for many metals, even for temperatures as low as 40 % of their melting points (0.4 T_m). EIC studies reveal initial surface conditions and their associated immediate sub-surface alloy microstructures generated during creation (i.e. disturbed layers) can dictate whether or not EIC initiation occurs under mechanical loading conditions otherwise sufficient to enable initiation and growth. The plethora of quantitative experimental techniques now available to researchers should enable significant advances towards understanding EIC initiation.

Over the last few decades, globalization has weakened the US manufacturing sector. The COVID-19 pandemic revealed import dependencies and supply chain shocks that have raised public and private awareness of the need to rebuild domestic production. A range of new technologies, collectively called Industry 4.0, create opportunities to revolutionize domestic and local manufacturing. Success depends on further refinement of those technologies, broad implementation throughout private companies, and concerted efforts to rebuild the industrial commons, the national ecosystem of producers, suppliers, service providers, educators, and workforce necessary to regain a competitive, innovative manufacturing sector. A recent workshop sponsored by the Engineering Research Visioning Alliance (ERVA) identified a range of challenges and opportunities to build a resilient, flexible, scalable, and high-quality manufacturing sector. This paper provides a strategic roadmap for regaining US manufacturing leadership by briefly summarizing discussions at the ERVA-sponsored workshop held in 2023 and providing additional analysis of key technical and economic issues that must be addressed to achieve dynamic, high-value manufacturing in the USA. The focus of this presentation is on discrete manufacturing of production of structural components, a large subset of total manufacturing that produces high-value inputs and finished products for domestic consumption and export.

As bioprinting advances into clinical relevance with patient-specific tissue and organ constructs, it must be capable of multi-material fabrication at high resolutions to accurately mimick the complex tissue structures found in the body. One of the most fundamental structures to regenerative medicine is microvasculature. Its continuous hierarchical branching vessel networks bridge surgically manipulatable arteries (∼1–6 mm) to capillary beds (∼10µm). Microvascular perfusion must be established quickly for autologous, allogeneic, or tissue engineered grafts to survive implantation and heal in place. However, traditional syringe-based bioprinting techniques have struggled to produce perfusable constructs with hierarchical branching at the resolution of the arterioles (∼100-10µm) found in microvascular tissues. This study introduces the novel CEVIC bioprinting device (i.e.ContinuouslyExtrudedVariableInternalChanneling), a multi-material technology that breaks the current extrusion-based bioprinting paradigm of pushing cell-laden hydrogels through a nozzle as filaments, instead, in the version explored here, extruding thin, wide cell-laden hydrogel sheets. The CEVIC device adapts the chaotic printing approach to control the width and number of microchannels within the construct as it is extruded (i.e. on-the-fly). Utilizing novel flow valve designs, this strategy can produce continuous gradients varying geometry and materials across the construct and hierarchical branching channels with average widths ranging from 621.5 ± 42.92%µm to 11.67 ± 14.99%µm, respectively, encompassing the resolution range of microvascular vessels. These constructs can also include fugitive/sacrificial ink that vacates to leave demonstrably perfusable channels. In a proof-of-concept experiment, a co-culture of two microvascular cell types, endothelial cells and pericytes, sustained over 90% viability throughout 1 week in microchannels within CEVIC-produced gelatin methacryloyl-sodium alginate hydrogel constructs. These results justify further exploration of generating CEVIC-bioprinted microvasculature, such as pre-culturing and implantation studies.

The mass reduction of passenger vehicles has been a great focus of academic research and federal policy initiatives of the United States with coordinated funding efforts and even a focus of a Manufacturing USA Institute. The potential benefit of these programs can be described as modest from a societal point of view, for example reducing vehicle mass by up to 25% with modest cost implications (under $5 per pound saved) and the ability to implement with existing manufacturing methods. Much more aggressive reductions in greenhouse gas production are necessary and possible, while delivering the same service. This is demonstrated with a higher-level design thinking exercise on an environmentally responsible lightweight vehicle, leading to the following criteria: lightweight, low aerodynamic drag, long-lived (over 30 years and 2 million miles), adaptable, electric, and used in a shared manner on average over 8 h per day. With these specifications, passenger-mile demand may be met with around 1/10 of the current fleet. Such vehicles would likely have significantly different designs and construction than incumbent automobiles. It is likely future automotive production will be more analogous to current aircraft production with higher costs per pound and lower volumes, but with dramatically reduced financial and environmental cost per passenger mile, with less material per vehicle, and far less material required in the national or worldwide fleets. Subsidiary benefits of this vision include far fewer parking lots, greater accessibility to personal transportation, and improved pedestrian safety, while maintaining a vibrant and engaging economy. The systemic changes to the business models and research and development directions (including lightweight design and manufacturing) are discussed, which could bring forth far more sustainable personal transportation.

Sharing genomic databases is critical to the collaborative research in computational biology. A shared database is more informative than specific genome-wide association studies (GWAS) statistics as it enables do-it-yourself calculations. Genomic databases involve intellectual efforts from the curator and sensitive information of participants, thus in the course of data sharing, the curator (database owner) should be able to prevent unauthorized redistributions and protect genomic data privacy. As it becomes increasingly common for a single database be shared with multiple recipients, the shared genomic database should also be robust against collusion attack, where multiple malicious recipients combine their individual copies to forge a pirated one with the hope that none of them can be traced back. The strong correlation among genomic entries also make the shared database vulnerable to attacks that leverage the public correlation models. In this paper, we assess the robustness of shared genomic database under both collusion and correlation threats. To this end, we first develop a novel genomic database fingerprinting scheme, called Gen-Scope. It achieves both copyright protection (by enabling traceability) and privacy preservation (via local differential privacy) for the shared genomic databases. To defend against collusion attacks, we augment Gen-Scope with a powerful traitor tracing technique, i.e., the Tardos codes. Via experiments using a real-world genomic database, we show that Gen-Scope achieves strong fingerprint robustness, e.g., the fingerprint cannot be compromised even if the attacker changes 45% of the entries in its received fingerprinted copy and colluders will be detected with high probability. Additionally, Gen-Scope outperforms the considered baseline methods. Under the same privacy and copyright guarantees, the accuracy of the fingerprinted genomic database obtained by Gen-Scope is around 10% higher than that achieved by the baseline, and in terms of preservations of GWAS statistics, the consistency of variant-phenotype associations can be about 20% higher. Notably, we also empirically show that Gen-Scope can identify at least one of the colluders even if malicious receipts collude after independent correlation attacks.

Conventional rolling is a plastic deformation process that uses compression between two rolls to reduce material thickness and produce sheet/plane geometries. This deformation process modifies the material structure by generating texture, reducing the grain size, and strengthening the material. The rolling process can enhance the strength and hardness of lightweight materials while still preserving their inherent lightness. Lightweight metals like magnesium alloys tend to lack mechanical strength and hardness in load-bearing applications. The general rolling process is controlled by the thickness reduction, velocity of the rolls, and temperature. When held at a constant thickness reduction, each pass through the rolls introduces an increase in strain hardening, which could ultimately result in cracking, spallation, and other defects. This study is designed to optimize the rolling process by evaluating the effects of the strain rate, rather than the thickness reduction, as a process control parameter.