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Abstract 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. 

Abstract Skeletal fixation plates are essential components in craniomaxillofacial (CMF) reconstructive surgery to connect skeletal disunions. To ensure that these plates achieve geometric conformity to the CMF skeleton of individual patients, a pre-operative procedure involving manual plate bending is traditionally required. However, manual adjustment of the fixation plate can be time-consuming and is prone to geometric error due to the springback effect and human inspection limitations. This work represents a first step towards autonomous incremental plate bending for CMF reconstructive surgery through machine learning-enabled springback prediction and feedback bending control. Specifically, a Gaussian process is first investigated to complement the physics-based Gardiner equation to improve the accuracy of springback effect estimation, which is then incorporated into nonlinear model predictive controller to determine the optimal sequence of bending inputs to achieve geometric conformity. Evaluation using a simulated environment for bending confirms the effectiveness of the developed approach. 

Abstract We introduce a novel digital twin framework for predictive maintenance of physical systems with long term operations. Using monitoring tire health as an application, we show how the digital twin framework is used to enhance automotive safety and efficiency, while overcoming technical challenges using a three-step approach. Firstly, for managing the data complexity over a long operation span, we employ data reduction techniques to concisely represent physical tires using historical performance and usage data. Relying on this data, for fast real-time prediction, we train a transformer-based model offline on our concise dataset to predict future tire health over time, represented as Remaining Casing Potential (RCP). Based on our architecture, our model quantifies both epistemic and aleatoric uncertainty, providing reliable confidence intervals around predicted RCP. Secondly, we incorporate real-time data by updating the predictive model in the digital twin framework, ensuring its accuracy throughout its life span with the aid of hybrid modeling and the use of a discrepancy function. Thirdly, to assist decision making in predictive maintenance, we implement a Tire State Decision Algorithm, which strategically determines the optimal timing for tire replacement based on RCP forecasted by our transformer model. This three-step approach ensures that our digital twin not only accurately predicts the health of a system, but also continually refines its digital representation and makes predictive maintenance decisions for removal from service. Our proposed digital twin framework embodies a physical system accurately and leverages big data and machine learning for predictive maintenance, model update and decision making. 

Abstract 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. 

Abstract 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. 

Abstract This paper details the design and operation of a testbed to evaluate the concept of autonomous manufacturing to achieve a desired manufactured part performance specification. This testbed, the autonomous manufacturing system for phononic crystals (AMSPnC), is composed of additive manufacturing, material transport, ultrasonic testing, and cognition subsystems. Critically, the AMSPnC exhibits common manufacturing deficiencies such as process operating window limits, process uncertainty, and probabilistic failure. A case study illustrates the AMSPnC function using a standard supervised learning model trained by printing and testing an array of 48 unique designs that span the allowable design space. Using this model, three separate performance specifications are defined and an optimization algorithm is applied to autonomously select three corresponding design sets to achieve the specified performance. Validation manufacturing and testing confirms that two of the three optimal designs, as defined by an objective function, achieve the desired performance, with the third being outside the design window in which a distinct bandpass is achieved in phononic crystals (PnCs). Furthermore, across all samples, there is a marked difference between the observed bandpass characteristics and predictions from finite elements method computation, highlighting the importance of autonomous manufacturing for complex manufacturing objectives. 

Abstract In contemporary manufacturing processes, reliable but efficient pick-and-place robots are frequently used. The automation and optimization of the pick and place procedures utilizing various path-planning approaches thereby support the expansion of application areas. Yet, the design of a controller faces significant difficulties due to the nonlinearities inherent in robotic manipulators and the unpredictable nature of the ambient factors. In place of the classic model predictive control (MPC), this paper presents the application of the Nonlinear Model Predictive Controller (NLMPC) as an acceptable control mechanism for real-time optimization and robust stability of the KINOVA Gen3 robotic arm. The developed NLMPC-based method ensures that the robotic arm does not run into obstacles in the workplace or with itself while reaching, gripping, selecting, and placing the necessary items. To acquire the control input trajectory, the optimization in NLMPC is solved repeatedly. When input constraints are available, the modeled system tracks reference trajectories to achieve the aim of recognizing and organizing distinct objects. After the NLMPC is successfully developed, a simulation environment is built and finally brought to life by combining all the processes into one using a MATLAB Stateflow chart. 

Abstract Highlights from a member survey and panel discussion outlining the challenges and benefits of advanced manufacturing for the materials community. The article covers research being conducted in academia and industry to develop and integrate advanced manufacturing techniques; methods for in-process monitoring and machine learning; and challenges in adopting new digital manufacturing technologies. 

Abstract 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. 

Abstract 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.