A. John Hart

Abstract Planar lithium-ion cells with thick electrodes are often subject to an inherent tradeoff between energy and power density due to tortuous lithium-ion diffusion distances through the porous electrodes. Interdigitated 3D cell architectures circumvent this problem by incorporating thick electrode designs while also keeping lithium-ion diffusion distances short, thereby enabling cells with high energy and power densities. However, attempts to fabricate interdigitated cells have been hindered by difficulty in producing conformally coated electrolytes and incorporating the second electrode. Previously, we demonstrated that patterned vertically aligned carbon nanotubes (VA-CNTs) grown directly on Cu foil and coated with Si thin films could serve as a versatile template for high energy density 3D electrodes. Here, we investigate these Si-CNT composites as a template for 3D full cell design. Initiated chemical vapor deposition (iCVD) is used to deposit conformal poly(hydroxyethyl methacrylate-co-ethylene glycol diacrylate) thin film electrolytes onto patterned Si-CNT composites structures. To complete the full cell, a slurry-based cathode is infiltrated into the patterned iCVD coated Si-CNT composite. This cell stack is then soaked in a liquid electrolyte and cycled, establishing the viability of using VA-CNTs to serve as a scaffold for 3D battery cell fabrication and iCVD films as electrolytes in 3D full cells.

This work examines how AI, especially agentic systems, is being adopted in engineering and manufacturing workflows, what value it provides today, and what is needed for broader deployment. This is an exploratory and qualitative state-of-practice study grounded in over 30 interviews across four stakeholder groups (large enterprises, small/medium firms, AI developers, and CAD/CAM/CAE vendors). We find that near-term AI gains cluster around structured, repetitive work and data-intensive synthesis, while higher-value agentic gains come from orchestrating multi-step workflows across tools. Adoption is constrained less by model capability than by fragmented and machine-unfriendly data, stringent security and regulatory requirements, and limited API-accessible legacy toolchains. Reliability, verification, and auditability are central requirements for adoption, driving human-in-the-loop frameworks and governance aligned with existing engineering reviews. Beyond technical barriers there are also organizational ones: a persistent AI literacy gap, cultural heterogeneity, and governance structures that have not yet caught up with agentic capabilities. Together, the findings point to a staged progression of AI utility from low-consequence assistance toward higher-order automation, as trust, infrastructure, and verification mature. This highlights key breakthroughs needed, including integration with traditional engineering tools and data types, robust verification frameworks, and improved spatial and physical reasoning.

This work examines how AI, especially agentic systems, is being adopted in engineering and manufacturing workflows, what value it provides today, and what is needed for broader deployment. This is an exploratory and qualitative state-of-practice study grounded in over 30 interviews across four stakeholder groups (large enterprises, small/medium firms, AI developers, and CAD/CAM/CAE vendors). We find that near-term AI gains cluster around structured, repetitive work and data-intensive synthesis, while higher-value agentic gains come from orchestrating multi-step workflows across tools. Adoption is constrained less by model capability than by fragmented and machine-unfriendly data, stringent security and regulatory requirements, and limited API-accessible legacy toolchains. Reliability, verification, and auditability are central requirements for adoption, driving human-in-the-loop frameworks and governance aligned with existing engineering reviews. Beyond technical barriers there are also organizational ones: a persistent AI literacy gap, cultural heterogeneity, and governance structures that have not yet caught up with agentic capabilities. Together, the findings point to a staged progression of AI utility from low-consequence assistance toward higher-order automation, as trust, infrastructure, and verification mature. This highlights key breakthroughs needed, including integration with traditional engineering tools and data types, robust verification frameworks, and improved spatial and physical reasoning.

High Resolution Image Download MS PowerPoint Slide Thermal catalytic oxidation has emerged as a promising technique to destroy methane from dilute sources, such as agricultural emissions in engineered indoor environments. The concentration of methane and total volumetric treatment requirements must be understood to evaluate the feasibility of this approach, particularly with respect to economic and environmental viability. Here, dairy farm field sampling campaigns were conducted in New England dairies (free stalls with 600–1,200 head of cattle in both convection and tunnel ventilation) to establish a robust framework to evaluate the climate-benefits and cost-effectiveness associated with deployment of thermal catalytic methane oxidizers in these housing styles. Methane (CH 4 ) levels ranged from 2–70 ppm in summer and 2–102 ppm in winter due to reduced ventilation during cold weather. Temperatures ranged from 24.2 to 33.3 and 3.0–15.5 °C, relative humidity (RH) ranged from 28.3 to 56.5 and 26.5–85.4% RH, and ammonia ranged from 0.0 to 6.5 and 0.0–8.0 ppm in summer and winter, respectively. Presuming a thermal catalytic reactor temperature of 400 °C and recovery of 90% of the heat generated from the methane destruction, climate beneficial operation requires a CH 4 concentration of at least 10 ppm (wind), 33 ppm (solar), 245 ppm (grid), and 140 ppm (natural gas) for a 100-year GWP. At a reactor temperature of 400 °C and 100 ppm of CH 4, the energy cost alone per tonne of CO 2 e removal would be $591, $425, and $173 for solar, wind, and biogas energy, respectively, and over $10,000 for both grid and natural gas. The use of onsite biogas provided a net climate benefit at all methane concentrations and operating temperatures due to the destruction credit from manure-derived fuel. However, the large dairy barn air flow rates (250 cfm cow –1 in winter and 2,000 cfm cow –1 in summer) translate to high power requirements (9–74 MW), challenging the practical implementation of renewable energy sources. This highlights the steep challenge associated with postrelease enteric methane destruction.

Established light-based additive manufacturing (AM) processes, such as vat polymerization, utilize nonrecyclable thermoset polymers, posing sustainability concerns. This work presents a method for circular photopolymerization three-dimensional (3D) printing of thermoplastic parts, addressing the demand for low-waste production of complex, high-resolution polymer parts. This is achieved through interfacial photopolymerization (IPP), where linear polymer chains form layerwise into entangled networks at the interface between the immiscible organic and aqueous phases. IPP has previously been demonstrated, but with limited chemistries and without 3D structural control. We demonstrate herein a chemistry to form poly(methyl methacrylate) (PMMA) by IPP and a process for multilayer fabrication in a modified commercial projector-based 3D printer. Layer resolution and stability are enhanced using light-absorbing dye and a water-soluble polyethylene glycol (PEG) binder. Postprocessing with controlled air drying and thermal treatment with PEG infiltration preserves geometry and reduces cracking. The resulting composite comprises 75% PEG and 25% PMMA with mechanical properties akin to those of polymer foams. Circularity of the IPP-PMMA process is demonstrated by recycling and reincorporating printed objects across several cycles without significant degradation of properties. Although enhancements in geometric fidelity and mechanical properties are necessary, IPP 3D printing enables, for the first time, digital light processing of recyclable thermoplastic PMMA and PEG-based parts.

Abstract CAD programs—parametric sequences of commands that compile into 3D geometries—uniquely enable precise and parametric editing of engineering designs. Generating CAD programs from nonparametric 3D data, like point clouds and meshes, is essential to engineering design, but existing “reverse engineering” methods typically require substantial expert intervention, limiting productivity and accessibility. Deep generative models have emerged as a potential solution to automatic and generalizable CAD generation, but they are significantly hindered by the lack of large-scale and balanced CAD datasets. This paper introduces GenCAD-3D, a novel multimodal generative framework designed to produce accurate CAD programs from point clouds and meshes. GenCAD-3D employs contrastive learning to align latent embeddings between CAD and geometric encoders, and uses latent diffusion models for generation, enabling multimodal retrieval and generation of CAD programs. We also propose SynthBal, a synthetic data augmentation strategy specifically crafted to balance datasets that under-represent important classes of CAD programs—particularly those of high complexity—and to expand small-scale datasets. Experiments demonstrate that SynthBal enhances reconstruction accuracy, reduces invalid CAD generation, and substantially improves performance for high-complexity geometries compared to existing methods. Furthermore, we propose a new sequence-length normalization metric to more accurately evaluate model performance on complex geometries. These contributions advance our ability to automatically generate complex and editable CAD models, promising significant progress in reverse engineering and automation in engineering design processes. We will publicly release our datasets and code, including a set of 51 3D-printed and laser-scanned parts, here: github.com/yunomi-git/GenCAD-3D.

Undergraduate engineering curriculum has commonly struggled to capture students’ imagination and in®terest for manufacturing. Curriculum in most undergraduate engineering programs give students limited opportunities to learn manufacturing. And if provided, manufacturing knowledge is often offered in one course near the end of the four-year degree – after significant career exploration has already passed. To stimulate students’ interest in manufacturing earlier in their undergraduate programs, we present the development and implementation of a LEGO®-themed freshman manufacturing course. The course is composed of interactive lectures, hands-on labs, factory visits, and team project-based learning. We integrated LEGOs throughout the curriculum as a medium to explore topics from prototyping to large-scale pr® for freshmen students in the spring of 2024 in a mechanical engineering program at MIT. Findings from implementing the Student Assessment of Learning Gains survey yield gains in attitudes, understanding, and skills in manufacturing. Finally, while manufacturing programs are traditionally predominantly male, the class’s enrollment was 75 % women, demonstrating the course’s promise to facilitate interest in manufacturing with a diverse audience.

Abstract Computer-aided design (CAD) programs, structured as parametric sequences of commands that compile into precise 3D geometries, are fundamental to accurate and efficient engineering design processes. Generating these programs from nonparametric data such as point clouds and meshes remains a crucial yet challenging task, typically requiring extensive manual intervention. Current deep generative models aimed at automating CAD generation are significantly limited by imbalanced and insufficiently large datasets, particularly those lacking representation for complex CAD programs. To address this, we introduce GenCAD-3D, a multimodal generative framework utilizing contrastive learning for aligning latent embeddings between CAD and geometric encoders, combined with latent diffusion models for CAD sequence generation and retrieval. In addition, we present SynthBal, a synthetic data augmentation strategy specifically designed to balance and expand datasets, notably enhancing representation of complex CAD geometries. Our experiments show that SynthBal significantly boosts reconstruction accuracy, reduces the generation of invalid CAD models, and markedly improves performance on high-complexity geometries, surpassing existing benchmarks. These advancements hold substantial implications for streamlining reverse engineering and enhancing automation in engineering design. We will publicly release our datasets and code, including a set of 51 3D-printed and laser-scanned parts on our project site.

Qualification of high-performance metal components produced by laser powder bed fusion (LPBF) must identify porous defects that nucleate fatigue cracking. Detecting such defects via optical monitoring of LPBF can enable in-process quality control without downstream testing. However, integration of in-process sensing with LPBF is hampered by optical complications, and therefore, it has yet to be proven that the finest pores that limit component fatigue life can be resolved. We present aperture division multiplexing (ADM) as a method for simultaneously focusing the process laser and providing unobstructed optical access for high-fidelity process monitoring using a common optic. Construction of an ADM optic with 50 μm spatial resolution in the mid-wave infrared is described, and it is demonstrated on a production-representative LPBF testbed. High-speed video data are correlated to micro-CT measurement of pores as fine as 4.3 μm, establishing the promise of ADM for the qualification of LPBF component fatigue performance.

The development of new metal alloys is key to the continued advances in critical technologies such as jet engines operating at higher temperatures, rocket engines with longer lifetime and reusability, and reactors for fusion and fission energy generation. While additive manufacturing (AM) is attractive for both prototyping and production of advanced alloys and components, the experimental screening and validation of new alloys typically requires costly synthesis of custom powder feedstocks. We present a technique for high-throughput screening of nanoparticle-enhanced alloys for AM, combining inkjet printing and laser powder bed fusion (LPBF). Alloyed specimens are prepared on metal substrates with shallow machined cavities; a nanoparticle-containing ink is printed into the cavities via inkjet deposition; powder is manually spread into the wells; and then the material is melted by scanning of a laser as in traditional LPBF. We exercise this workflow using Niobium as the base metal and with custom-formulated inks containing Si and/or Ti nanoparticles. The alloyed specimens exhibit locally defined composition, microstructure, and hardness. We demonstrate control of minority element composition of <1 % to >10 % over <1 mm distances, and along with the capability to create multi-material gradients exhibiting complex microstructural effects.

Abstract Development and qualification of process parameters in laser powder bed fusion (LPBF) involves many variables. At the outset of development, whether transferring known parameters to a new machine, or exploring a new material, single-track and single-layer experiments are a convenient means of down-selecting key variables and exploring parameter scaling behavior. We present an experimental workflow for single-layer LPBF experiments using high-precision metal template substrates, overcoming challenges with precision single-layer alignment in LPBF systems and enabling efficient processing and cross-sectional analysis. Templates are fabricated using chemical etching and machining, and are characterized using optical profilometry and X-ray transmission imaging of powder layers. Using the etched templates, a single-track parameter study is performed in SS316 including three powder layer thicknesses, and spanning common laser melting modes (lack-of-fusion, conduction, and keyhole mode). Analysis of melt track geometries using automated image processing allows a scaling law to be applied to define the process window, quantifying the amount of material added with increasing powder layer thickness. Single-track results are verified with raster scanning experiments, showing the potential to transfer single-track results to full LPBF builds. Graphical abstract

Abstract Surfaces become “sticky” at the micro/nano length scale as the gravitational force is no longer effective. Ultragentle, high‐contrast switching of interfacial adhesion is the key to reliable small‐scale object manipulation. Here, a novel approach is presented for surface adhesion control utilizing a liquid‐permeable nanoporous surface, which can switch from off‐state adhesion (&lt; 0.002 kPa) to on‐state attraction (0.8 kPa) without preload. The surface of the gripper is composed of vertically aligned composite nanowires with an average diameter of 79 nm. When a liquid is injected into the nanoporous membrane, capillary adhesion occurs, allowing the object to be picked up. As the liquid evaporates, the object can be released by extremely sparse contact. The off‐state adhesion of a millimeter‐scale gripper is even lower than the gravitational force of thin polymer films (0.18 mN cm −2 ), enabling the solid‐contactless release of lightweight materials. We characterize and model the mechanism across length scales and provide pick‐and‐place demonstrations including LED chips, micro‐architected materials, and thin‐film electronics.

Abstract Vat photopolymerization (VP) additive manufacturing (AM) is valued for its speed, precision, and material versatility. However, its requirement for support structures limits printable geometries, complicates post‐processing, and generates non‐recyclable waste when typical thermoset resins are used. Here, a wavelength‐selective resin system for VP that enables single‐vat, multi‐material printing with dissolvable supports is introduced. Exposure to visible light produces a rigid, dissolvable thermoplastic, while UV light forms a crosslinked thermoset resistant to dissolution. This process, termed selective solubility vat photopolymerization (SSVP), eliminates the geometric constraints imposed by conventional VP methods, facilitating the creation of complex objects with supports that are removable using green and food‐safe solvents such as D‐limonene and ethyl acetate, as well as mineral oil. Post‐print heat treatment tunes crosslink density and solubility. Dissolved supports can be recycled into fresh resin and reprinted without mechanical property loss, offering a practical, scalable route to reducing waste. Additionally, SSVP provides spatial control of dissolution kinetics, enabling programmable 3D dissolution profiles. By enabling the integration of dissolvable and insoluble regions in a single print, SSVP sets the stage for fully automated and more sustainable AM workflows.

Nanoparticle superlattices present transformative opportunities for material design by enabling precise control over both nanoscale organization and composition; however, translating these assemblies into macroscopic constructs while preserving nanoscale order remains a critical challenge due to the incompatibility of traditional processing techniques with colloidal systems. This study introduces "colloidal metallurgy," a framework for understanding and controlling defect evolution and densification in nanoparticle superlattices during colloidal sintering. We investigate the effects of pressure and temperature to elucidate mechanisms of particle transport, defect annealing, and densification as single-crystal colloidal assemblies coalesce into polycrystalline superlattices. Pressure-driven crystallite fracture is identified as the primary mode of densification, while temperature enhances particle mobility, enabling defect reduction and grain growth. A multistage sintering strategy employing high temperature annealing to grow grains and restore fracture-based capacity for densification was developed to produce dense (∼1% porosity) polycrystals with low defect counts, demonstrating a pathway for processing nanoparticle superlattices. By exploring the parallels and distinctions between atomic and colloidal sintering, this work establishes critical insights into the mechanisms governing colloidal material processing. These findings lay the groundwork for defect engineering in colloidal systems, offering a scalable approach to design macroscopic materials with tailored properties.

Rapid heating of carbon nanotube (CNT) materials via microwave-induced heating is known to enable efficient chemical functionalization and accelerated curing of composites. Both liquid-phase and solvent-free techniques rely on the large dielectric loss of CNT materials, which causes them to absorb incident microwave energy. However, despite the prevalence of liquid-phase techniques in the processing of continuous CNT networks (e.g., CNT yarns and tapes), to our knowledge a detailed exploration of microwave heating of continuous CNT networks imbibed with liquid is not present in literatured. Here, we study liquid-phase microwave heating of CNT networks using commercially produced nanoporous CNT yarns as a model system. We observe that rapid heating of immersed CNT networks causes macroscopic swelling of CNT yarns and even exfoliation of CNT bundles, increasing their porosity and surface area while preserving their continuity and crystallinity. Through selection of appropriate solvents and heating rates, this swelling is found to be controllable. Swelling isminimized via low heating rates (i.e., 1–3 °C min –1 ), which allow for thermal equilibration, and maximized using microwave-transparent, low-loss solvents with low boiling points. Accordingly, we believe that the microwave-superheated liquid environment is particularly promising for manufacturing advanced CNT materials, which can benefit from this physical modification. By exploiting this intrinsic behavior in the presence of various reactants, liquid-phase microwave processing may enable faster and more effective functionalization or decoration of CNT yarns or in situ synthesis of polymer nanocomposites.

Aluminum (Al) alloys are widely used in aerospace and automotive industries as a result of their high strength-to-density ratio and cost-effectiveness, with their use at room temperature in housings and brackets. Although additive manufacturing (AM) facilitates the manufacturing of high-temperature aluminum alloys (200-400°C) to enable their potential use in intake fans and engine pistons, few alloying systems can sufficiently inhibit dislocation motions to achieve high strength, and their dislocation blockage features can hardly be retained at elevated temperatures. The high-demand service also requires reducing the material cost and CO 2 emissions (net cost) without sacrificing mechanical performance. The two main blockage features for Al alloys are: the introduction of pinning sites that disrupt dislocation motions, generating tortuous paths; and interfaces that cause dislocation pileups and prevent plastic deformation. The mechanical design of the microstructure promotes an increase in the percentage of volume and a reduction in the length scale of these features to achieve higher strength. Here, we show that we can exploit rapid solidification in laser-based AM to introduce new pathways to achieve the mechanical design via precipitation of metastable phases that form at high fractions and with sub-micron length scale. Furthermore, with thermal aging, these phases transform into exceptional volumes of nanometer-scale pinning sites that are stable at high temperatures. We performed high-throughput calculated phase diagram (CALPHAD)-based integrated computational materials engineering (ICME) simulations along with inverse design using Bayesian optimization. We propose Al-Ni-Er-Zr-Y as a class of Al alloy that the cost/strength trade-off can be tailored by Er/Y ratio. Our high-temperature design has 95% strength of a benchmark printable Al alloy with 15% anticipated net cost savings. For room temperature use, by substituting Er with Y, in the first design, metastable phases can be exploited to achieve 3 × room-temperature strengthening of the benchmark design with a 60% net cost reduction. The second design matches the strength of the benchmark alloy with 80% net cost savings.

Nanoparticle superlattices present transformative opportunities for material design by enabling precise control over both nanoscale organization and composition, however, translating these assemblies into macroscopic constructs while preserving nanoscale order remains a critical challenge due to the incompatibility of traditional processing techniques with colloidal systems. This study introduces "colloidal metallurgy," a framework for understanding and controlling defect evolution and densification in nanoparticle superlattices during colloidal sintering. We investigate the effects of pressure and temperature to elucidate mechanisms of particle transport, defect annealing, and densification as single-crystal colloidal assemblies coalesce into polycrystalline superlattices. Pressure-driven crystallite fracture is identified as the primary mode of densification, while temperature enhances particle mobility, enabling defect reduction and grain growth. A multi-stage sintering strategy employing high temperature annealing to grow grains and restore fracture-based capacity for densification was developed to produce dense (~1% porosity) polycrystals with low defect counts, demonstrating a novel pathway for processing nanoparticle superlattices. By exploring the parallels and distinctions between atomic and colloidal sintering, this work establishes critical insights into the mechanisms governing colloidal material processing. These findings lay the groundwork for defect engineering in colloidal systems, offering a scalable approach to design macroscopic materials with tailored properties.

Nanoparticle superlattices consisting of ordered arrangements of nanoparticles exhibit unique optical, magnetic, and electronic properties arising from nanoparticle characteristics as well as their collective behaviors. Understanding how processing conditions influence the nanoscale arrangement and microstructure is critical for engineering materials with desired macroscopic properties. Microstructural features such as grain boundaries, lattice defects, and pores significantly affect these properties but are challenging to quantify using traditional manual analyses as they are labor-intensive and prone to errors. In this work, we present a machine learning workflow for automating grain segmentation in scanning electron microscopy (SEM) images of nanoparticle superlattices. This workflow integrates signal processing techniques, such as Radon transforms, with unsupervised learning methods like agglomerative hierarchical clustering to identify and segment grains without requiring manually annotated data. In the workflow we transform the raw pixel data into explainable numerical representation of superlattice orientations for clustering. Benchmarking results demonstrate the workflow's robustness against noisy images and edge cases, with a processing speed of four images per minute on standard computational hardware. This efficiency makes the workflow scalable to large datasets and makes it a valuable tool for integrating data-driven models into decision-making processes for material design and analysis. For example, one can use this workflow to quantify grain size distributions at varying processing conditions like temperature and pressure and using that knowledge adjust processing conditions to achieve desired superlattice orientations and grain sizes.

Vat photopolymerization (VP) is widely used for additive manufacturing due to its speed, precision, and material versatility. However, traditional support structures limit printable geometries, require manual post-processing, and produce non-recyclable waste. We introduce a wavelength-selective resin for VP that enables single-vat, multi-material printing with dissolvable supports. Exposure to one wavelength produces a rigid, dissolvable thermoplastic, while a second wavelength forms a crosslinked thermoset resistant to dissolution. This selective solubility vat photopolymerization (SSVP) process allows for the fabrication of complex objects with support structures removable using non-toxic solvents like mineral oil. Heat treatment further tailors crosslink density and solubility. Dissolved supports can be recycled into fresh resin and reprinted without mechanical property loss, eliminating waste and paving the way for fully automated, sustainable manufacturing workflows.

Rheology describes the flow of fluids from food and plastics, to coatings, adhesives, and 3D printing inks, and is commonly denoted by viscosity alone as a simplification. While viscometers adequately probe Newtonian (constant) viscosity, most fluids have complex viscosity, requiring tests over multiple shear rates, and transient measurements. As a result, rheometers are typically large, expensive, and require additional infrastructure (e.g., gas lines), rendering them inaccessible for regular use by many individuals, small organizations, and educators. Here, we introduce a low-cost (under USD$200 bill of materials) Open Source Rheometer (OSR), constructed entirely from thermoplastic 3D printed components and off-the-shelf electromechanical components. A sample fluid rests in a cup while a micro stepping motor rotates a tool inside the cup, applying strain-controlled shear flow. A loadcell measures reaction torque exerted on the cup, and viscosity is calculated. To establish the measurement range, the viscosity of four Newtonian samples of 0.1-10 Pa.s were measured with the OSR and compared to benchmark values from a laboratory rheometer, showing under 23% error. Building on this, flow curves of three complex fluids - a microgel (hand sanitizer), foam (Gillette), and biopolymer solution (1% Xanthan Gum) - were measured with a similar error range. Stress relaxation, a transient test, was demonstrated on the biopolymer solution to extract the nonlinear damping function. We finally include detailed exposition of measurement windows, sources of error, and future design suggestions. The OSR cost is ∼1/25th that of commercially available devices with comparable minimum torque (200 µN.m), and provides a fully open-source platform for further innovation in customized rheometry.

Qualification of high-performance metal components produced by laser powder bed fusion (LPBF) must identify process-induced porous defects that reduce ductility and nucleate fatigue cracking. Detecting such defects via optical monitoring of LPBF provides a path towards in-process quality control without downstream testing such as by computed tomography. However, integration of in-process sensing with LPBF is hampered by geometric and optical complications and, as a result, it has yet to be proven that the finest pores that limit component fatigue life can be resolved via in situ data. We present aperture division multiplexing (ADM) as a method for simultaneously focusing the process laser and providing unobstructed optical access for high-fidelity process monitoring using a common optic. Construction of an ADM optic of achieving imaging at 50 micron spatial resolution in the mid-wave infrared is described, and this optic is demonstrated on a production-representative LPBF testbed. High-speed infrared video data are correlated to micro-CT measurement of pores as fine as 4.3 microns, through multiple process signatures, establishing the promise of ADM for qualification of LPBF component fatigue performance.

The growing demands for high-temperature materials, especially in aerospace and energy production, compel thorough explorations of innovative materials. Here, we demonstrate signi fi cantly enhanced high-temperature mechanical properties of Inconel 625 (In625) based metal matrix composites (MMCs) fabricated by laser powder bed fusion (LPBF) additive manufacturing. The MMC feedstocks for LPBF were fabricated with fi ne ceramic particles (i.e., titanium diboride (TiB 2 ), titanium carbide (TiC), zirconium diboride (ZrB 2 ) and zirconium carbide (ZrC)) separately mixed with In625 powders. Among the printed specimens, the In625 + TiB 2 showed an exceptional strength-ductility combination at 800 (cid:1) C as well as an outstanding creep resistance at 800 (cid:1) C under 150 MPa tensile stress. The detailed microstructural characterization, along with thermodynamic calculation and atomic simulations, reveal that the addition of TiB 2 results in the formation of serrated grain boundaries, (Cr, Mo)-boride phases near the grain boundaries, and nano-dispersed (Ti, Al, Nb)-oxide phases within the matrix. These features effectively suppress the formation of detrimental high-temperature phases and enhance the material ’ s high-temperature properties. Beyond amplifying the inherent thermal attributes of

With the increasing adoption of metal additive manufacturing (AM), researchers and practitioners are turning to data-driven approaches to optimise printing conditions. Cross-sectional images of melt tracks provide valuable information for tuning process parameters, developing parameter scaling data, and identifying defects. Here we present an image segmentation neural network that automatically identifies and measures melt track dimensions from a cross-section image. We use a U-Net architecture to train on a data set of 62 pre-labelled images obtained from different labs, machines, and materials coupled with image augmentation. When neural network hyperparameters such as batch size and learning rate are properly tuned, the learned model shows an accuracy for classification of over 99% and an F1 score over 90%. The neural network exhibits robustness when tested on images captured by various users, printed on different machines, and acquired using different microscopes. A post-processing module extracts the height and width of the melt pool, and the wetting angles. We discuss opportunities to improve model performance and avenues for transfer learning, such as extension to other AM processes such as directed energy deposition.

Development and qualification of process parameters in laser powder bed fusion (LPBF) commonly involves many variables. At the outset of development, whether transferring known parameters to a new machine, or exploring a new material, single-track and single-layer experiments are a convenient means of down-selecting key variables and exploring parameter scaling behavior. We present an experimental workflow for single-layer LPBF experiments using etched high-precision metal template substrates, overcoming challenges with precision single-layer alignment in LPBF systems and enabling efficient processing and cross-sectional analysis. Templates are fabricated using chemical etching and machining, and are characterized using optical profilometry and X-ray transmission imaging of powder layers. Using the templates a single-track parameter study is performed in SS316 including three powder layer thicknesses, and spanning common laser melting modes (lack-of-fusion, conduction, and keyhole mode). Analysis of melt track geometries using automated image processing allows a scaling law to be applied to define the process window, quantifying the amount of material added with increasing powder layer thickness. Single-track results are verified with raster scanning experiments, showing the potential to transfer single-track results to full LPBF builds.

3D printable elastomers capable of self-healing are attractive for fabricating complex biomimetic and soft-robotic devices. While polymer network reorganization can be enabled with dynamic bond exchange, this strategy typically faces intrinsic trade-offs between healability, processability, and mechanical performance. Thus, new material design strategies that can overcome these trade-offs are needed. Here, we report the use of multivalent polymer-grafted nanoparticles (PGNPs) as reinforcing fillers for self-healing photoresins. As each nanoparticle is functionalized with thousands of polymer chains engaging in multivalent interactions with the surrounding elastomeric matrix, the bulk modulus of the composite can be increased without impairing the local segmental motion of polymer chains necessary for self-healing. We also examine PGNP structural parameters to establish structure–property relationships that permit fine-tuning of composite mechanical performance. Finally, these enhancements do not impair the materials’ manufacturability, as they can be used as feedstocks for digital light printing to produce complex and high-resolution 3D objects.

Additively manufactured (AM) aluminum alloys with high strength and thermal stability have broad applications in turbine engines, vacuum pumps, heat exchangers, and many other industrial systems. Employing precipitates with an L1$_2$ structure to block dislocation motions is a widespread strategy to strengthen aluminum. However, to achieve high strength, a high volume fraction of small precipitates is required, and these characteristics are generally mutually exclusive. Here, we show that for certain compositions of Al alloys, L1$_2$ phases initially precipitate as sub-micron metastable ternary phases under the rapid solidification conditions of powder bed AM, yet the subsequent L1$_2$ phases that precipitate during heat treatment of the sample remain at the nanoscale, imparting high strength. For strength to be retained at elevated temperature, these nanoprecipitates must have low coarsening rates. To inversely design the composition of an alloy to have these target microstructural features, we used hybrid calculation of phase diagram (CALPHAD)-based integrated computational materials engineering (ICME) and Bayesian optimization techniques. We tested our approach by designing an Al-Er-Zr-Y-Yb-Ni model alloy, and the selected composition was manufactured in powder form as AM feedstock. The strength of specimens manufactured via laser powder bed fusion (LPBF) from the designed composition is comparable to that of wrought Al 7075, yet without cracking that occurs upon LPBF of Al 7075. After high-temperature (400$^\circ$C) aging the designed alloy is 50% stronger than the strongest known benchmark printable Al alloy.

Spreading of fine (D50<=20um) powders into thin layers typically requires a mechanism such as a roller to overcome the cohesive forces between particles. Roller-based spreading requires careful optimization and can result in low density and/or inconsistent layers depending on the characteristics of the powder feedstock. Here, we explore improved, roller-based spreading strategies for highly cohesive powders using an integrated discrete element-finite element (DEM-FEM) framework. Powder characteristics are emulated using a self-similarity approach based on experimental calibration for a Ti-6Al-4V 0-20um powder. We find that optimal roller-based spreading relies on a combination of surface friction of the roller and roller kinematics that impart sufficient kinetic energy to break cohesive bonds between powder particles. However, excess rotation can impart excessive kinetic energy, causing ejection of particles and a non-uniform layer. Interestingly, the identified optimal surface velocities for counter-rotation as well as rotational oscillation are very similar, suggesting this quantity as the critical kinematic parameter. When these conditions are chosen appropriately, layers with packing fractions beyond 50% are predicted for layer thicknesses as small as ~2 times D90 of the exemplary powder, and the layer quality is robust with respect to substrate adhesion over a 10-fold range. The latter is an important consideration given the spatially varying substrate conditions in AM due to the combination of fused/bound and bare powder regions. As compared to counter-rotation, the proposed rotational oscillation is particularly attractive because it can overcome practical issues with mechanical runout of roller mechanisms. In particular, the application to rubber-coated rollers, which promises to reduce the risk of tool damage and particle streaking, is recommended for future investigation.

In supramolecular materials, multiple weak binding groups can act as a single collective unit when confined to a localized volume, thereby producing strong but dynamic bonds between material building blocks. This principle of multivalency provides a versatile means of controlling material assembly, as both the number and the type of supramolecular moieties become design handles to modulate the strength of intermolecular interactions. However, in materials with building blocks significantly larger than individual supramolecular moieties (e.g., polymer or nanoparticle scaffolds), the degree of multivalency is difficult to predict or control, as sufficiently large scaffolds inherently preclude separated supramolecular moieties from interacting. Because molecular models commonly used to examine supramolecular interactions are intrinsically unable to examine any trends or emergent behaviors that arise due to nanoscale scaffold geometry, our understanding of the thermodynamics of these massively multivalent systems remains limited. Here we address this challenge via the coassembly of polymer-grafted nanoparticles and multivalent polymers, systematically examining how multivalent scaffold size, shape, and spacing affect their collective thermodynamics. Investigating the interplay of polymer structure and supramolecular group stoichiometry reveals complicated but rationally describable trends that demonstrate how the supramolecular scaffold design can modulate the strength of multivalent interactions. This approach to self-assembled supramolecular materials thus allows for the manipulation of polymer-nanoparticle composites with controlled thermal stability, nanoparticle organization, and tailored meso- to microscopic structures. The sophisticated control of multivalent thermodynamics through precise modulation of the nanoscale scaffold geometry represents a significant advance in the ability to rationally design complex hierarchically structured materials via self-assembly.

Three-dimensional (3D) battery architectures have been envisioned to enable high energy density electrodes without the associated power drop experienced by planar cells. However, the development of 3D cells is hampered by difficulties producing conformal solid-state electrolytes (SSE), solid polymer electrolytes (SPE) and gel polymer electrolytes (GPE) that are pinhole-free and have adequate ionic conductivities. Fortunately, electrolytes in 3D cells are often utilized at lower thickness, which may compensate the decreased ionic conductivity. Here, we comprehensively review potential 3D SSE, SPE and GPE electrolyte materials by compiling their thickness and room temperature ionic conductivity. We use area specific resistance (ASR) as a metric to compare 3D electrolytes with one another and conventional electrolytes. We find that certain process-material combinations, such as atomic layer deposition of SSEs, electrodeposition of SPEs and GPEs, and initiated chemical vapor deposition of SPEs demonstrate ASRs beneath the interfacial impedances of Li-based systems and approach state-of-the-art electrolytes. We also comment on additional factors, such as electrochemical stability, that should be evaluated when determining 3D electrolyte suitability. Future research should focus on adapting known materials chemistries for conformal deposition techniques to further improve the ionic conductivity, as these techniques are capable of producing the necessary thicknesses and conformality.

Abstract Visualization, contextualization, and hands-on experiences are key to effective engineering education, and go hand-in-hand with the understanding of theoretical concepts. Learners must employ highly-developed visual and spatial thinking, yet teaching still often relies on two-dimensional boards and screens to render inherently three-dimensional concepts. Limitations to resources (e.g., equipment or machine shop access), geography, and safety considerations constrain the learner's opportunity to see or perform authentic hands-on activities. Augmented Reality (AR) provides a compelling opportunity to address these gaps because of its inherent three dimensionality, connection to the learner's physical context, scalability, and responsiveness. Unlike Virtual Reality, wherein interactive headsets cost hundreds of dollars each, many AR apps are hosted through the ubiquitous smartphone and would therefore increase the feasibility of implementation for a wider range of institutions of higher learning. However, AR instruction is a relatively new and growing research field and the assessment of learning gains has primarily focused on lower level cognitive skills. We present the pedagogy, design and development, and course implementation of a vision-based AR app to teach higher level cognitive skills in Bloom's taxonomy: apply, analyze, and evaluate. The app enables learners to manipulate, and virtually disassemble various parts and products (representing high-volume manufacturing processes), while receiving scaffolded guidance. We used an iterative process to design the app by implementing user feedback. The app has now been released into an online manufacturing course (Fundamentals of Manufacturing Processes). Learner reflections reveal engagement with manufacturing analysis, experience of the app, and attitudes towards AR technology. The development of a codebook was used to evaluate learner reflections with the goal of understanding the opportunities learners have to engage with manufacturing analysis. The iterative development of the codebook and results of applying the codebook to learner reflections are reported; overall inter-rater reliability computed using Cohen's Alpha is 85.48%. The experience feedback indicates that the activity was well received with 70% of users indicating an overall positive experience using the app. 79% of attitude feedback was positive indicating that learners are interested in using AR applications. AR-augmented instruction is a cost-effective approach that makes accessible time- and resource-constrained hands-on activities through virtualization, and bridges the gap between in-person and fully virtual instruction. Ongoing work is extending the AR platform to additional manufacturing processes, products, and components.

Hands-on fabrication skills require complementary capacity for spatial thinking, but teaching often relies on 2D drawings to render inherently three-dimensional concepts. Computer-aided Design (CAD) software provides robust engineering visualization, but 3D models are displayed on a 2D screen and separate from the learner's physical context. Augmented Reality (AR) is another 3D technology that has the potential to facilitate embodied learning and an introductory student's transfer of concepts to tasks, because of its ability to integrate three-dimensional information into the physical context of authentic tasks.