Materials Science
Assembly Line
SonoPrint: Acoustically Assisted Volumetric 3D Printing for Composites
Advancements in additive manufacturing in composites have transformed various fields in aerospace, medical devices, tissue engineering, and electronics, enabling fine-tuning material properties by reinforcing internal particles and adjusting their type, orientation, and volume fraction. This capability opens new possibilities for tailoring materials to specific applications and optimizing the performance of 3D-printed objects. Existing reinforcement strategies are restricted to pattern types, alignment areas, and particle characteristics. Alternatively, acoustics provide versatility by controlling particles independent of their size, geometry, and charge and can create intricate pattern formations. Despite the potential of acoustics in most 3D printing, limitation arises from the scattering of the acoustic field between the polymerized hard layers and the unpolymerized resin, leading to undesirable patterning formation. However, this challenge can be addressed by adopting a novel approach that involves simultaneous reinforcement and printing the entire structure. Here, we present SonoPrint, an acoustically-assisted volumetric 3D printer that produces mechanically tunable composite geometries by patterning reinforcement microparticles within the fabricated structure. SonoPrint creates a standing wave field that produces a targeted particle motif in the photosensitive resin while simultaneously printing the object in just a few minutes. We have also demonstrated various patterning configurations such as lines, radial lines, circles, rhombuses, quadrilaterals, and hexagons using microscopic particles such as glass, metal, and polystyrene particles. Furthermore, we fabricated diverse composites using different resins, achieving 87 microns feature size. We have shown that the printed structure with patterned microparticles increased their tensile and compression strength by ∼38% and ∼75%, respectively.
Inferring material properties from FRP processes via sim-to-real learning
Fiber reinforced polymers (FRP) provide favorable properties such as weight-specific strength and stiffness that are central for certain industries, such as aerospace or automotive manufacturing. Liquid composite molding (LCM) is a family of often employed, inexpensive, out-of-autoclave manufacturing techniques. Among them, resin transfer molding (RTM), offers a high degree of automation. Herein, textile preforms are saturated by a fluid polymer matrix in a closed mold. Both impregnation quality and level of fiber volume content are of crucial importance for the final part quality. We propose to simultaneously learn three major textile properties (fiber volume content and permeability in X and Y direction) presented as a three-dimensional map based on a sequence of camera images acquired in flow experiments and compare CNNs, ConvLSTMs, and Transformers. Moreover, we show how simulation-to-real transfer learning can improve a digital twin in FRP manufacturing, compared to simulation-only models and models based on sparse real data. The overall best metrics are: IOU 0.5031 and Accuracy 95.929 %, obtained by pretrained transformer models.
A New Type of Glass Promises to Cut Glass Manufacturing's Carbon Footprint in Half
The invention, known as LionGlass and engineered by researchers at Penn State, needs considerably less energy to produce and is highly damage-resistant compared to the standard soda lime silicate glass. The research group has filed a patent application as an initial step toward bringing the product to market.
Mauro believes that the enhanced strength of LionGlass means that the products made from it could be lighter in weight. Since LionGlass is 10 times more damage resistant compared to present glass, it could be considerably thinner.
3D Printing Materials Explained: Compare FDM, SLA, and SLS
🖨️ The Transformative Power of Innovations in Additive Materials
The slow but steady ascent of additive manufacturing (AM) into mainstream production environments is changing how products of all kinds are designed, made, and delivered. The evolution of advanced materials is further elevating the industry by empowering end-use parts and products with improved physical properties for greater utilization at lower costs as well as faster delivery and less waste.
Many, if not all, of the most popular additive materials can be enhanced through refinement of polymer formulations and compounding processes. Highly specialized skills in controlling the morphology and particle crystallization are needed, requiring chemists and scientists to create and iterate new material formulas.
In the world of AM, breakthroughs in polymer innovations are being driven by the demand for more affordable, lighter and higher-modulus composites as well as the ability to print materials that previously were too difficult to integrate into additive processes Additionally, the incorporation of value-added attributes to existing polymers is ushering in a new class of engineered materials with special functionality, such as flame-retardant or resistant attributes; reinforced materials containing glass fiber, as well as mineral fillers, carbon fiber, or nanotubes.
The inclusion of conductive attributes also is on the rise to address Electrostatic Dissipative (ESD), EMI-shielded or electrically conductive materials. The need for lubricated materials also is vital to reduce part friction and wear, along with the addition of UV-stable materials to reinforce part longevity. Many of these attributes are designed to extend the usefulness of materials for traditional manufacturing and 3D-printing applications, and vice versa.
Closed-loop fully-automated frameworks for accelerating materials discovery
Our work shows that a fully-automated closed-loop framework driven by sequential learning can accelerate the discovery of materials by up to 10-25x (or a reduction in design time by 90-95%) when compared to traditional approaches. We show that such closed-loop frameworks can lead to enormous improvement in researcher productivity in addition to reducing overall project costs. Overall, these findings present a clear value proposition for investing in closed-loop frameworks and sequential learning in materials discovery and design enterprises.
World-First Project to 'Self Heal' Cracked Concrete Using Sloppy Sludge Could Save $1.4 Billion Annual Repair Bill to Australia’s Sewer Pipes
A world-first project led by University of South Australia sustainable engineering expert Professor Yan Zhuge is trialling a novel solution to halt unprecedented levels of corrosion in the country’s ageing concrete pipelines. Self-healing concrete, in the form of microcapsules filled with water treatment sludge, could be the answer.
Corrosive acid from sulphur-oxidising bacteria in wastewater, along with excessive loads, internal pressure and temperature fluctuations are cracking pipes and reducing their life span, costing hundreds of millions of dollars to repair every year across Australia.
“Sludge waste shows promise to mitigate microbial corrosion in concrete sewer pipes because it works as a healing agent to resist acid corrosion and heal the cracks,” Prof Zhuge says.
The role of temperature on defect diffusion and nanoscale patterning in graphene
Jesse said, “It heals locally, like the (fictitious) liquid-metal T-1000 in Terminator 2: Judgment Day.”
Graphene is of great scientific interest due to a variety of unique properties such as ballistic transport, spin selectivity, the quantum hall effect, and other quantum properties. Nanopatterning and atomic scale modifications of graphene are expected to enable further control over its intrinsic properties, providing ways to tune the electronic properties through geometric and strain effects, introduce edge states and other local or extended topological defects, and sculpt circuit paths. The focused beam of a scanning transmission electron microscope (STEM) can be used to remove atoms, enabling milling, doping, and deposition. Utilization of a STEM as an atomic scale fabrication platform is increasing; however, a detailed understanding of beam-induced processes and the subsequent cascade of aftereffects is lacking. Here, we examine the electron beam effects on atomically clean graphene at a variety of temperatures ranging from 400 to 1000 °C. We find that temperature plays a significant role in the milling rate and moderates competing processes of carbon adatom coalescence, graphene healing, and the diffusion (and recombination) of defects. The results of this work can be applied to a wider range of 2D materials and introduce better understanding of defect evolution in graphite and other bulk layered materials.
Simplifying the world of materials properties evaluation using AI
Mettler-Toledo, together with CSEM and ZHAW has developed AIWizard: An artificial intelligence (AI) option for their STARe software that will make it easier to interpret DSC curves for thermal analysis.
Currently, manufacturers have high expectations surrounding the performance of their materials. A sealing ring must not become brittle, a PET bottle cannot deform, and medications need to react within the body at exactly the right time. Across the material science domain, Mettler-Toledo’s dynamic Differential Scanning Calorimeter (DSC) has become an indispensable tool for many. Thermal analysis makes a valuable contribution from quality control to research and development of materials and chemical compounds.
These autonomous factories on satellites will produce materials in space that can’t be made on Earth
Bacon and cofounder-CEO Joshua Western want to take advantage of the unique conditions in space—the very low gravity and the fact that it’s an almost perfect vacuum—to make materials that can’t be made on Earth. Some new materials have already been produced on the International Space Station. A new type of fiber-optic cable, for example, is cloudy when it’s made on Earth because of gravity and impurities in the air, but crystal clear when made in space.
In space, it’s possible to manufacture new alloys that can be used to make bigger, stronger, turbines on aircraft, so planes use less fuel. On electric planes, new materials can make the electronic connections between batteries and the propeller motor more efficient, so the planes need less cooling equipment and can carry more passengers. Space factories are also well-suited to make better batteries for electric planes or cars. Wind turbines, for example, are more efficient the larger they are, but have to be made in pieces so they can be transported to a site for installation, and then held together with bolts. By making bolts that are stronger than what can be manufactured on Earth, it’s possible to develop a larger, more efficient wind turbine that can create more energy.
Machine-learning system accelerates discovery of new materials for 3D printing
The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses.
A material developer selects a few ingredients, inputs details on their chemical compositions into the algorithm, and defines the mechanical properties the new material should have. Then the algorithm increases and decreases the amounts of those components (like turning knobs on an amplifier) and checks how each formula affects the material’s properties, before arriving at the ideal combination.
The researchers have created a free, open-source materials optimization platform called AutoOED that incorporates the same optimization algorithm. AutoOED is a full software package that also allows researchers to conduct their own optimization.
Machine learning predictions of superalloy microstructure
Gaussian process regression machine learning with a physically-informed kernel is used to model the phase compositions of nickel-base superalloys. The model delivers good predictions for laboratory and commercial superalloys. Additionally, the model predicts the phase composition with uncertainties unlike the traditional CALPHAD method.
Complex machine validations performed with multiphysics simulation
When new materials and methods are applied to manufacturing, it increases product complexity. But the benefits can be significant: Products are now lighter, smaller and more easily customizable to meet consumer demands. Multiphysics simulations enable machine builders to explore the physical interactions complex products encounter, virtually. It tracks interactive data of product performance, safety and longevity.
Using AI to Find Essential Battery Materials
KoBold’s AI-driven approach begins with its data platform, which stores all available forms of information about a particular area, including soil samples, satellite-based hyperspectral imaging, and century-old handwritten drilling reports. The company then applies machine learning methods to make predictions about the location of compositional anomalies—that is, unusually high concentrations of ore bodies in the Earth’s subsurface.
Leveraging AI and Statistical Methods to Improve Flame Spray Pyrolysis
Flame spray pyrolysis has long been used to make small particles that can be used as paint pigments. Now, researchers at Argonne National Laboratory are refining the process to make smaller, nano-sized particles of various materials that can make nano-powders for low-cobalt battery cathodes, solid state electrolytes and platinum/titanium dioxide catalysts for turning biomass into fuel.
Cell Phones, Sporting Goods, and Soon, Cars: Ford Innovates with “Miracle” Material, Powerful Graphene for Vehicle Parts
Graphene has recently generated the enthusiasm and excitement in the automotive industry for paint, polymer and battery applications.
Dubbed a “miracle material” by some engineers, graphene is 200 times stronger than steel and one of the most conductive materials in the world. It is a great sound barrier and is extremely thin and flexible. Graphene is not economically viable for all applications, but Ford, in collaboration with Eagle Industries and XG Sciences, has found a way to use small amounts in fuel rail covers, pump covers and front engine covers to maximize its benefits.
“A small amount of graphene goes a long way, and in this case, it has a significant effect on sound absorption qualities,” said John Bull, president of Eagle Industries. The graphene is mixed with foam constituents, and tests done by Ford and suppliers has shown about a 17 percent reduction in noise, a 20 percent improvement in mechanical properties and a 30 percent improvement in heat endurance properties, compared with that of the foam used without graphene.