We took on the challenge of re-tasking a $5 Raspberry Pi Zero W computer-on-a-stick and a $25 Pi Camera using the latest software platforms available to create an intelligent security system that recognizes people in front of the camera and notifies the owner via SMS text messages or email.
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Microscopic photos of top and side views of printing results with a 0.38 mm wide extrusion path: (a) without versus (b) with overlapping by 0.36 mm respectively. Overlapping extrusion paths exhibit over-extrusion of material at the overlapping region, which results in unwanted blobs on the surface of the print.
“In our latest paper we present a type of microstructure which can be printed using continuous extrusion so that we can generate infill structures which follow a user specified density field to be printed reliably by standard desktop FDM printers,” Kuipers, a Software Engineer and Researcher for Ultimaker, wrote in an email.
“This is the first algorithm in the world which is able to generate spatially graded microstructures while adhering to continuous extrusion in order to ensure printing reliability.”
Because 3D printing offers such flexible fabrication, many people want to design structures with spatially graded material properties. But, it’s hard to achieve good print quality when using FDM technology to 3D print FGM, since these sorts of infill structures feature complex geometry. In terms of making foam structures with graded density using FDM, the researchers knew they needed to develop a method to generate “infill structures according to a user-specific density distribution.”
The abstract reads, “In this paper, we propose a new type of density graded structure that is particularly designed for 3D printing systems based on filament extrusion. In order to ensure high-quality fabrication results, extrusion-based 3D printing requires not only that the structures are self-supporting, but also that extrusion toolpaths are continuous and free of self-overlap. The structure proposed in this paper, called CrossFill, complies with these requirements. In particular, CrossFill is a self-supporting foam structure, for which each layer is fabricated by a single, continuous and overlap-free path of material extrusion. Our method for generating CrossFill is based on a space-filling surface that employs spatially varying subdivision levels. Dithering of the subdivision levels is performed to accurately reproduce a prescribed density distribution.”
Their method – a novel type of FDM printable foam structure – offers a way to refine the structure to match a prescribed density distribution, and provides a novel self-supporting, space-filling surface to support spatially graded density, as well as an algorithm that can merge an infill structure’s toolpath with the model’s boundary for continuity. This space-filling infill surface is called CrossFill, as the toolpath resembles crosses.
“Each layer of CrossFill is a space-filling curve that can be continuously extruded along a single overlap-free toolpath,” the researchers wrote. “The space-filling surface consists of surface patches which are embedded in prism-shaped cells, which can be adaptively subdivided to match the user-specified density distribution. The adaptive subdivision level results in graded mechanical properties throughout the foam structure. Our method consists of a step to determine a lower bound for the subdivision levels at each location and a dithering step to refine the local average densities, so that we can generate CrossFill that closely matches the required density distribution. A simple and effective algorithm is developed to merge a space-filling curve of CrossFill of a layer into the closed polygonal areas sliced from the input model. Physical printing tests have been conducted to verify the performance of the CrossFill structures.”
The researchers say that the user prescribes density distribution, and can use CrossFill and its space-filling surfaces, with continuous cross sections, to “reliably reproduce the distribution using extrusion-based printing.” CrossFill surfaces are built by using subdivision rules on prism-shaped cells, each of which contains a surface patch that’s “sliced into a line segment on each layer to be a segment” of the toolpath, which will be made with a constant width; cell size determines the density.
“By adaptively applying the subdivision rules to the prism cells, we create a subdivision structure of cells with a density distribution that closely matches a user-specified input,” the team wrote. “Continuity of the space-filling surface across adjacent cells with different subdivision levels – both horizontally and vertically – is ensured by the subdivision rules and by post-processing of the surface patches in neighboring cells.”
The subdivision system distinguishes an H-prism, which is built by cutting a cube in half vertically along a diagonal of the horizontal faces, and a Q-prism, generated by spitting a cube into quarters along the faces’ diagonals. To learn more about this system and the team’s algorithms, check out the paper in its entirety.
Schematic overview of our method. The top row shows a 2D analogue of our method for clear visualization. The prism-shaped cells in the bottom row are visualized as semi-opaque solids to keep the visualization uncluttered. Red lines in the bottom row highlight the local subdivisions performed in the dithering phase.
The researchers also explained the method’s toolpath generation in their paper, starting with how to slice the infill structure into a continuous 2D polygonal curve for each layer of the object, which is followed by fitting a layer’s curve “into the region of an input 3D model.”
Experiments measuring features like accuracy, computation time, and elastic behavior were completed on an Intel Core i7-7500U CPU @ 2.70 GHz, using test structures 3D printed out of white TPU 95A on Ultimaker 3 systems with the default Cura 4.0 profile of 0.1 mm layer thickness. The team also discussed various applications for CrossFill, such as imaging phantoms for the medical field or cushions and packaging.
“The study of experimental tests shows that CrossFill acts very much like a foam although future work needs to be conducted to further explore the mapping between density and other material properties,” the researchers concluded. “Another line of research is to further enhance the dithering technique, e.g. changing the weighing scheme of error diffusion.”
CrossFill applications. (a) Bicycle saddle with a density specification. A weight of 33 N is added on various locations to show the different response of different density infill. (b) Teddy bear with a density specification. (c) Shoe sole with densities based on a pressure map of a foot. (d) Stanford bunny painted with a density specification. (e) Medical phantom with an example density distribution for calibrating an MRI scanning procedure.
The team’s open source implementation is available here on GitHub. To learn more, check out their video below:
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Artificial intelligence (AI) company Intellegens, which is a spin-off from the University of Cambridge, created a unique toolset that can train deep neural networks from noisy or sparse data. The machine learning algorithm, called Alchemite, was created at the university’s Cavendish Laboratory, and is now making it faster, easier, and less expensive to design new materials for 3D printing projects. The Alchemite engine is the company’s first commercial product, and was recently used by a research collaboration to design a new nickel-based alloy for direct laser deposition.
Researchers at the university’s Stone Group, along with several commercial partners, saved about $10 million and 15 years in research and development by using the Alchemite engine to analyze information about existing materials and find a new combustor alloy that could be used to 3D print jet engine components that satisfy the aerospace industry’s exacting performance targets.
“Worldwide there are millions of materials available commercially that are characterised by hundreds of different properties. Using traditional techniques to explore the information we know about these materials, to come up with new substances, substrates and systems, is a painstaking process that can take months if not years,” Gareth Conduit, the Chief Technology Officer at Intellegens, explained. “Learning the underlying correlations in existing materials data, to estimate missing properties, the Alchemite engine can quickly, efficiently and accurately propose new materials with target properties – speeding up the development process. The potential for this technology in the field of direct laser deposition and across the wider materials sector is huge – particularly in fields such as 3D printing, where new materials are needed to work with completely different production processes.”
Alchemite engine
Alchemite is based on deep learning algorithms which are able to see correlations between all available parameters in corrupt, fragmented, noisy, and unstructured datasets. The engine then unravels these data problems and creates accurate models that are able to find errors, optimize target properties, and predict missing values. Alchemite has been used in many applications, including drug discovery, patient analytics, predictive maintenance, and advanced materials.
Thin films of oxides deposited with atomic layer precision using pulsed laser deposition. [Image: Adam A. Læssøe]
“Worldwide there are millions of materials available commercially that are characterised by hundreds of different properties. Using traditional techniques to explore the information we know about these materials, to come up with new substances, substrates and systems, is a painstaking process that can take months if not years. Learning the underlying correlations in existing materials data, to estimate missing properties, the Alchemite engine can quickly, efficiently and accurately propose new materials with target properties – speeding up the development process,” said Gareth, who is also a Royal Society University Research Fellow at the University of Cambridge. “The potential for this technology in the field of direct laser deposition and across the wider materials sector is huge – particularly in fields such as 3D printing, where new materials are needed to work with completely different production processes.”
Direct laser deposition – a form of DED – is used in many industries to repair and manufacture bespoke and high-value parts, such as turbine blades, oil drilling tools, and aerospace engine components, like the Stone Group is working on. As with most 3D printing methods, direct laser deposition can help component manufacturers save a lot of time and money, but next generation materials that can accommodate high stress gradients and temperature are needed to help bring the process to its full potential.
When it comes to developing new materials with more traditional methods of research, a lot of expensive and time-consuming trial and error can occur, and the process becomes even more difficult when it comes to designing new alloys for direct laser deposition. As of right now, this AM method has only been applied to about ten nickel-alloy compositions, which really limits how much data is available to use for future research. But Intellegens’ Alchemite engine helped the team get around this, and complete the material selection process more quickly.
(a) Secondary electron micrograph image for AlloyDLD. (b) Representative geometry of a sample combustor manufactured by direct laser deposition. [Image: Intelligens]
Because Alchemite can learn from data that’s only 0.05% complete, the researchers were able to confirm potential new alloy properties and predict with higher accuracy how they would function in the real world. Once they used the engine to find the best alloy, the team completed a series of experiments to confirm its physical properties, such as fatigue life, density, phase stability, creep resistance, oxidation, and resistance to thermal stresses. The results of these experiments showed that the new nickel-based alloy was much better suited for direct laser deposition 3D printing, and making jet engine components, than other commercially available alloys.
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In an article entitled “Tuning the Center of Gravity of 3D Printed Artifacts,” a pair of researchers discusses how they came up with an Algorithms-Aided Design (AAD) approach to shifting the center of gravity of 3D printed objects to a desired location.
“When the conventional design and fabrication pipeline of 3D printers and additive manufacturing machinery is employed, information about the interior of the artifacts is lost during the conversion of the design files to the STL file format,” the researchers point out. “This de facto file standard only stores the boundary information of the objects. Even though the designed artifact has heterogeneous interior in the Computer Aided Design (CAD) software, after the conversion it becomes a homogeneous solid.”
The researchers’ method does not require an STL file, as they used a query-based approach in which the built-in algorithm communicates with CAD software to acquire the necessary information about the design. First, the designed object in CAD software is decomposed into voxels of predefined sizes with AAD add-on software. Then the desired center of gravity and the amount of extra material available are entered by the user. This additional material is distributed to the voxels by the algorithm so that the center of gravity of the final object is at the predefined location.
“At the end of the design process, filling percentages of some voxels is altered which made the structure internally heterogeneous,” the researchers continue. “Then the final structure is directly sliced and the trajectories are converted to G-codes. Using the generated file, artifacts are printed on a desktop FFF printer. With the developed algorithm, we can modify the coordinates of the center of gravity of any shape by adjusting their interior structures and fabricate them on FFF printers.”
Why do this? Shifting the center of gravity can create a more stable object, according to the researchers, without altering any of the visible physical characteristics of the object. They point out that several other methods have been used to do this, including using Voronoi cells to create a heterogeneous interior. The researchers took this concept and added more mass to increase the effect of the Voronoi cells.
The main objective of the algorithm the researchers developed is to create a secondary shape inside the input geometry so that the overall center of gravity of the 3D printed object is in the desired position. The algorithm is divided into several steps. First, voxelization of the initial geometry needs to be done to create a base structure to hold the additional mass to be placed inside the initial geometry. The mass and the center of gravity of the voxelized geometry are then calculated.
“After the amount of additional mass is determined, center coordinates of the additional mass are found,” the researchers state. “After the formation of the uniform box around the center of additional mass, it is aimed to fit the created box inside the initial geometry so that the integrity and the accuracy of the desired artifact are maintained. Lastly, direct slicing algorithm is run to obtain the G-codes to fabricate the final geometry with the modified interior structure.”
To test the process, the researchers 3D printed a low-poly bunny that could sit on various surfaces once the center of gravity was adjusted. The target center of gravity was set so that it could stand on its tail. This was successful, though there were some defects on the surface of the print due to the lack of support structures for overhanging areas. The researchers admit that their technique needs some modification, but that in the future it will likely “have a major impact on industries that seek for an out of the box solution with the help of using original designs.”
Authors of the study include Mert Keles and Ulas Yaman.
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“Umbilical digital” project is a kind of farm where special algorithm that runs on arduino board “cares about” digital nurslings – Japanese Tamagotchi toys that became in due time one of the first pet simulators. Viewing condition of every nursling, the system performs all the necessary manipulations to support “life” and “good spirit” of digital animals. Through simulation of pressing the buttons, the system behaves like an ideal careful host, and a pet exists as if it was nursed by a human.
Looking at the behavior of “organisms”, the algorithm constantly learns and modifies itself. Stats of all processes in real time are shown on a small thermal printer that keeps “chronicle” of the farm that lets retrace the history of colony and its evolution.
Every Tuesday is Art Tuesday here at Adafruit! Today we celebrate artists and makers from around the world who are designing innovative and creative works using technology, science, electronics and more. You can start your own career as an artist today with Adafruit’s conductive paints, art-related electronics kits, LEDs, wearables, 3D printers and more! Make your most imaginative designs come to life with our helpful tutorials from the Adafruit Learning System. And don’t forget to check in every Art Tuesday for more artistic inspiration here on the Adafruit Blog!
Each Friday is PiDay here at Adafruit! Be sure to check out our posts, tutorials and new Raspberry Pi related products. Adafruit has the largest and best selection of Raspberry Pi accessories and all the code & tutorials to get you up and running in no time!
engine can quickly, efficiently and accurately propose new materials with target properties – speeding up the development process,” said Gareth, who is also a Royal Society University Research Fellow at the University of Cambridge. “The potential for this technology in the field of direct laser deposition and across the wider materials sector is huge – particularly in fields such as 3D printing, where new materials are needed to work with completely different production processes.”
