We’ve got so much happening here at Adafruit that it’s not always easy to keep up! Don’t fret, we’ve got you covered. Each week we’ll be posting a handy round-up of what we’ve been up to, ranging from learn guides to blog articles, videos, and more.
Together as a community, we reached over 14,000+ humans thank you! We share projects, coordinate events, make new friends, build open-source together like CircuitPython, we’ve worked really hard to make this a special place for everyone to share their projects, code, and things they make.
We’ve got so much happening here at Adafruit that it’s not always easy to keep up! Don’t fret, we’ve got you covered. Each week we’ll be posting a handy round-up of what we’ve been up to, ranging from learn guides to blog articles, videos, and more.
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Meet Device Simulator Express, #PythonSim a @MSFTGarage project, built by Garage interns that makes it easier to program the @adafruit Circuit Playground Express in #Python, with or without a physical device
OK! Big news! Meet Device Simulator Express, a Microsoft Garage project, built by Garage interns that makes it easier to program the Adafruit Circuit Playground Express in Python / CircuitPython, with or without a physical device!
This summer 16 groups of Garage interns tackled interesting engineering challenges ranging from making apps more accessible to VR solutions for cybersecurity. One of them was sponsored by the Python Tools for AI team and electronics paragon Adafruit to and set out to make programming embedded solutions for IoT devices simpler and more available to a broader audience.
This 3D printed heart beats rhythmically, powered by a servo motor and a Circuit Playground Express board connected to a potentiometer, which allows the user to increase or decrease the heart rate.
From the ‘DeepRC Robot Car’ project on hackaday.io by Piotr Sokólski
The ‘DeepRC Robot Car’ project on hackaday.io by Piotr Sokólski aims to create a miniature self-driving car that can be trained at home. Probably the coolest part about this project is that it incorporates a smartphone for a number of the pieces of hardware. For instance, a mirror was used to shift the phone camera view to the front of the vehicle so the car can see the road (see below). The chassis was 3D printed and a number of other small electronics were used to build the car (like the NRF52 SOC).
For controlling the actuators and reading telemetry data a small number of electronic components are installed on the chassis. The main circuit board is based on an excellent NRF52 SOC. It provides a Bluetooth LE radio to communicate with the phone. The servo is controlled by the chip directly, however the motor requires an additional Electronic Speed Controller (ESC).
From the ‘DeepRC Robot Car’ project on hackaday.io by Piotr Sokólski
The software powering the robot was split between an app on the phone and a computer. @pyetras trained the robot to avoid collisions using deep reinforcement learning. Specifically, the TensorFlow agents implementation of the Soft Actor-Critic algorithm was used.
For a Deep Reinforcement Learning algorithm I chose Soft Actor-Critic (SAC)(specifically the tf-agents implementation). I picked this algorithm since it promises to be sample-efficient (therefore decreasing data collection time, an important feature when running on a real robot and not a simulation) and there were already some successful applications on simulated cars and real robots.
From techxplore.com. Credit: UCLA Engineering Institute for Technology Advancement
The Ozcan Lab at UCLA has created optical neural networks using 3D printing and lithography. TensorFlow models were trained on the MNIST, Fashion-MNIST, and CIFAR-10 data sets using beefy GPUs. The trained models were then translated into multiple diffractive layers. These layers create the optical neural network. What the model lacks in adaptability it gains in speed as it can make predictions “at the speed of light” without any power. The basic workflow involves passing light through an input object which is filtered through the entire optical neural network to a detector which captures the results.
…each network is physically fabricated, using for example 3-D printing or lithography, to engineer the trained network model into matter. This 3-D structure of engineered matter is composed of transmissive and/or reflective surfaces that altogether perform machine learning tasks through light-matter interaction and optical diffraction, at the speed of light, and without the need for any power, except for the light that illuminates the input object. This is especially significant for recognizing target objects much faster and with significantly less power compared to standard computer based machine learning systems, and might provide major advantages for autonomous vehicles and various defense related applications, among others.
If you’d like to learn more about Photonics checkout the research happening at the Ozcan Lab. If you’d like more details about diffractive deep neural networks checkout this publication in Science or the most recent Ozcan Lab publication on the topic.
We’ve got so much happening here at Adafruit that it’s not always easy to keep up! Don’t fret, we’ve got you covered. Each week we’ll be posting a handy round-up of what we’ve been up to, ranging from learn guides to blog articles, videos, and more.
Big, exciting news folks – AdaBox subscriptions are now available in Netherlands, Norway, and Switzerland! Click here and sign up now to receive AdaBox013, shipping September 2019.
AdaBox is a curated quarterly subscription service centered around products from the Adafruit ecosystem and is designed for makers of all levels, with a special focus on folks just starting out.
Put an Adafruit Gemma M0 to work as you attach 120 Adafruit NeoPixels to the lapels of a vintage band jacket.
In a nutshell, this project starts by soldering small sections of NeoPixels into two longer strips and arrange them into a matrix formation. Add a clicky button to a Gemma M0 to showcase some beautiful animations and pick a brightness setting. Connect the two strips of NeoPixels to the Gemma M0, utilizing Mark Kriegsman’s special XY mapping code to tell the Gemma the location of each pixel. Finally, protect the strips from the elements, and attach it to a costume jacket. Whether you’re headed to TTITD (that thing in the desert aka burning man) or leading a marching band, this wearable is sure to make you the star of any event.
We’ve got so much happening here at Adafruit that it’s not always easy to keep up! Don’t fret, we’ve got you covered. Each week we’ll be posting a handy round-up of what we’ve been up to, ranging from learn guides to blog articles, videos, and more.
Together as a community, we reached over 13,000+ humans thank you! We share projects, coordinate events, make new friends, build open-source together like CircuitPython, we’ve worked really hard to make this a special place for everyone to share their projects, code, and things they make.
Mike Barela also shed some light on Adafruit and Digi-Key Electronics’s search for historic dates in electronics history and put out a call for founding dates!
Use an Adafruit Feather M4 and Prop-Maker FeatherWing to make a portal NeoTrellis soundbox! Play and trigger motion activated audio samples with CircuitPython. Build and assemble the 3D printed enclosure to make your own with built-in speaker and rechargeable battery!
We’ve got so much happening here at Adafruit that it’s not always easy to keep up! Don’t fret, we’ve got you covered. Each week we’ll be posting a handy round-up of what we’ve been up to, ranging from learn guides to blog articles, videos, and more.
You’ve heard of machine learning (ML), but what is it? And do you have to buy specialty hardware to experiment? Nope! If you have some Adafruit hardware, you can build some Tiny ML projects today!
We’ve wrappered the TensorFlow Lite micro speech demo to Arduino so you can do basic speech recognition on our SAMD51 boards. Read more
Learn how to program 80s-style with Logo running on an Apple II – it’s Turtles all the way down!
You may have seen the turtle graphics library that ladyada ported to CircuitPython and thought, “Wow, that’s cool! But can I do that on 30+ year old hardware?” Or if you’re above a certain age you may remember doing something similar in school on an Apple II. Learn more.
We’ve got so much happening here at Adafruit that it’s not always easy to keep up! Don’t fret, we’ve got you covered. Each week we’ll be posting a handy round-up of what we’ve been up to, ranging from learn guides to blog articles, videos, and more.
Tiny Machine Learning on the Edge with TensorFlow Lite Running on SAMD51 (video and/or skip to the demo part at 7 min 28 secs). You’ve heard of machine learning (ML), but what is it? And do you have to buy specialty hardware to experiment? If you have some Adafruit hardware, you can build some Tiny ML projects today!
Our 1,900th guide has landed in the Learn System! It’s John Park’s Trash Panda 2: Garbage Day and it’s lots of fun! You can make it with MakeCode Arcade, and mod and hack it all you like.
In Trash Panda 2: Garbage Day, you play as the suburban dweller just trying to get some sleep when the raccoons and cats decide its time to make noise and throw garbage our of the trash bins! You must try to stop them by shining your flashlight on them. But you can only play at night, so be sure that your PyGamer or PyBadge’s light sensor indicates it’s dark out!
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.
Discuss this story and other 3D printing topics at 3DPrintBoard.com or share your thoughts in the Facebook comments below.
We made it to the weekend! To celebrate, check out our 3D Printing News Briefs today, which covers business, research, and a few other topics as well. PostProcess has signed its 7th channel partner in North America, while GEFERTEC partners with Linde on 3D printing research. Researchers from Purdue and USC are working together to develop new AI technology, and the finalists for Additive World’s Design for Additive Manufacturing 2019 competition have been announced. Finally, Marines in Hawaii used 3D printing to make a long overdue repair part, and Thermwood and Bell teamed up to 3D print a helicopter blade mold.
PostProcess Technologies Signs Latest North American Channel Partner
PostProcess Technologies, which provides automated and intelligent post-printing solutions for additive manufacturing, has announced its seventh North American Channel Partner in the last year: Hawk Ridge Systems, the largest global provider of 3D design and manufacturing solutions. This new partnership will serve as a natural extension of Hawk Ridge Systems’ AM solutions portfolio, and the company will now represent PostProcess Technologies’ solution portfolio in select North American territories.
“Hawk Ridge Systems believes in providing turnkey 3D printers for our customers for use in rapid prototyping, tooling, and production manufacturing. Often overlooked, post-printing is a critical part of all 3D printing processes, including support removal and surface finish refinement,” said Cameron Carson, VP of Engineering at Hawk Ridge Systems. “PostProcess Technologies provides a comprehensive line of equipment that helps our customers lower the cost of labor and achieve more consistent high-quality results for our 3D printing technologies, including SL (Vat polymerization), MJF (Sintered polymer), and ADAM (Metal) printing. We vet our partnerships very closely for consistent values and quality, and I was impressed with PostProcess Technologies’ reputation for reliability and quality – an ideal partnership to bring solutions to our customers.”
GEFERTEC and Linde Working Together on 3D Printing Research
Near-net-shaped part after 3D printing. [Image: GEFERTEC]
In order to investigate the influence of the process gas and the oxygen percentage on 3DMP technology, which combines arc welding with CAD data of metal parts, GEFERTEC GmbH and Linde AG have entered into a joint research project. The two already work closely together – Linde, which is part of the larger Linde Group, uses its worldwide distribution network to supply process gases for 3D printing (especially DMLS/metal 3D printing/LPBF), while GEFERTEC brings its arc machines, which use wire as the starting material to create near-net-shaped parts in layers; conventional milling can be used later to further machine the part after 3D printing is complete.
The 3D printing for this joint project will take place at fellow research partner Fraunhofer IGCV‘s additive manufacturing laboratory, where GEFERTEC will install one of its 3D printers. The last research partner is MT Aerospace AG, which will perform mechanical tests on the 3D printed parts.
Purdue University and USC Researchers Developing New AI Technology
In another joint project, researchers from Purdue University and the University of Southern California (USC) are working to develop new artificial intelligence technology that could potentially use machine learning to enable aircraft parts to fit together more precisely, which means that assembly time can be reduced. The work speaks to a significant challenge in the current AM industry – individual 3D printed parts need a high level of both precision and reproducibility, and the joint team’s AI technology allows users to run software components in their current local network, exposing an API. Then, the software will use machine learning to analyze the product data and build plans to 3D print the specific parts more accurately.
“We’re really taking a giant leap and working on the future of manufacturing. We have developed automated machine learning technology to help improve additive manufacturing. This kind of innovation is heading on the path to essentially allowing anyone to be a manufacturer,” said Arman Sabbaghi, an assistant professor of statistics in Purdue’s College of Science.
“This has applications for many industries, such as aerospace, where exact geometric dimensions are crucial to ensure reliability and safety. This has been the first time where I’ve been able to see my statistical work really make a difference and it’s the most incredible feeling in the world.”
Both 3D Printing and AI are very “hot” right now. Outside of the hype there are many ways that machine learning could be very beneficial for 3D printing in coming years in part prediction, melt pool monitoring and prediction, fault analysis and in layer QA. Purdue’s technology could be a possible step forward to “Intelligent CAD” that does much of the calculation, analysis and part generation for you.
Finalists Announced for Design for Additive Manufacturing Challenge
[Image: Additive Industries]
Additive Industries has announced the finalists for its Additive World Design for Additive Manufacturing Challenge, a yearly competition where contestants redesign an existing, conventionally manufactured part of a machine or product with 3D printing, taking care to use the technology’s unique design capabilities, like custom elements and thin walls. This year, over 121 students and professionals entered the contest, and three finalists were chosen in each category, with two honorable mentions – the Unibody Hydraulic System by from Italy’s Aidro Hydraulics & 3D Printing and the Contirod-Düse from Nina Uppenkam, SMS Group GmbH – in the professional category.
“The redesigns submitted from all over the world and across different fields like automotive, aerospace, medical, tooling, and high tech, demonstrated how product designs can be improved when the freedom of additive manufacturing is applied,” said Daan Kersten, CEO of Additive Industries. “This year again we saw major focus on the elimination of conventional manufacturing difficulties, minimization of assembly and lowering logistical costs. There are also interesting potential business cases within both categories.”
The finalist designs are listed below, and can be seen in the image above, left to right, top to bottom:
“Cutting dough knife” from Jaap Bulsink, K3D (The Netherlands, professional)
“Cold Finger” from Kartheek Raghu, Wipro3D (India, professional)
“Brake Caliper” from Nanyang Technological University team (Singapore, student)
“Cubesat Propellant Tank” from Abraham Mathew, the McMaster University (Canada, student)
“Twin Spark Connecting Rod” from Obasogie Okpamen, the Landmark University (Nigeria, student)
Marines 3D Printed Repair Part
US Marine Corps Lance Cpl. Tracey Taylor, a computer technician with 7th Communications Battalion, aboard Marine Corps Base Camp Hansen in Okinawa, Japan, is one of the Marines that utilize 3D printing technology to expand capabilities within the unit. [Photo: US Marine Corps Cpl. George Melendez]
To save time by moving past the lengthy requisitioning process, 3D printing was used at Marine Corps Base Hawaii, Kaneohe Bay, to create a repair part that would help fix a critical component to increase unit readiness. This winter, Support Company, Combat Logistics Battalion (CLB) 3 fabricated the part for the Electronic Maintenance (EM) Platoon, 3rd Radion Battalion, and both EM technicians and members of CLB-3 worked together to design, develop, and 3D print the part, then repaired the component, within just one month, after having spent almost a year trying to get around delays to fix it.
US Marine Cpl. Anthony Farrington, designer, CLB-3, said that it took about three hours to design the replacement part prototype, and an average between five to six hours to 3D print it, before it was used to restore the unit to full capability.
“With the use of 3D printing, Marines are empowered to create solutions to immediate and imminent challenges through additive manufacturing innovation,” said subject matter expert US Marine Chief Warrant Officer 3 Waldo Buitrago, CLB-3.
“We need to embrace 3D printing and encourage our Marines to express their creativity, which in turn, could lead to solutions in garrison and combat such as in this case study.”
3D Printed Helicopter Blade Mold
Thermwood and Bell recently worked together to create a 3D printed tool, but not just any 3D printed tool. Thermwood believes that the 3D printed helicopter blade mold is the largest ever 3D printed autoclave-capable tool. Bell, frustrated with expensive tooling that took a long lead time, reached out to Thermwood for help, and the company suggested its LSAM system, with new 60 mm melt core technology. Bell then provided Thermwood with a 20-foot-long, 17-inch-high, 14-inch-wide closed cavity blade mold, and upon receiving both the model and Bell’s tooling requirements, Thermwood began printing the tool with Techmer PM’s 25% carbon fiber reinforced PESU material (formulated specifically for its LSAM additive printing) in a continuous run. The new melt core can achieve a high print rate, even when processing high temperature material, which was great news for Bell.
Glenn Isbell, Vice President of Rapid Prototyping and Manufacturing Innovation at Bell, said, “Thermwood’s aggressive approach to pushing the boundaries and limitations of traditional 3D printing and machining is exactly what we were looking for.”
The final bond tool was able to maintain the vacuum standards required by Bell for autoclave processing right off the printer, without needing a seal coating. Thermwood will soon 3D print the second half of the blade mold, and both teams will complete further testing on PESU 3D printed molds for the purpose of continued innovation.
Discuss these stories and other 3D printing topics at 3DPrintBoard.com or share your thoughts in the Facebook comments below.
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.”
