Service bureaus offer the ability to have prototypes and parts fabricated on professional equipment (especially important as some designers may not have access to any 3D printing resources) and in most cases bring extensive expertise to the table to help with design and manufacturing plans.
The PrintSyst.ai, team, founded in 2017 and headquartered in Israel, understands the benefits and the challenges in offering 3D printing services as the founding brothers—Eitan and Itamar Yona—not only had a lot of work in their beginning stages, but a lot of questions from customers, too. As they began educating their customers further, they also gained a deeper understanding of the processes and continued to learn through their experiences and mistakes.
The 3DP AI-Perfecter dashboard
The results of their work and learning have evolved into an automated workflow system that, according to PrintSyst.ai, “turns 3D service bureaus and manufacturing engineers into instant 3D printing experts.” The 3DP AIPerfecter was developed over the last two years for industrial users involved in 3D printing applications like aerospace, defense, and automotive.
The company suggests that, with this new pre-printing evaluation tool, customers may see a considerable improvement in the quality and strength of their parts while also enjoying faster turnaround in production, greater affordability, and less need for labor. The AI system offers users the ability to analyze parts before printing—an element of the process that is becoming recognized as more critical—and especially in metal 3D printing.
“Analyzing parts before printing is a crucial step that requires a lot of time from highly skilled engineers and bears significant risks to a company’s reputation and ability to meet the desired lead times and regulations,” explained the PrintSyst.ai team in a recent press release.
Without automated analysis, far too many parts result in dysfunction. 3DP AI Perfecter is meant to offer relief for users with automated part analysis which the PrintSyst.ai team claims saves “more than 99 percent of the preparation time and cost.” It was developed with scalability, user-friendliness, and simplicity in mind for customers engaged in complex digital fabrication projects. The AI tool also provides a streamlined dashboard for monitoring the printing process—and can be used to “scale and optimize” operations further. Not only that, but it can also be modified according to the needs of the customer.
Users may save more than 99% of prep time with the 3DP AI-Perfecter
The authors are quite blunt—and accurate—in their abstract, regarding needs in additive manufacturing, stating that “because of its rapid growth, the efficiency and robustness of AM-based product development processes should be improved.”
This point of view is shared by researchers around the world who not only want to stretch the limits of technology like 3D printing, but also ambitiously reach for the rewards of potential, and seemingly infinite, innovations. Such strides are revolutionizing fields like medicine and architecture, as well as bolstering production in applications like automotive and aerospace.
The greatest benefits of 3D printing are undeniably felt within manufacturing, as industrial users realize faster turnaround in production, better affordability, and the ability to make products that would have been impossible with conventional methods. As the researchers explain, however, success is based on user expertise as they engage in product development, to include:
Design
Process planning
Production planning
Process monitoring
Accentuated by other technologies like artificial intelligence, manufacturing processes can be further streamlined with smart agents capable of searching for answers, learning, and acting. In this study, the authors sought to identify what gaps and limitations might exist in using AI, as well as considering future potential for smarter AM, defined as: “a fully integrated, collaborative additive manufacturing system that responds in real time to support ubiquitous and intelligent design, manufacturing and services of 3D printed products.”
The basic structure of intelligent agents
Separated into three types, smart agents are considered to be reflex agents, goal-based agents, and utility-based agents. They may be involved in software or hardware processes, responsible for input and output of commands and files, or they may function as sensors for images, direction, or sound.
The basic structure of intelligent agents
“The layer between the inputs and outputs contains the core functions that form problems and generate solutions. These functions can be based on various structured and unstructured information and knowledge,” explained the researchers.
Each type of AI agent allows users to optimize processes; for instance, knowledge-based agents are able to compile data for users who may be lacking in experience regarding specific production, while goal-based agents may be used in optimization for more expansive design spaces. Current knowledge gaps exist in terms of:
Lack of comprehensive integration of knowledge in applications
Need for improvement in current accuracy and generality of models, especially as a deeper understanding of AM processes is met
Standard databases for the gathering of data
Lack of integration for a variety of models
Application of learning models
An underlying theme in this study is the continued need for high-quality and high-volume knowledge in AM-based processes.
“The agents with searching and planning algorithms usually require a large computational power. In addition, some tasks require a fast response. Learning-based real-time control and monitoring is required for the training and execution stages, respectively,” stated the researchers. “Although cloud-based design and simulation software have been developed, real-time tasks cannot be performed. Therefore, a new efficient computational framework should be studied to fulfil both requirements.”
“Because of deep learning development, AI use has increased in many fields. The unique capabilities of AI have also increased the attention given to the improvement of AM-based product development,” concluded the researchers.
What do you think of this news? Let us know your thoughts! Join the discussion of this and other 3D printing topics at 3DPrintBoard.com.
Stratasys and DSM Venturing (venture capital arm of Royal DSM) lead the way in yet more financing for startups, acting as the major sources of funding support of $12 million total, in an equity round for Inkbit—the 2017 spinout of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. Headquartered in Medford, MA, Inkbit’s technology is based on research led by Professor Wojciech Matusik which has evolved into a vision-based, AI additive manufacturing platform.
“As pioneers of jetting-based additive manufacturing solutions, we are excited to help Inkbit bring their technology to the factory floor. Vision-based feedback control and artificial intelligence will take additive manufacturing to a whole new level and will help to enable its widespread use for production,” said Ronen Lebi, Vice President of Corporate Development at Stratasys.
Up until this point, a lack of technology in the form of inkjet 3D printers has held back many innovators seeking superior performance—and this means improving reliability, accuracy, and the need for planarization. Known as the company that created the first 3D printer offering vision-based feedback control and artificial intelligence, Inkbit technology is unique due to the inclusion of machine vision and machine learning.
The first 3D printer created by Inkbit. (Image: Inkbit)
“We are excited to partner with such an extraordinary team of industry-leading players and impressed by their entrepreneurial spirit and commitment to innovation,” said Davide Marini, Inkbit cofounder and CEO.
“The composition of this syndicate was chosen to maximize the speed of development and commercialization of our platform, with each investor bringing to us their unique expertise in equipment manufacturing, high-performance materials and applications in robotics, medical devices and life sciences tools. Our value proposition to customers is simple: we are adding a layer of machine vision and machine learning to material jetting, increasing its accuracy, reliability and enabling its use with production-grade materials.”
With this latest infusion of cash, Inkbit plans to industrialize their AM system further by:
Integrating multi-material and volume manufacturing requirements
Expanding the set of materials for medical, life sciences, and robotics applications
Installing the first units for customers
Currently the Inkbit team is working with customers like Johnson & Johnson in an early access program, with a release date of 2021 set for select customers.
“Materials always play a major role in industrializing breakthrough technologies and in additive manufacturing they become absolutely critical. We are delighted to have Inkbit in our investment portfolio and look forward to helping them develop the best materials for customers world-wide,” said Pieter Wolters, Managing Director of DSM Venturing.
Inkbit has also been in the news previously for working with companies like Johnson & Johnson, in early access programs, as well as gaining industry respect for their development of artificial intelligence products. What do you think of this news? Let us know your thoughts! Join the discussion of this and other 3D printing topics at 3DPrintBoard.com.
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.
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 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.
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!
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’re seeing an increased focus by software firms in our teeny tiny industry. Simulation, PLM, CAD, the Digital Twin, Industry 4.0 have some very large and influential firms salivating about that peanut butter and jelly sandwich that is the combination of the digital and manufacturing. If we are to grow digital manufacturing and 3D printing then our soft and hard assets will have to be managed and controlled through software. The more conventionally manufactured parts we replace with 3D printing the more files, settings, and process information will have to be monitored and accessed through software packages. This in part explains Dan Wellers’ interest in 3D printing and enthusiasm around the technology. He leads Digital Futures at SAP Global Marketing where in effect he has to be SAP’s Nostradamus and predict the impacts of technologies such as ours.
What is 4D printing?
Building upon existing 3D printing technology, 4D printing uses dynamic materials that perform differently when they encounter changing conditions such as water, light, heat, or electrical current. These materials—hydrogels, shape memory polymers, carbon fiber, custom textile composites, and more – have properties that enable objects to self-assemble, reshape themselves, or otherwise react to changing events or conditions. It’s called 4D printing because it incorporates what’s often referred to as the fourth dimension: time.
Why is it important?
4D printing can expand what is currently achievable in prototyping, design, manufacturing, and post-production adaptability and usage. Examples include: self-flattening boxes to be used in warehouses and logistics companies; plumbing system pipes capable of changing their diameter in response to flow rate or water demand. 4D printing has opened up entirely new innovations, such as medical implants made of dynamic biomaterials, which are already saving lives.
Because of its self-assembling capability, objects too big to be printed via conventional 3D printers can be compressed for printing and then expand after manufacturing. 4D printing could also be used to eliminate the mundane problem of furniture assembly. In addition, researchers have demonstrated how smart materials used in 4D printing can enable an object to “remember” its shape. That capability could be used to flat-pack a self-assembling shelter that springs into place after a natural disaster, or develop bridges and temporary roads made from materials that expand to heal damage and cracks.
How important will 3D printing be?
3D printing represents the digital transformation of both design and production in the manufacturing industry and will have a profound impact on everything from logistics to extended supply chains to trucking. 3D printing eliminates many of the design and production constraints inherent in traditional manufacturing. Product design can now be optimized for customer need and function, instead of production efficiencies.
Over the last few years, 3D printing has advanced in the way it employs different materials—not only plastic, but also metal, resins, sandstone, wax, and ceramics—increasingly incorporating multiple materials at once. These improvements are paving the way for significant benefits, including cost reductions, streamlined supply chains, faster time to market, increased personalization, optimized resource usage, improved prototyping, and the manufacture of new designs not possible in the past.
Commercial applications continually emerge to power 3D printing of everything from everyday household products to customized medical devices and prosthetics to nearly all the components of a house. 3D printing will expand into 4D printing in the coming years by adding a fourth dimension: time. By using specially engineered materials that perform differently when they encounter changing conditions, 4D printing promises to further shift the shape of manufacturing.
How will people and machines work together in the future?
Artificial intelligence is getting better at solving increasingly complex problems. If we want to retain humanity’s value in an increasingly automated world, we need to start recognizing and nurturing skills that are uniquely human.
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.
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dashboard
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.”
