2017 3D Printing: stop prototyping, start producing!
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3D Printing: stop prototyping, start producing! Jan Eite Bullema, Senior Scientist, TNO 3D printing is transforming from a prototyping technology into a manufacturing technology. Two important roadblocks in this transformation are (1) the difficulty of designing products suitable for 3D printing and (2) production costs. In my presentation I will show how the issue of product design for 3D printing is addressed using big data and machine learning. To lower production costs faster 3D printing technologies have been developed. In the presentation I will show examples of innovative equipment that TNO has developed to increase the production speed of 3D printing.
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2017 3D Printing: stop prototyping, start producing!
- 1. STOP PROTOTYPING: START PRODUCING! Innovation by TNO and TU/e High Tech Systems Center Jan Eite Bullema, Senior Scientist, TNO
- 2. AM SYSTEMS The AMSYSTEMS Center is a joint innovation center of TNO and the High Tech Systems Center of Eindhoven University of Technology (TU/e HTSC) to accelerate (new ways of) additive manufacturing in diverse industries
- 3. CONTENT - 3D Printing (3DP) from prototyping towards manufacturing - Designing products suitable for 3DP - Reduce production costs by increasing production speed
- 4. PROTOTYPING → MANUFACTURING 3D printing for parts production grew from virtually zero in 2003 to 43% ($1.8B) of global 3D-printed product and service revenue in 2014 In 2015, 3D-printed manufactured goods represented less than 1% of all manufactured products in the U.S. 3D printing represents only 0.04% of the global manufacturing market in 2015 according to Wohler U.S. hearing aid industry converted to ~100% 3D printing in < 500 days
- 5. CONTENT - 3D Printing (3DP) from prototyping towards manufacturing - Designing products suitable for 3DP - Reduce production costs by increasing production speed
- 6. DESIGNING PRODUCTS FOR 3DP - 3DP design for High Tech Systems - Machine Learning for 3DP design
- 7. DESIGNING PRODUCTS FOR 3DP Mechatronics Design Additive Manufacturing Design Freedom Open structures Sophisticated Mechanisms Thermal Solutions
- 8. DESIGNING PRODUCTS FOR 3DP Open structures: Unit cell exploration Sphere OctahedronOctahedral unit cellsSpherical unit cells Gain insight in the open structures best suited for lightweight, and at the same time, stiff components. This has been done using topology optimization
- 9. DESIGNING PRODUCTS FOR 3DP Since the unit cells can be scaled with wall thickness, they therefore tailor the mechanical properties of a cell, a family of different cells with different wall thicknesses can be used Open structures: Unit cell exploration
- 10. DESIGNING PRODUCTS FOR 3DP Thermal Solutions: beyond basic shapes A freeform, yet rather simple idea is to design channels that enforce flow mixing and thus make better use of the cooling fluid flow through the channels
- 11. DESIGNING PRODUCTS FOR 3DP Thermal Solutions: beyond basic shapes Basic idea: the engineering insight that upward flow towards surfaces that require high- performance thermal control should result in enhanced, local cooling performance
- 12. DESIGNING PRODUCTS FOR 3DP Thermal Solutions
- 13. DESIGNING PRODUCTS FOR 3DP Thermal Solutions
- 14. DESIGNING PRODUCTS FOR 3DP - 3DP design for High Tech Systems - Machine Learning for 3DP design
- 15. DESIGNING PRODUCTS FOR 3DP Autodesk: Design Graph, machine learning will transform 3D engineering
- 16. DESIGNING PRODUCTS FOR 3DP Autodesk: Intelligent 3D printing
- 17. DESIGNING PRODUCTS FOR 3DP
- 18. DESIGNING PRODUCTS FOR 3DP
- 19. DESIGNING PRODUCTS FOR 3DP Big Data @ AM: Flawless 3D printing of human spare parts. A finger exercise: using Deep Learning to build a vertebrate classifier
- 20. CONTENT - 3D Printing (3DP) from prototyping towards manufacturing - Designing products suitable for 3DP - Reduce production costs by increasing production speed
- 21. PROTOTYPING → MANUFACTURING Carbon / Carbon 3D introduced CLIP in 2015
- 22. PROTOTYPING → MANUFACTURING Carbon / Carbon 3D introduced CLIP in 2015
- 24. PROTOTYPING → MANUFACTURING April 2017: Adidas announces mass production of 3D printed midsoles using Carbon’s CLIP 3D printing process
- 25. PROTOTYPING → MANUFACTURING STOP PROTOTYPING: START PRODUCING The current goal is to produce 100,000 pairs of Futurecraft 4D shoes by 2018 and then continue to scale up production into the tens of millions.
- 27. 3D PRINTING FOR MICROFLUIDICS
- 28. 3D PRINTING FOR MICROFLUIDICS
- 29. MICROFLUIDIC MANUFACTURING Basic Serpentine mixer design Major challenge in MicroFluidics: from Lab to Fab
- 30. MICROFLUIDIC MANUFACTURING Model of 3D printed serpentine mixer
- 31. MICROFLUIDIC MANUFACTURING 3D printed serpentine mixer, early attempt
- 32. MICROFLUIDIC MANUFACTURING Micro CT scan of the 3D printed serpentine mixer Thanks to Katholieke Universiteit Leuven, Valérie Vancauwenberghe
- 33. MICROFLUIDIC MANUFACTURING LEPUS Next Gen: Fast 3D printing (SLA) equipment
- 34. MICROFLUIDIC MANUFACTURING Improved Light Engine of LEPUS → increased channel accuracy
- 35. MICROFLUIDIC MANUFACTURING Improved Light Engine of LEPUS → increased channel accuracy
- 36. MICROFLUIDIC MANUFACTURING The print area of the LEPUS can be increased to Large Area 3D printing thus increasing the number of products made and reducing cycle time per product Increase throughput by increase of printing area
- 37. PRINTING MAKES SENSE Increase throughput by increase of printing area
- 38. MICROFLUIDIC MANUFACTURING LEPUS cycle time based upon a hypothetical A1 (0.5 m2) print area Increase throughput by increase of printing area
- 39. CONCLUSION - 3D Printing (3DP) from prototyping towards manufacturing - Designing products suitable for 3DP - Reduce production costs by increasing production speed
- 40. THANK YOU FOR YOUR ATTENTION Innovation by TNO and TU/e High Tech Systems Center Explore more on amsystems.com
- 41. MICROFLUIDIC MANUFACTURING Example estimated production cost
- 42. MICROFLUIDIC MANUFACTURING 3D Printed Virtual Impactor for a PM2.5 particle detector
- 43. DESIGNING PRODUCTS FOR 3DP Step 1: Gather scan data of vertebrates A finger exercise: using Deep Learning to build a vertebrate classifier
- 44. DESIGNING PRODUCTS FOR 3DP Step 2: Slice A finger exercise: using Deep Learning to build a vertebrate classifier
- 45. DESIGNING PRODUCTS FOR 3DP Step 3: Vectorise the scan data A finger exercise: using Deep Learning to build a vertebrate classifier
- 46. DESIGNING PRODUCTS FOR 3DP Step 4: Define a Deep Learning Model A finger exercise: using Deep Learning to build a vertebrate classifier
- 47. DESIGNING PRODUCTS FOR 3DP Step 5: Train the Deep Learning model A finger exercise: using Deep Learning to build a vertebrate classifier
- 48. DESIGNING PRODUCTS FOR 3DP print(h2o.confusionMatrix(spine_disk_class_deep, valid)) Confusion Matrix (vertical: actual; across: predicted) for max f1 @ threshold = 0.0175062986238944: 0 1 Error Rate 0 10 1 0.090909 =1/11 1 1 7 0.125000 =1/8 Totals 11 8 0.105263 =2/19 Step 6: Look at the model Performance , if not OK back to prior steps A finger exercise: using Deep Learning to build a vertebrate classifier
- 49. DESIGNING PRODUCTS FOR 3DP Next Step: Build a regression model to create designs for vertebrate bones A finger exercise: using Deep Learning to build a vertebrate classifier
Editor's Notes
- 3D Printing: stop prototyping, start producing! Jan Eite Bullema, Senior Scientist, TNO 3D printing is transforming from a prototyping technology into a manufacturing technology. Two important roadblocks in this transformation are (1) the difficulty of designing products suitable for 3D printing and (2) production costs. In my presentation I will show how the issue of product design for 3D printing is addressed using big data and machine learning. To lower production costs faster 3D printing technologies have been developed. In the presentation I will show examples of innovative equipment that TNO has developed to increase the production speed of 3D printing.
- TNO cooperates with the HTSC of the TU/e in the AMSYSTEMS Center with the objective to accelerate new ways of additive manufacturing in diverse industries.
- In my presentation I will address these three topics, first very briefly the trend that additive manufacturing is becoming a manufacturing technology. To be able to use additive manufacturing for manufacturing there are two bottle necks that have to be addressed. One bottle neck is designing for 3D printing, the other are the relative slow production speeds that make use of 3D printing for mass manufacturing cost prohibitive.
- So very briefly some facts on 3D printing. That show the current situation and current trends. I took this figures from a recent study by UPS. 3D printing is transforming from a prototyping technology into a manufacturing technology. Two important roadblocks in this transformation: 3D printing for parts production grew from virtually zero in 2003 to 43% ($1.8B) of global 3D-printed product and service revenue in 2014. In 2015, 3D-printed manufactured goods represented less than 1% of all manufactured products in the U.S. 3D printing represents only 0.04% of the global manufacturing market in 2015 according to Wohler. Wohler’s and Associates believes 3D printing will eventually capture 5% of the global manufacturing capacity, which would make 3D printing a $640 billion industry. This is a market ripe for disruption. Technology adopters that move beyond prototyping to use 3D printing in supporting and streamlining production can achieve new manufacturing efficiencies. Plus, there is an enormous opportunity for companies that get it right.
- So the design bottle neck. 3DP comes with an enormous design freedom, and that also makes it difficult – in combination with the fact that most designers have limited experience with designing for 3DP.
- For Design I want to go into two topics, first the work we did at TNO in a TKI project together with industry were TNO developed systematic approaches for designing for 3D printing. And I want to go into use of machine learning for designing 3D printing – I will show some development that I think are interesting and give an example of how one can apply machine learning. First I show you this design with we won the GE design challenge tworyears ago: it is a 3D printed fluid connector, I piece integrated seal. Price was 20,000 USD. The design is based upon brain-storming.
- TNO has done a TKI project were AM was explored. If you want to achieve maximum benefit from AM start from functional requirements at system level, not merely try to develop a lightweight component. A second insight was that from topology optimization often amazing new design come forward that improve overall performance. === Typical system requirements for many precision equipment applications are stated in terms of extremely accurate motion and positioning of a substrate, combined with substrate (thermal) stability within stringent limits. Known bottlenecks in such motion systems concentrate around moving mass, thermal non-uniformity and system complexity. This article presents initial attempts to answer the question as to how AM-enabled freeform design can provide precision mechatronics solutions to such bottlenecks, and pave the way to breakthroughs in system performance. This work was done by TNO colleagues: GREGOR VAN BAARS, JEROEN SMELTINK, JOHN VAN DER WERFF, MAURICE LIMPENS, MARCO BARINK, JAN DE VREUGD, OLEKSIY GALAKTIONOV AND GERT WITVOET
- Unit cell exploration: The question arises as to which open structure is best suited in the search for lightweight, and at the same time, stiff components. To gain insight, unit cells as building blocks. In literature many structures (mostly 2D patterns) can be found, each with specific benefits. In return, such structures suffer from weak points when loaded otherwise than in the optimised case. For example, some structures are very stiff when compressed, but perform poorly when subjected to shear forces. This is obviously unacceptable in many high-tech system applications. The following starting points were taken into account in the unit cell exploration: • 3D structure as building blocks, with repeatable geometry to be able to build 3D objects. • Unit cell geometry should be AM-printable without internal supports, and powder must be removable after build. • Primary target is mass reduction (per unit volume) without sacrificing mechanical properties. Scalable mechanical properties are desired, for example, through varying wall thickness within the unit cell structure. • Analysis on strength, stiffness and isotropy under various load cases. Since the unit cells can be scaled with wall thickness, and they therefore tailor the mechanical properties of a cell, it is proposed to offer a family of different cells with different wall thicknesses (e.g. thin, medium, and thick) in addition to the regular choices of void or solid
- Since the unit cells can be scaled with wall thickness, and they therefore tailor the mechanical properties of a cell, it is proposed to offer a family of different cells with different wall thicknesses (e.g. thin, medium, and thick) in addition to the regular choices of void or solid Now, a component design volume can be gridded, in accordance with unit cell dimensions, and the idea is to let the topology optimisation algorithm decide which type of cell to place at each grid position, such that the resulting component properties are optimal with respect to the optimisation targets and boundary conditions. This way, the probability of ending up in local minima is reduced; mainly due to a limited optimisation scale (the number of unit cells to be placed is by far less than with regular topology optimisation over much finer grids). This idea has been tested in a simplified case, as depicted
- Starting from existing engineering reality, the conventional cooling channels exhibit more basic shapes, mainly determined by design-for-manufacturing constraints. A freeform, yet rather simple idea is to design channels that enforce flow mixing and thus make better use of the cooling fluid flow through the channels. A kind of corkscrew-shaped channel is shown which could be manufactured with AM. Because of the spiralling shape, the flow is mixed internally along the main microchannel direction (going from left to right, the initially separated blue and red flow volumes are clearly being mixed). From preliminary flow and thermal analysis we observed that preheated water is continuously refreshed with initially cooler water in the stream, whilst preserving laminar flow. The results indicate that heat transfer can be significantly improved (even to values comparable with turbulent flow), depending on the flow velocity through the channel (Reynolds number). Additional pressure drop along the channel appeared limited.
- The next freeform idea also did not originate from optimisation, but from the engineering insight that upward flow towards surfaces that require high-performance thermal control should result in enhanced, local cooling performance. The elementary principle is illustrated in Figure Left . This was translated into various freeform design concepts; see for example Figures Middle and Right. A top surface can be obtained from repeating the elementary unit (which we call a thermal pixel). One way of organising cooling flow for each individual thermal pixel is via an orthogonal set of parallel supply and return pipes. Currently this idea is under development, so design changesand refinements can be expected. Especially the potential of dedicated, local cooling flow control can be very interesting in dealing with dynamic thermal loads that only affect part of a substrate area. This requires active control of cooling flows at each individual pixel of course. Studies are currently being conducted to find the best way of steering separate supply and return channels.
- So you can see here a prototype of the thermal pixel table werr individual pixels can be cooled. It is a mock up made in mylon and then painted to look like a metal pixel table The result will be that each pixel has its own cooling characteristic
- Eventually we printed this pixel table in metal to demonstrate that it is also manufacturable in metal with AM. So with this pixel table we wen much further than was shown in the Mikroniek of 2014 were initial results of 3D Design for High Tech Systems were shown.
- So Machine Learning and 3DP. Last year I did a short study of a few weeks to look into this topic. As I have picked up an interest in ML. The next big thing – according to some.
- https://www.themanufacturer.com/wp-content/uploads/2015/12/Slide118.jpg Mike Haley of Autodesk demonstrates how Autodesk uses Big Data (Hadoop) and Machine Learning to automate product design. Designing systems with minimal human intervention. Autodesk used 3 Million CAD files to train a network that can identify parts and aggregate this parts to subsystems and systems. The end goal appears to be defining user requirements and from these design the product most fitting to the customers’ requirements https://www.autodeskresearch.com/groups/machine-intelligence https://blog.a360.autodesk.com/design-graph-machine-learning-for-3d-engineering/ Design Graph is a powerful new Machine Learning system that uses algorithms to extract large amounts of rich 3D design data. It then categorizes every single component and design your design team has ever created, by classification and relationship, to create a living catalog that is able to react to a constantly-evolving world and guide your designs of the future
- Autodesk describes in several patents how Machine Learning can be made useful for 3D printing. In some cases in combination with Process Models can be used to improve control of 3D printing. Autodesk thinks to make 3D printing more accessible for companies that are lack sufficient knowledge for application of 3D printing. In an Autodesk patent, “Intelligent 3D printing through optimization of 3D print parameters” (US2015/0331402 A1), Karl Willis (currently of Voxel8) claims that the algorithms will provides: support generation and fault analysis with one or more machine learning algorithms that link with specific 3D geometries, 3D print profiles materials or applications. In another Autodesk patent (Dynamic Real Time Slice Engine for 3D Printing (US2015/032889 A1), process conditions per slice are adjusted during the print process. Machine learning typically can lead to completely automated – per slice- process control
- Prof Tanaka of KEIO University: “Deep learning for Advanced 3D Printing”. In this work they collected 1,000,000 STL files from the Internet and trained an algorithm that can produce a 3D voxel file from a new 2D profile. Tanaka described 3D Printing as an Iceberg. Data is the large unseen digital part of 3D printing. 3D Printing can be understood as an iceberg. Visible parts of 3D print technology (above the water) are about materials, machines and processes. While invisible parts of 3D printing (beneath the water) are about 3D modelling, data processing, algorithms and Artificial Intelligence. The visible part is in the physical world, the invisible part is in the digital world.” - According to Tanaka there are two streams beneath the water: (1) Sophisticated software with new user interfaces, approaches like “Computational Design” or “Algorithmic Design” sometimes “Biomimetic” - and (2) “Big Data” based approaches, were machine learning is used to improve 3D design.
- Based upon this model he model can generate 3D Voxels from a 2D sketch. One wonders, still sketchy. The 2D image is pulled through an encoder (2D image) - decode (3D image) algorithm to generate a 3D voxel pattern Our prototype system is available at: http://fab3d.cc
- I proposed this project within TNO, unfortunately it did not receive funding. Still it is relative easy – given one can obtain the data - Big Data @ AM: Flawless 3D printing of human spare parts. A problem with printing implants like knee joints and hip implants, is that – ideally- each product has to be made patient specific. Less than perfect designed and produced implants lead to (i) increased wear, (ii) patient discomfort and pain and (iii) the need for additional surgery. Current situation is that many implants are not up to quality. Application of Big Data technology will lead to dramatic improvement of this situation. Big Data gathered from 3D print files, 3D printers, patients and doctors, will be used for training 3D print design algorithms for flawless 3D printing of implants.
- So there are design approached for 3D printing. Having data and using data is clearly an issue. So an second roadblock that I want to discuss is the production cost of 3D printing, And for that I want to zoom in on production speeds as a dricver for 3D printing production costs.
- First I want to introduce to you the CLIP process that has been presented two years ago by DeSimone from Carbon 3D – now called Carbon. On this animation you see the build up of a product using CLIP Continuous Liquid Interface Production (CLIP; originally Continuous Liquid Interphase Printing) is a proprietary method of 3D printing that uses photo polymerization to create smooth-sided solid objects of a wide variety of shapes using resins. The continuous process begins with a pool of liquid photopolymer resin. Part of the pool bottom is transparent to ultraviolet light (the "window"). An ultraviolet light beam shines through the window, illuminating the precise cross-section of the object. The light causes the resin to solidify. The object rises slowly enough to allow resin to flow under and maintain contact with the bottom of the object.[1] An oxygen-permeable membrane lies below the resin, which creates a “dead zone” (persistent liquid interface) preventing the resin from attaching to the window (photopolymerization is inhibited between the window and the polymerizer).[2] Unlike stereolithography, the printing process is continuous. The inventors claim that it can create objects up to 100 times faster than commercial three dimensional (3D) printing methods
- In this movie you can see how the CLIP process works continuously (the movie is sped up 7 times) There are some other companies that also use oxygen inhibition to increase the VAT printing speed. In deze link staan drie filmpjes met nexa3d, newpro3d en carbon. Geen real time video van carbon, ik heb wel ergens gelezen dat die er moet zijn. https://3dprint.com/108599/patent-nexa3d-newpro3d/ Links naar newpro3d met fotos van producten: http://newpro3d.com/ (Worlds fastest printer) Twee patenten (Denaro: Nexa3D technologie, Castanon: NewPro3D) Ik denk dat hun patenten elkaar niet in de weg zitten, voorzover ik dat kan beoordelen. In mijn ogen gebruiken ze verschillende manieren van zuurstofinhibitie. Carbon has investors from Google/Alphabeth, BMW, GE for over 200 Million USD. https://www.forbes.com/forbes/welcome/?toURL=https://www.forbes.com/sites/aarontilley/2016/09/15/carbon-bmw-ge-80-million/&refURL=https://www.google.nl/&referrer=https://www.google.nl/ https://equityzen.com/trending/carbon3d/
- Figure after https://3dprinting.com/news/carbon3d-reaches-incredible-3d-printing-speeds-with-clip/ If one compares printing speeds, printing a 50 mm high sphere can be printed in 6.5 minutes with the Carbon process. Were the traditional VAT process will take 11.5 hours. SLS and polyjet need 3 to 3.5 hours for the 50 mm sphere Newpro3D claims a little faster process with the Intelligent Liquid Interface process. That also works with oxygen – at the polymer interface- inhibition but not with a membrane but a chemical active layer
- Dan Howarth | 10 April 2017 4 comments Sports brand Adidas has unveiled designs for trainers with latticed plastic midsoles, which are shaped using a new additive-manufacturing technique that "overcomes shortcomings" of 3D printing. The soles of the Futurecraft 4D running shoes are formed by a process called Digital Light Synthesis, developed by Silicon Valley tech firm Carbon. ## Just in the weeks before this announcement I made some cost calculations for using tha Carbon process, and thought it was too expensive due to my estimates of the material prices. Carbon just announced this week that they will bring down material prices to below 100 USD. The y currently have about seven material with properties ranging from elastomeric to more stiff materials
- The title of my presentation actually fcomes from the Carbon website: stop prototyping start producing. Carbon is planning together with Adidas to mass produce –eventually – custom midsoles in large quantities. https://3dprint.com/170425/adidas-carbon-3d-printed-shoe/
- With a nice animation showing how carbon wants to address the million soles challenge
- Carbon also prints fluidic devices, the y claim that their Cyanate Ester material withstands high temperatures 230 degrees Celsius and has also chemical resitance. Case from the carbon website https://www.carbon3d.com/stories/carbon-uses-clip-technology-to-optimize-fluid-manifold-designs/ Engineers at the manifold company began with the part’s material: the manifolds need specific chemical resistance, lifetime material stability, and isotropic properties. Without these attributes, design and scalability of an additive process would not matter. Carbon’s Cyanate Ester was the first additive material and process to surpass these requirements with a heat deflection temperature of 230°C, long-term thermal stability, and the necessary chemical resistance.
- In the ENIAC project Micro Fluidic Manufacturing (MFM) TNO has developed 3DP for micriofluidics. Some examples are given on this slide. Printed with the BIOC material that TNO has developed for biocompatible 3D printing. Top left: some villi that we printed for organ o a chip – in this case gut on a chip together with the colleagues from TNO Zeist. We demonstrated the our printing material was biocompatible- and that gut-cells (CoCa2) showed differentiation based upon the topology. Top right: an example of a 3D printed vascular system, that van be used for medical experiments (e.g. thrombosis on a chip or Alzheimer on a chip) Bottom left: a first prototype for cancer on a chip research that we made in cooperation with Jaap den Toonder Bottom right:3D printed example of some functional channels, a tesla valve and a serpentine mixer in a TNO logo (colored with a dye)
- An important challenge in Microfluidics is the the Lab to Fab issue. Many working devices heve been developed, but it appears very challenging to develop effective manufacturing processes. Important cause is that many development work is done in PDMS technology which is very affordable, but PDMS technology is difficult to scale into production. In the MFM project we have tried to address the issue of manufacturability, by developing standards and design tools, but also by developing useful technology. That is printing technology. Dolomite / Blacktrace developed the fluid factory a FDM printer that can be used for prototyping purposes. At TNO we developed further on our TNO LEPUS platform. To show what we have worked on I want to go a little more in detail on the serpentine mixer that we developed for Philips (Handheld Diagnostics) a partner in the MFM) project. First the basic design of a serpentine mixer – the mixer channel goes out of plane – would be very difficult to make by injection molding from one piece
- So we dis design studies with computational fluid dynamics to gain insights in the desired dimensions for optimal mixing
- Then we started with printing in TNO’s own developed BIOC resin. Here an initial experiments with rather larger channels – we wanted to go into 100 micron diameters, Clearing of a channel proved to be a challenge – the viscosity of the BIOC is about 200 mPa.s – due to hydrodynamic resistance it proves to be difficult to clear the channels completely. So we also varied the material composition to reduce viscosity and played with the photoinnitiator and UV absorbers in the material. Without UV absorbers you will also cure uncured material in a previous layer. You can work with greyscales during lighting, adapting bitmaps Eventually you will obtain sharper features.
- We had contact with the KU Leuven in another EU project and they did some non destructive analysis of the 3D printed micromixers. A very nice result. Later we learned that TU/e also has micro CT facilities that are accessible for us.
- TNO has developed the LEPUS machines for 3D printing – the LEPUS from rabbit in Latin. It is a fast 3D printer. The LEPUS is an SLA machine. And as you can see in the movie. It does not use the traditional DLP light source but a low-cost light engine that moves over het resin. Resin replenishment is done by a re-coater that supplies a new layer of resin. (The first generation LEPUS was a modification of traditional VAT polymerization machine and used force control during substrate movement, leading to a ten time increase of printing speed) (The second generation used a moving light engine and was capable of printing a cubic meter. This third generation machine s the latest development what is shown here development machine – eventually the LEPUS Next gen should be able to print printing areas of a square meter.
- The light engine involving 405 nm diodes (low cost) and a rotating polygon delivers a high quality pattern. To the left prints made with a commercial VAT printer, in the middle the desired design and to the left – if you look closely you can see the channels the result with the LEPUS
- Channel diameters in this channels are meant to be 150 micron with a tolerance of plus or minus twenty micron. You see that there is some systematic off set but that the mean channel diameter is 143,7 micron with a standard deviation of 5 microns. Further optimization still possible require(SixSigma) channel.diameter <- c(149,155,147,155,143,146,141,143,142,144,142,139,142,136,142,146,141,138,141) ss.study.ca (channel.diameter, LSL = 130, USL = 170, Target = 150, alpha = 0.05, f.su = "Micro Channel")
- If you would build a machine with a larger printing area – one can reduce the cycle time per product. For a printing area of 210 x 297 mm2 typically the cycle time is 30 seconds for this well-plate product ( several wells with embedded channels) Scaling up towards A1 size, about 0.5 square meter printing surface would reduce the cycle time for this well plate to about 4 seconds
- In this movie I show how multiple serpentine mixer are printed in the current LEPUS next configuration
- Comparing the hypothetical large area printer to other printing techniques show that LEPUS NEXT potentially is a winner in respect to cycle time (throughput) for such an application. I showed this graph at an MFM meeting and the remark was, is there enough demand already for that amount of well plates. Off course does not have to be – 3D printing has the advantage that you can print other products. But defining an optimum configuration is a matter of System Engineering balancing cost of equipment, demand ands so on. I want to point out the possibility to mass produce these type of products with 3 Printing. (one would need 8 – 10 Carbon machines to keep up with this production speed)
- So this brings me to the end of my presentation 3D printing is transforming from a prototyping technology into a manufacturing technology. A discussed two important roadblocks in this transformation: - Design for 3D printing, I showed work that TNO has done in this field, the repeating unit cells, and showed how Machine learning is used for design purposes - How printing costs can be reduced by increasing printing speed and showed the example of carbon 3d and the Lepus Next that TNO has developed
- Thank you for your attention
- As additional slide I made an example calculation for a price estimate of a well plate produced with the LEPUS Next Gen for different configurations based upon some estimates on equipment process material cost and overall equipment effectiveness. Production cast would lie between somewhere about 50 and 90 cent per product. Notice: it is an example not a quotation
- This is another example of a 3D printed device, it is a so-called virtual impactor that is designed to separate small particles so-called PM2.5 (particles with a diameter of 2.5 micron from an air flow) Our ambition is to ingetrate a (capacitive) sensor in this device, so that we create a low cost < 10 EURO per piece 3D printed particle detector / fijnstof detector
- 3D printing is transforming from a prototyping technology into a manufacturing technology. Two important roadblocks in this transformation: 3D printing for parts production grew from virtually zero in 2003 to 43% ($1.8B) of global 3D-printed product and service revenue in 2014. In 2015, 3D-printed manufactured goods represented less than 1% of all manufactured products in the U.S. 3D printing represents only 0.04% of the global manufacturing market in 2015 according to Wohler
- I made a finger exercise for this, I used 3D data from a spinal column that I found on the thingiverse. From the 3D data, in this case it were already STL files, but could have been other forms of 3D data like point clouds or CAD files For Deep Learniong purposes one would need to fill a big data repository using Hadoop, Cassandra, Spark or other Big Data environments In my finger exercise I only had this one spinal colum – just to show some steps one can make
- Several ways leas to Rome, but I chose to slice the 3D bones and then subsequently vectorise the data
- From slices to vectors, leading to long vectors (depending on the resolution one chooses and the number of slices in this case) I labelled the slices disk ort non disk, for classification purposes. If I had modre scan data from more spinal columns, I coul have labllede these with more detailed bone names, like A1 Atlas, and so on
- Once the data is labelled and vectorised one can apply Deep Learning – in this case I used the open source h2o.ai package in R. Python also has several packages supporting Deep Learning, Tensorflow, Keras, Theano, all work more or less the same, but in detail support different Deep Learning approaches to a defiirent level. H2o only suppota RBM and auto encoders, Theano supports CNN and I think also RNN Basically if you are able to vectorise your data you are in business with most Deep Learning packages.
- The data is split in a training, validation and test set, The training set is used to train the model , validation can be used to tune the model and the test set is used for evaluation the prediction accuracy of a model. Normally a ttest set is only used to test a model, sio you get an insight of how well the model performs on data that it never has seen. A problem wih mnearl nwtworks is that they can overfit A learning criterion is used to evaluate the performance of the model.
- In this cse for the example a classification model was trained. And even the model was trainee on very limited data set, the model is able to classify 17 out of 19 correct. More data and more sophisiticated Deep Learning models will improve this. I just show this to demonstrate how relative easy it is to use these techniques for 3D printing
- At the end one wanted to generate printfiles for the creation of custom made implants – so also including print process parameters and patient relevant information