The additive micromanufacturing technology (µAM), which has been developed by Exaddon, is based on electrochemical deposition; A small printing nozzle, called an iontip, is immersed in a supporting electrolyte bath. A precisely regulated air-pressure pushes the metal ion containing liquid through a microchannel inside the iontip. The liquid flow is very small and can be as low as femtoliters per second. At the end of the microchannel, the ion containing liquid will be released onto the surface. The dissolved metal ions are then electrodeposited into solid metal atoms. These metal atoms are growing together into small building blocks, so called voxels. Optical force feedback registers the completion of each voxel until all voxels are printed and the complete object is constructed. The electrochemical printing process takes place at room temperature. The process leads to very high-quality metal structures that do not need any post-processing, they are immediately ready for their application. 

The acting forces on the iontip can be measured optically and used as feedback. This allows to detect which voxels of the object have already been printed. This optical force feedback provides real-time process control.

The following video explains the process:

For years, engineers at Lawrence Livermore National Laboratory  have used sensors and imaging techniques to analyze the physics and processes behind metal 3D printing in order to build high-quality metal parts the first time, every time. Now, they are leveraging machine learning to process data obtained during 3D builds in real time, detecting within milliseconds whether a build will be high quality. More precisely, they are developing convolutional neural networks (CNNs), a type of algorithm commonly used to process images and videos, to predict whether a part will be good by looking at as little as 10 milliseconds of video.

Until now, analysis of sensor data taken while 3D printing a metal parts was done after the part was finished and it was expensive. And part quality could only be determined long after, explains principal investigator and LLNL researcher Brian Giera. With parts that take days to weeks to print, CNNs help engineers better understanding the printing process and let them correct or adjust the process in real time if necessary.

For years, engineers at Lawrence Livermore National Laboratory  have used sensors and imaging techniques to analyze the physics and processes behind metal 3D printing in order to build high-quality metal parts the first time, every time. Now, they are leveraging machine learning to process data obtained during 3D builds in real time, detecting within milliseconds whether a build will be high quality. More precisely, they are developing convolutional neural networks (CNNs), a type of algorithm commonly used to process images and videos, to predict whether a part will be good by looking at as little as 10 milliseconds of video.

Until now, analysis of sensor data taken while 3D printing a metal parts was done after the part was finished and it was expensive. And part quality could only be determined long after, explains principal investigator and LLNL researcher Brian Giera. With parts that take days to weeks to print, CNNs help engineers better understanding the printing process and let them correct or adjust the process in real time if necessary.

LLNL researchers developed the neural networks using about 2,000 video clips of melted laser tracks under varying conditions, such as speed or power. They scanned part surfaces with a tool that generated 3D height maps, using that information to train the algorithms to analyze sections of video frames (each section called a convolution). The process is too difficult and time-consuming for humans, according to Giera.

The algorithms that label the height maps of each build then use the same model to predict the build track’s width and standard deviation. As well as whether the track was broken was developed by LLNL researcher Bodi Yuan. Using the algorithms, researchers could video parts being printed and determine if it would have acceptable quality. The neural networks detected whether parts would be continuous with 93% accuracy.

Some researchers at LLNL had spent years collecting various forms of real-time data on the laser powder-bed fusion metal 3D-printing process, including video, optical tomography, and acoustic data. While working with that group to analyze the data, Giera concluded it wouldn’t be possible to do all the data analysis manually and wanted to see if neural networks could simplify the task.

The neural networks they developed could theoretically be used in other 3D printing systems, Giera said. Other researchers should be able to follow the same formula, creating parts under different conditions, collecting video, and scanning them with a height map to generate information that could be used with standard machine-learning techniques.

​HP's multi jet fusion technology produces quality functional parts at a production speed which is up to ten times faster than today's competing products. Unlike the slow point processes of some current technologies, HP's multi jet fusion technology processes parts in two fast area-wide passes to achieve top speed part production. The process begins by applying a layer of material in a work area in the opposite direction in one continuous pass we print fusing and detailing agents across the full working area, this pass combines the printing with the fusing energy and the process is then completed. HP's proprietary architecture is capable of printing 30 million drops per second along every single inch of bed width enabling extreme precision and dimensional accuracy.

To produce truly functional parts, it's important to ensure that the material has been properly fused and that part edges are smooth and well-defined. To achieve part quality at speed, HP invented a multi agent printing process; in this process, a fusing agent is applied on a material layer where the particles are meant to fuse together, a detailing agent is applied to modify fusing and create fine detail and smooth surfaces, the area is exposed to energy and reactions between the agents and the material cause the material to selectively fuse together to form the part. The fusing process requires accurate temperature control across the entire material layer. HP multi jet fusion closed-loop thermal control system does this by measuring temperatures at hundreds of points on the material bed, this information then determines which areas receive more energy to raise the temperature and which areas are cooled allowing for control of thermal bleed and layer to layer fusing and cooling. The result is full control over mechanical properties, dimensional accuracy, and repeatability. The process is then repeated until a complete quality truly functional part has been formed.

The HP multi jet fusion technology achieves new levels of part quality at these breakthrough speeds. In 3D design and printing, a voxel represents a value on a regular grid in a three-dimensional space like a pixel with volume. By controlling the properties of each individual voxel through agents, HP multi jet fusion can produce parts that can't be made by other methods; taking advantage of HP's in-depth knowledge of color science, HP's 3d printers could in the future selectively print a different color at each volumetric pixel, a single 3d printed part could have literally millions of colors but more than just full-color printing of functional parts. HP's multi-agent system enables a fundamentally different approach that could unlock the full potential of 3d printing at each voxel; HP transforming agents could control surface texture wear, and friction enabling single parts with multiple textures or the monitoring of part performance. The transforming agents could control the translucency of each voxel enabling the printing of lenses or sensors. We could also optimize the strength and stiffness in portions of a part and print elastic voxels in other portions of the part. The conductivity of certain voxels could also be controlled enabling embedded electronics. HP transforming agents could also be used to enable the printing of new advanced materials or enable emulating different materials at each voxel. HP's multi jet fusion technology could enable design and manufacturing possibilities that surpass the limits of our imagination.

​Explaining Video for the process:

NanoParticle Jetting technology produces high strength metal parts with the ease and versatility of the Inkjet based additive manufacturing. The technology is based on enabling metal to be deposited in liquid form so it can be jetted from standard inkjet nozzles, to do this, the system uses nano sized metal particles suspended within special liquid formula eliminating inefficient lasers, this metal liquid formula can be jetted from standard printing head. The material are packaged with specially adapted cartridges that are loaded easily by hand into the system.

Because there is no powder VAT, the system uses only the material that you need saving space and eliminating the need to handle metal powders.
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The print heads deposit a fine layer of metal liquid droplets onto the system build tray, with each pass of the print heads the metal part is built up as the tray descends the metal liquid formula which is jetted from many thousands of printing nozzles synchronously in a process that is up to five times faster than laser metal printer. Within the system build enveloped, a temperature of up to 550 degrees Fahrenheit or 300 degrees Celsius cause the liquid jacket around the metal nanoparticles to elaborate allowing the stochastic metal particles to bind strongly and with virtually the same metallic and density as traditional made metal parts. With layer thickness under 2 microns, the result is never before seen levels of detailed surface finish and accuracy with no compromise on speed or build time.

Explaining Video for the process:

A research team has designed a new style of 3D printing which is able to print combined materials using ultrasonic waves. The engineers, based at the University of Bristol, demonstrated the novel method in which ultrasound is used to position millions of microscopic glass fibers into a tiny reinforcement framework. The layer is then placed using a focused laser beam, which cures the epoxy resin and prints the object.

The study explains how the researchers mounted a switchable, focused laser module onto the carriage of a conventional 3D printer, above the new ultrasonic alignment equipment.

In the test, a print speed of 20mm/s was achieved – comparable to the speed of a standard 3D printer. The engineers showed the ability to build a plane of fibers into a reinforcement framework, and precisely orientate the fibers by switching the ultrasonic standing wave pattern during the printing process.

This technique allows for the creation of almost any type, size or shape of fiber, including complex fibrous architectures, such as those required in high-performing products (tennis rackets, golf clubs, aerospace components, and fishing rods etc).

​Short explaining video for the process: