While 3D printing has exploded in popularity, lots of the plastic materials these printers use to create objects can’t be easily recycled. While latest sustainable materials are emerging to be used in 3D printing, they continue to be difficult to adopt because 3D printer settings should be adjusted for every material, a process generally done by hand.
To print a brand new material from scratch, one must typically set as much as 100 parameters in software that controls how the printer will extrude the fabric because it fabricates an object. Commonly used materials, like mass-manufactured polymers, have established sets of parameters that were perfected through tedious, trial-and-error processes.
However the properties of renewable and recyclable materials can fluctuate widely based on their composition, so fixed parameter sets are nearly inconceivable to create. On this case, users must give you all these parameters by hand.
Researchers tackled this problem by developing a 3D printer that may mechanically discover the parameters of an unknown material by itself.
A collaborative team from MIT’s Center for Bits and Atoms (CBA), the U.S. National Institute of Standards and Technology (NIST), and the National Center for Scientific Research in Greece (Demokritos) modified the extruder, the “heart” of a 3D printer, so it could actually measure the forces and flow of a cloth.
These data, gathered through a 20-minute test, are fed right into a mathematical function that’s used to mechanically generate printing parameters. These parameters may be entered into off-the-shelf 3D printing software and used to print with a never-before-seen material.
The mechanically generated parameters can replace about half of the parameters that typically have to be tuned by hand. In a series of test prints with unique materials, including several renewable materials, the researchers showed that their method can consistently produce viable parameters.
This research could help to scale back the environmental impact of additive manufacturing, which generally relies on nonrecyclable polymers and resins derived from fossil fuels.
“On this paper, we reveal a way that may take all these interesting materials which are bio-based and created from various sustainable sources and show that the printer can determine by itself methods to print those materials. The goal is to make 3D printing more sustainable,” says senior creator Neil Gershenfeld, who leads CBA.
His co-authors include first creator Jake Read a graduate student within the CBA who led the printer development; Jonathan Seppala, a chemical engineer within the Materials Science and Engineering Division of NIST; Filippos Tourlomousis, a former CBA postdoc who now heads the Autonomous Science Lab at Demokritos; James Warren, who leads the Materials Genome Program at NIST; and Nicole Bakker, a research assistant at CBA. The research is published within the journal Integrating Materials and Manufacturing Innovation.
Shifting material properties
In fused filament fabrication (FFF), which is usually utilized in rapid prototyping, molten polymers are extruded through a heated nozzle layer-by-layer to construct a component. Software, called a slicer, provides instructions to the machine, however the slicer have to be configured to work with a selected material.
Using renewable or recycled materials in an FFF 3D printer is very difficult because there are such a lot of variables that affect the fabric properties.
As an illustration, a bio-based polymer or resin is perhaps composed of various mixes of plants based on the season. The properties of recycled materials also vary widely based on what is offered to recycle.
“In ‘Back to the Future,’ there may be a ‘Mr. Fusion’ blender where Doc just throws whatever he has into the blender and it really works ]as an influence source for the DeLorean time machine]. That is identical idea here. Ideally, with plastics recycling, you may just shred what you might have and print with it. But, with current feed-forward systems, that will not work because in case your filament changes significantly in the course of the print, all the things would break,” Read says.
To beat these challenges, the researchers developed a 3D printer and workflow to mechanically discover viable process parameters for any unknown material.
They began with a 3D printer their lab had previously developed that may capture data and supply feedback because it operates. The researchers added three instruments to the machine’s extruder that take measurements that are used to calculate parameters.
A load cell measures the pressure being exerted on the printing filament, while a feed rate sensor measures the thickness of the filament and the actual rate at which it’s being fed through the printer.
“This fusion of measurement, modeling, and manufacturing is at the guts of the collaboration between NIST and CBA, as we work develop what we have termed ‘computational metrology,'” says Warren.
These measurements may be used to calculate the 2 most significant, yet difficult to find out, printing parameters: flow rate and temperature. Nearly half of all print settings in standard software are related to those two parameters.
Deriving a dataset
Once that they had the brand new instruments in place, the researchers developed a 20-minute test that generates a series of temperature and pressure readings at different flow rates. Essentially, the test involves setting the print nozzle at its hottest temperature, flowing the fabric through at a hard and fast rate, after which turning the heater off.
“It was really difficult to determine methods to make that test work. Trying to search out the boundaries of the extruder means that you will break the extruder pretty often while you’re testing it. The notion of turning the heater off and just passively taking measurements was the ‘aha’ moment,” says Read.
These data are entered right into a function that mechanically generates real parameters for the fabric and machine configuration, based on relative temperature and pressure inputs. The user can then enter those parameters into 3D printing software and generate instructions for the printer.
In experiments with six different materials, several of which were bio-based, the strategy mechanically generated viable parameters that consistently led to successful prints of a fancy object.
Moving forward, the researchers plan to integrate this process with 3D printing software so parameters don’t should be entered manually. As well as, they need to reinforce their workflow by incorporating a thermodynamic model of the recent end, which is the a part of the printer that melts the filament.
This collaboration is now more broadly developing computational metrology, through which the output of a measurement is a predictive model reasonably than simply a parameter. The researchers shall be applying this in other areas of advanced manufacturing, in addition to in expanding access to metrology.
This research is supported, partly, by the National Institute of Standards and Technology and the Center for Bits and Atoms Consortia.
