Design tool enables 3D printing of dynamic, flexible objects
StaffAdditive Manufacturing 3D printing Disney Research
The objects are able to sense when they are being bent due to added piezoresistive materials.
With the expansion of 3D printing capabilities, more and more teams are developing new and unique ways to use the technology. 3D printing bendable objects with customized appearance and shape is currently possible. However, a collaboration between Disney Research and ETH Zurich have taken this one step further.
The team, which also includes collaborators from McGill University and IST Austria, has developed a new design tool, called DefSense, which enables designers to give objects deformation-sensing functionality, even if the designers lack expertise in this complex task. This means that objects can sense when they are being deformed, making it easier to control games, provide feedback for toys or otherwise provide input to a computer.
“3D-printed objects that can sense their own deformation will open the door to a range of exciting applications, such as personalized toys, custom game controllers and electronic musical instruments,” said Markus Gross, vice president at Disney Research.
Piezoresistive materials were introduced into the team’s construction, allowing the objects to sense when they are bent. The electrical resistivity of these wires changes when they are bent, so it’s possible to infer the amount of deformation based on changes in measured resistivity.
But ensuring that the changes in resistivity correctly reflect the amount and type of deformation—whether the object is being twisted, stretched or bent—depends on proper placement of the sensor wires within the object, said Moritz Bächer, research scientist at Disney Research.
DefSense not only allows users to create the shape of the object, but specifies the deformations that need to be sensed. An optimization algorithm then computes sensor layouts based on those example deformations and iteratively guides the designer in placing the wires within the object. The algorithm optimizes the sensor layout to minimize reconstruction error.
The objects are produced in the 3D printer in layers, so that the prefabricated piezoresistant wires can be positioned between them. After fabrication, a machine learning algorithm recovers 3D deformations in real time, despite material imperfections or modeling errors. The deformation signals can be used to control a virtual version of the object or to provide any sort of control that the user wishes.