New Form of 3D Printing That Using Thermoforming
Not many people realize that that magic maker, Disney, have a whole separate network of research labs that support the company. It’s called Disney Research and has facilities in Los Angeles, Pittsburgh, Boston, and Zurich.
Not many people realize that that magic maker, Disney, have a whole separate network of research labs that support the company. It’s called Disney Research and has facilities in Los Angeles, Pittsburgh, Boston, and Zurich. Supporting The Walt Disney Company, its primary purpose is to pursue new scientific and technological innovation to advance their own company’s entertainment efforts. Just recently, some of its researchers uncovered a new way of creating 3D models using the old method of thermoforming.
The computational thermoforming technique works by first creating a digital image of the product and then using a 3D printer to print a negative mould of the image. Next, the negative mould is filled with a gypsum powder compound and using a vacuum former a layer of plastic coating is shrunk over the top of the item to add a layer of detail. The computational thermoforming process then sets to work creating a 3D plastic copy over the previously printed mould.In a paper published by Disney Research, the authors go into detail about the hardware and software that is used during the process and also goes on to conclude how this method will be a valuable tool moving forward. By using computational thermoforming, manufacturing companies could save a fortune if they switched to this method of making products. Material costs are low, running costs are low, there are low hardware requirements but high fabrication speeds and quality. What more could you ask for?
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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