3D printing methods have opened up an endless array of new possibilities for designers – additive manufacturing eliminates a lot of design and manufacturing constraints that previously limited the design space. However, many engineers are still learning how to best leverage the possibilities of the technology, and this has made collaborating with the manufacturing sector difficult.
Researchers at the University of Huddersfield are creating a new “smart language” that can enable better communication between designers and engineers that are creating objects using 3D printing. The university’s Dr. Qunfen Qi presented a paper on the topic at the 15th CIRP Conference of Computer Aided Tolerancing in Milan, explaining how she is using category theory to create computer-readable guidelines and rules for additive manufacturing.
This language could potentially improve repeatability and part reproducibility, while also lowering production costs.
Qi is a research fellow at the University of Huddersfield’s EPSRC Future Metrology Hub, and her research focuses on the application of mathematics theory and information technology to advanced manufacturing.
“The research can provide a smart language that enables designers, AM engineers and inspectors to truly communicate with each other in an intelligent, robust and productive way,” Qi said. “This will greatly improve the process repeatability and part-to-part reproducibility, and significantly reduce power usage and failure rate, lower the cost of production and make a more environmentally-friendly manufacturing technology.”
This approach was already used in another application, assisting Rolls Royce by creating a smart database that helped designers and manufacturing engineers more easily comply with ISO standards. In that case, the product was reported to perform three times faster than relational databases, and took up just one-third the storage space.
Qi hopes to create a smart language for additive manufacturing that can be used without an in-depth knowledge of category theory, and across multiple types of applications.
“It will have an enormous range of applications, not just limited to advanced manufacturing, but in the future to be applied in smart cities, healthcare, social science and more,” said Professor Paul Scott, research director at the EPSRC Center.
Source: University of Huddersfield