Laser cutters are a simple but powerful tools, yet users often find it difficult to distinguish between visually similar materials. The results of a wrong choice include gooey messes, horrendous odors, and even harmful chemicals.
MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) scientists came up with “SensiCut,” a smart material-sensing platform for laser cutters. SensiCut identifies materials using deep learning combined with an optical method called “speckle sensing,” that uses a laser to sense a surface’s microstructure, enabled by just one image-sensing add-on.
To test their findings, the team trained SensiCut’s deep neural network on images of 30 different material types of over 38,000 images, where it could then differentiate between acrylic, foamboard, and styrene, and even provide further guidance on power and speed settings.
The speckle imaging technique was used inside a laser cutter, with low-cost, off-the shelf-components, like a Raspberry Pi Zero microprocessor board. To make it compact, the team designed and 3-D printed a lightweight mechanical housing. Beyond laser cutters, the team envisions the technology could eventually be integrated into other fabrication tools like 3-D printers.
Original Release: MIT
