With the addition of computers, laser cutters have rapidly become a relatively simple and powerful tool, with software controlling machinery that can chop metals, woods, papers, and plastics. But users still face difficulties distinguishing among stockpiles of visually similar materials.

A team has created SensiCut , a smart material-sensing platform for laser cutters. In contrast to conventional, camera-based approaches that can easily misidentify materials, SensiCut identifies materials using deep learning and an optical method called speckle sensing, a technique that uses a laser to sense a surface’s microstructure, enabled by just one image-sensing add-on.

SensiCut could potentially protect users from hazardous waste, provide material-specific knowledge, suggest subtle cutting adjustments for better results, and even engrave various items like garments or phone cases that consist of multiple materials. The system leverages a material’s micron-level surface structure, a unique characteristic even when visually similar to another type. Without it, an educated guess would have to be made on the correct material name from a large database.

Beyond using cameras, sticker tags (like QR codes) have also been used on individual sheets to identify them. However, during laser cutting, if the code is cut off from the main sheet, it can’t be identified for future uses. Also, if an incorrect tag is attached, the laser cutter will assume the wrong material type. The team trained SensiCut’s deep neural network on images of 30 different material types of more than 38,000 images, where it could then differentiate among things like acrylic, foamboard, and styrene and even provide further guidance on power and speed settings.

In one experiment, the team built a faceshield, which would require distinguishing between transparent materials from a workshop. The user would first select a design file in the interface and then use the “pinpoint” function to get the laser moving to identify the material type at a point on the sheet. The laser interacts with the very tiny features of the surface and the rays are reflected off it, arriving at the pixels of the image sensor and producing a unique 2D image. The system could then alert or flag the user that their sheet is polycarbonate, which means potentially highly toxic flames if cut by a laser.

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 3D-printed a lightweight mechanical housing.

Beyond laser cutters, the team envisions that SensiCut’s sensing technology could eventually be integrated into other fabrication tools like 3D printers. They also plan to extend the system by adding thickness detection, a pertinent variable in material makeup.

For more information, contact Rachel Gordon at This email address is being protected from spambots. You need JavaScript enabled to view it.; 617-258-0675.