NEWS
LASERS IN ACTION
IN BRIEF
The University of South Australia is working on technology to underpin the next generation of high-powered lasers in Australia, bringing the country’s laser building capabilities up to speed with other developed nations.
Hamamatsu Photonics has built a pulsed laser system that produces what the firm says is the world’s highest pulse energy among laser- diode-pumped lasers: 250J.
Fraunhofer ILT’s 13th International Laser Technology Congress, AKL’22, is in Aachen on 4 to 6 May next year. The institute’s second AI for Laser Technology Conference takes place online on 28 and 29 September.
BluGlass has demonstrated tunnel junction laser diodes that leverage remote plasma chemical vapour deposition. This could enable higher power and more efficient lasers for additive manufacturing and welding.
Evosys Laser is offering an ‘advanced quasi- simultaneous welding’ plastic joining process. It combines two wavelengths that are alternately guided over the welding zone in a controllable pattern, and improves process times compared to single-laser plastic welding.
4 LASER SYSTEMS EUROPE AUTUMN 2021
Deep learning material sensing platform augments laser cutting
MIT scientists have developed a smart material sensing platform for laser cutters powered by deep learning. The platform could protect
users from hazardous waste, provide material-specific knowledge, suggest subtle cutting adjustments for better results, and allow items to be engraved with multiple materials (such as garments or phone cases). Laser cutters can process a
variety of materials including metals, woods, papers and plastics. However, users can face difficulties distinguishing between stockpiles of visually similar materials, which can result in the wrong material processed. This can cause messes, bad odours and the release of harmful chemicals. The scientists, from
MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), have developed SensiCut, a smart material-sensing platform for laser cutters. Conventional ID approaches
have been known to misidentify materials, while sticker tags (like QR codes) used to identify individual sheets can be accidentally cut off in processing. Also, if an incorrect tag is attached, the laser cutter will assume the wrong material type. But SensiCut uses a more
nuanced fusion. It identifies materials using deep learning and an optical method called
speckle sensing, which uses a laser to sense a surface’s microstructure.
‘By augmenting standard
laser cutters with lens-less image sensors, we can easily identify visually similar materials found in workshops and reduce overall waste,’ said Mustafa Doga Dogan, PhD candidate at MIT CSAIL. ‘We do this by leveraging a material’s micron- level surface structure, which is a unique characteristic even when visually similar to another type. Without that, you’d likely have to make an educated guess on the correct material name from a large database.’ The team trained SensiCut’s deep neural network on images of 30 material types across 38,000 images, where it could differentiate between things like acrylic, foamboard, and styrene, and even provide guidance on power and speed settings. In one experiment, the
team built a face shield, which required distinguishing between transparent materials from a workshop. The user selected
“Augmenting standard laser cutters with lens-less image sensors enables the identification of visually similar materials commonly found in workshops”
The SensiCut smart sensing platform distinguishes between visually similar materials
a design file in the interface and used a pinpoint function to get the laser moving to ID the material type at a point on the sheet. The laser interacted with the very tiny features of the surface and reflected off it, arriving at the pixels of an image sensor to produce a unique 2D image. The system could alert or flag the user that their sheet was polycarbonate, which would release potentially highly toxic flames if cut by a laser. The speckle technique was put in a laser cutter with low- cost, off-the-shelf-components, such as a Raspberry Pi Zero microprocessor board. To make it compact, the team designed and 3D printed a lightweight mechanical housing. They will present the work at the ACM Symposium on User Interface Software and Technology in October. Beyond laser cutters, they
believe SensiCut’s technology could be integrated in other fabrication tools, such as 3D printers. For additional nuances, they plan to extend the system by adding thickness detection.
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WWW.LASERSYSTEMSEUROPE.COM
MIT CSAIL
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