Hi! I'm Doğa from MIT CSAIL and today I will present SensiCut -- material-aware laser cutting
using speckle imaging and deep learning. First, here's a question, what materials do you see here?
they all look similar but are they of the same type?
They're actually completely different materials as you see here
SensiCut can sense and distinguish between these material types and then
set the correct laser power and speed values for cutting or engraving them
or even warn users if the chosen material is actually hazardous for laser cutting
to clarify our motivation, let me start by explaining the common laser cutting workflow today
when a user wants to laser code a design they first select the material from stockpiles or
scraps in a shared workshop such as this one in our lab where our postdoc Isabel is looking
for a piece of extruded acrylic as you see in this real life example these materials
are often left unlabeled, unfortunately, or may be hard to distinguish for those that look similar
After picking the sheet you would then launch the laser cutter software
and hopefully make an educated guess and select the correct material name from a big database
now if you choose the correct one that is great but in reality, it's tough to get it right the first time
especially if you're not an expert. So what happens if it goes wrong?
Well, the best-case scenario is you haven't applied enough laser power yet, so the outline is not cut through yet
so you may put it back in the laser cutter try to align the sheet at the same position and apply the
laser again. More concerning is that you may have chosen a material like PVC that is not intended
for laser cutting as it releases toxic fumes when lasers apply to it and perhaps the worst scenario
the laser power is too high for the material and the workshop is set on fire as you can imagine
these possibilities not only raise the entry barrier to laser cutting for beginners but
also even for expert users differentiating between visually similar workshop materials you see here
including those that are suitable or unsuitable for laser cutting remains an obstacle
Plus, laser cutter users want smarter machines that can identify the materials they are working with
so that the system can suggest settings based on material as shown in a CHI 2020 study
now that we know the problem let me explain what solutions have been proposed so far
the first solution that comes to mind is adding a camera to the laser cutter
so that it can automatically identify the sheet however a single camera can be easily fooled by
cases like this one where the sheets have the same color but require completely different
laser settings alternatively we could measure the retroreflective properties of different materials
to more accurately distinguish between them but such systems often require bulky hardware
another solution is to attach sticker tags to individual sheets such as this QR code
you see here however during laser cutting if the code is cut off from the main sheet
it cannot be identified in future uses also if an incorrect tag is attached the laser cutter will
assume the wrong material type in short previous work includes naive camera based approaches
that may not be robust or require the use of bulky hardware or cumbersome tag-based approaches
in our work we intend to identify materials by leveraging their inherent properties to overcome
these issues we do this by using an optical method called laser speckle imaging which helps us sense
the surface's microstructure speckle imaging works by pointing a laser onto the material so that the
laser interacts with the very tiny feature of the surface which you see at the bottom when the light
source is on it emits the laser beam marked green here and these rays are reflected off the surface
arriving at the pixels of the image sensor on the top right. Now phase differences between
these rays cause mutual interference at each pixel of the sensor causing either destructive
or constructive interference shown here as dark and bright pixels as a result we get this 2D image
when I was first experimenting with this this technique, I took pictures like this one with
a regular camera which has an optical lens so we see that the green laser appears as a small dot on
the surface in this picture and here is a zoomed in version of the same setup which shows us the
speckle pattern much better however it is still present in only a small portion of the whole photo
this is because the lens of the camera directs not just the laser light but all the available
light rays in the scene onto the image sensor and this gives us less speckle data to work with
I noticed when you remove the lens from the camera though it is only the reflected laser light that
lands on the image sensor thus the resolution of the speckle is as large as the entire image
and this gave us a much clearer high-resolution pattern. Here you see this in action the camera
has no lens so we see nothing in regular lighting but when you turn on the laser pointer we see the
speckles in great detail in short the distribution in these patterns are distinct for each surface
and therefore lets us easily classify the material. To make this more clear I actually
went into a material science imaging lab and used the scanning electron microscope to get these
micron-scale images you see in the middle which really shows how different materials actually are
we use the speckle imaging technique inside a laser cutter for which we have to create a
mountable setup as you see here our imaging add-on can be attached onto laser head very quickly
and it consists of low-cost, off-the-shelf components as you see here to
make it compact we designed and 3D printed a lightweight mechanical housing also gathered
attachment weighs only 114 grams in the future this additional laser pointer may not be necessary
since most cutters commercial cutters already have one which can be repurposed for our imaging method
similarly other components like the battery and the microprocessor can all be fully integrated
in future fabrication tools which already have an energy supply and processing hardware
this makes our method particularly easy to integrate with laser cutters so now that we
know more about this technology, I would like to talk about the user interface we have built for it
and what kind of applications we can use it for in the first example, my lab mate Isabel wants to
rapidly prototype a new earring design and test the look and feel of different materials for it
designers often use material swatches consisting of different samples to get a sense of what
material would work best for a design. In this example Isabel picks four samples from a swatch
that may work well for the earring design she then places these on the laser cutter button
and loads the design file in the UI. Next she chooses the scan multiple sheets option
SensiCut then captures the speckle pattern for each shape and predicts the corresponding
material type using our algorithm you can see these results in the right sidebar where each
color represents a unique material in this case felt, foamboard, cast acrylic, and leather
and we see these shapes on the left are colored according to this detection
after entering the thickness values, Isabel starts the cutting process
and here you see the finished gearing prototypes. In our final use case, I would like to show you an
application very common among leisure cutter users many of us often engrave custom designs such as
text or patterns on our everyday belongings with SensiCut cut we can take this one step further
and engrave objects that consist of different materials across its surface
this phone case looks as if it has leather and wooden parts on its surface however the wood-look
part is actually made out of rubber which may not look obvious in a camera image to detect
these materials we select scan multi-material objects design in this mode sensor automatically
samples points along the shape's outline and captures the speckle patterns at these points
once complete our UI automatically partitions the shapes into material
specific paths and assigns them to cardiac laser settings
and here's the final result.
So let me conclude. Many laser cutting materials look similar and
additionally, many hazardous materials are often confused with safe ones.
SensiCut senses the sheets unique surface structure using speckle imaging and this enables us to select correct laser cutting settings
and new applications such as precisely engraving multi-material objects
now let me zoom out a little bit and talk about what's next for this work on identification and tagging
my bigger vision is to enable users to more and obtrusively identify things in our surroundings so that
they can access and retrieve more information related to them
quickly and seamlessly using physical tags
I distinguish between two types of tags here natural tags and engineer tags
SensiCut is a great example for natural tags which are
basically existing physical features of objects and materials
in comparison engineered texts enable us to define a type of information or pattern we want to embed
on an object during the fabrication process. An example is my CHI 2020 project G-ID,
which uses the subtle patterns left by 3D printers
to distinguish and identify objects that otherwise look similar to the human eye
as future work, I'm planning to build new systems that can leverage
advantages of both methods.
thank you so much for your time and I'm happy to answer your questions!