A prototype camera chip that combines a machine-vision-grade image sensor with hyperspectral sensing will go a long way toward integrating spectroscopy into industrial vision applications.
Imec's system-on-chip device puts a set of spectral filters that are directly post-processed at the wafer level on top of a commercially available CMOSIS CMV4000 image sensor. The four-megapixel image sensor has a maximum frame rate of 180fps, or six times the basic rate of industrial machine vision inspection applications.
A prototype camera chip meant for industrial vision applications combines a machine-vision-grade
image sensor with hyperspectral sensing.
Multispectral or hyperspectral cameras combine spectroscopy and imaging to distinguish objects that cannot be identified separately with traditional red-green-blue imaging methods. But this functionality has traditionally been limited to cameras that are large, expensive, and slow, so they can't usually be used for time-critical or high-throughput applications such as high-speed industrial inspection.
In industrial machine vision and inspection, the advantage of gathering spectroscopy data could be applied to objects made of multiple materials that look similar, such as certain types of films and thin layers of materials on printed circuit boards, or products made of multiple metals and different types of composites. Since each material has a unique spectral signature, data can be gathered by the sensor and extracted for further analysis to identify defects in product inspection for quality control applications.
Having seen spectroscopy systems in the semiconductor industry in the 1980s, this seems like about as small a package as I can ever remember. Is this indeed smaller than the current state of the art? Has anyone else used a system on a chip approach like this one, Ann?
There's a large number of apps that could take advantage of this technology. Industrial machine vision and inspection of chips, boards and electronics sub-assemblies, R&D of several different kinds including component failure and analysis labs, medical labs of various kinds, and medical equipment manufacturing. It could possibly also be used in various kinds of materials detection, possibly in security apps, as well as for detecting counterfeit components made of inferior materials.
What I like most about this technology is the huge difference in size between other multispectral cameras I've written about in the past and the fact that this is a chip-level solution, even doing post-processing filters on-chip. I think the need for this technology will only continue to increase as design features keep getting smaller, and with the mixes of multiple material types.
A recent report sponsored by the American Chemistry Council (ACC) focuses on emerging gasification technologies for converting waste into energy and fuel on a large scale and saving it from the landfill. Some of that waste includes non-recycled plastic.
Capping a 30-year quest, GE Aviation has broken ground on the first high-volume factory for producing commercial jet engine components from ceramic matrix composites. The plant will produce high-pressure turbine shrouds for the LEAP Turbofan engine.
Seismic shifts in 3D printing materials include an optimization method that reduces the material needed to print an object by 85 percent, research designed to create new, stronger materials, and a new ASTM standard for their mechanical properties.
A recent study finds that 3D printing is both cheaper and greener than traditional factory-based mass manufacturing and distribution. At least, it's true for making consumer plastic products on open-source, low-cost RepRap printers.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.