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The New Collar Network, a non-profit education organization based in Santa Fe, has been awarded a hardware grant for its novel 3D printed early wildfire detection system.
The project idea is one of five winners selected to receive the Knowles AISonic™ Hardware Grant, a competition launched in partnership with engineering education platform Wevolver.
Knowles is a global developer of advanced micro-acoustic and audio processing equipment and will award the five winners with its recently launched Knowles AISonic™ IA8201 Raspberry Pi Development Kit. Additionally, the firm will provide technical support to the winning teams to help develop their project ideas.
“We are particularly proud of this award,” explains Sarah Boisvert, New Collar Network founder and CEO. “Because all three team members are Registered Apprentices in the US Dept. of Labor 3D printing apprenticeship program at our sister company, Fab Lab Hub, LLC. With creativity and innovative problem-solving, these apprentices beat out teams from college and university engineering departments!”
The growing threat of wildfires
According to the National Interagency Fire Center (NIFC), wildfires are becoming more and more prevalent around the world.
In the US in particular, parts of California and the West Coast have recently experienced exponential growth in wildfire counts due to record-breaking temperatures and ongoing drought conditions. Looking at the figures, the past year saw California lose twice as many acres of land to major wildfires as the five-year average.
Beyond just the environmental impacts to local wildlife, wildfires also have major implications for the people living in the affected areas. The latest Verisk estimates claim that there were more than two million Californian properties at high to extreme wildfire risk in 2021. This is the largest number of properties of any US state.
New Collar Network’s 3D printed detection system
To mitigate the growing risk of wildfires, the New Collar Network proposed an innovative new design for an early detection system that relies on acoustic surveillance. The device will feature a 3D printed casing and leverage the Knowles AISonic™ IA8201 Raspberry Pi Development Kit, an all-in-one device that combines voice, audio edge processing, and machine learning listening capabilities in one package.
The proposed design will be able to listen out for fire ignition, reignition, and power line sounds to determine a possible wildfire risk ahead of time. It will also be incorporated with other devices such as field cameras in the forest to aid with reliability. Additionally, the New Collar Network’s early detection system will be able to communicate with a wider network of products to notify emergency services as soon as possible.
To enable the detection system to survive out in the wild, the design team has opted to use 3D printing to fabricate the casing. As such, users will be able to strap the device to trees branches or combine it with a magnet to attach it to metal structures at various points throughout an area of woodland.
If successful, the device is expected to save more acres of forest, give more time for communities to evacuate, decrease firefighter response times, and provide more precise fire location details.
As it stands, the New Collar Network’s early wildfire detection system is only at the design stage. However, with support from Knowles, the team is set to demonstrate a proof-of-concept and develop a prototype in due course.
Additive manufacturing has an ever-growing list of novel applications and environmental monitoring is one of them. Researchers at Santa Clara University have previously used 3D printing to build an upgraded version of the hydration sensing units deployed in agricultural irrigation systems. By redesigning, 3D printing and iterating on parts of these sensors, the engineers have been able to improve their thermal detection capabilities, and shrink their overall size.
More recently, researchers from Trinity College Dublin and the SFI Research Centre for Advanced Materials and BioEngineering Research (AMBER) used 3D printing to develop a novel set of microscopic gas sensors. Designed to mimic the color-changing feathers of a peacock, the 3D printed sensors are capable of changing colors in the presence of certain solvent vapors. As such, they can be used to provide a very visual manner of detecting hazardous pollutants
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Featured image shows The New Collar Network team in the 3D Printing Lab – Filip Perez, Jed Beddo, and Alec Kerr. Photo via New Collar Network.