Kitov.ai has been named the winner of the prestigious VISION Award 2022 for its CAD2SCAN software solution. Kitov.ai was among five finalists, selected from a pool of 61 applications who had been named shortlisted finalists for the award.
Kitov.ai was presented with the VISION Award 2022 for its CAD2SCAN software solution. Courtesy of Messe Stuttgart.
Kitov.ai’s CAD2SCAN is a software solution that takes in all available information from a CAD, including geometric and component specifications and specific inspection requirements, to plan the robot inspection, including the best 3D camera and illumination directions for each inspection and the optimal robot path.
“Most critical inspection is still done by humans, and even that which is automated tends to be very rigid and inflexible,” Corey Merchant, Kitov.ai’s vice president for the Americas, explained. “So, the need was recognized to make that a much easier and simpler process to implement.”
Corey Merchant of Kitov.ai (left) and Christoph Hofbeck of RSBG Ventures celebrate Kitov.ai's receipt of the 2022 VISION Award at the VISION trade fair in Stuttgart. Courtesy of Kitov.ai.
The technology was introduced earlier this year as the latest feature in the Kitov Smart Planner toolkit. By combining CAD2SCAN with Kitov’s automated planning and inspection software platform, customers can automate inspection solutions that are too difficult and time-consuming to manually program and deploy. Particularly useful for parts with complex geometries, CAD2SCAN allows quality managers to define their inspection requirements directly on the part CAD model, saving weeks or even months compared to manual programming.
CAD2SCAN technology is implemented as a plugin to common CAD software systems, currently available for SolidWorks and Creo. Additionally, it supports the evolving Quality Information Framework (QIF) ISO standard and can parse visual inspection requirements embedded into it.
Kitov.ai's CAD2SCAN CAD-based automated inspection planning tool aims to simplify the setup of robotic vision inspection systems. Users can define their inspection requirements directly on the model, and the software automatically takes all available information from the CAD and uses it for the planning of the robot inspection. The product earned Kitov.ai the VISION Award 2022, presented Oct. 5. Courtesy of Kitov.ai.
The finalists gathered Oct. 5 to present their innovations in a one-hour Industrial Vision Days session. Brighter AI, Edge Impulse, Kitov.ai, Saccade, and SWIR Vision Systems each presented its respective technologies in front of an audience and panel of expert jurors. Christian Ripperda, managing director of Interroll Innovation and a member of the jury, delivered a congratulatory speech announcing Kitov.ai as the winner.
“It’s a tremendous honor, especially coming from the community,” Merchant told Photonics Media. “It’s a very gratifying validation of what we’re doing. And we’re extremely proud and very impressed with the other four finalists that were named in the contest.”
Past VISION Award winners include Prophesee, for its metavision technology in 2021; Photoneo, for its PhoXi 3D camera in 2018; and Machine Vision Lighting Inc., for its variable irradiation solid angle (VISA)-method lighting in 2016.
Additionally on Oct. 5, VISION celebrated two startup companies. HACARUS, an AI technology company based in Japan, and Covision Quality, an Italian software company focused on visual inspection, earned VISION Start-up of the Day honors.
Kitov.ai was founded in 2014 as a spinoff of RTC Vision, which had been developing advanced computer vision algorithms. The company’s initial product offering, Kitov-CorePlus, a smart inspection planning product, was launched in 2016. The company introduced new options to its CorePlus line of stand-alone vision systems during the past year. Kitov.ai's CorePlus hybrid vision systems, combining deep learning, 3D imaging, and robotic planning in a single platform, aim to provide OEMs with additional flexibility for inspecting products of all types in any production environment.