The Manufacturing Executive · 27 Apr 2021 · 24m · Episode 46
Jonathan Berte, Founder of RoboVision, explains how his company uses AI-driven machine vision to solve the labor shortage and complexity challenges in modern manufacturing. He details the shift from manual algorithm creation to automated deep learning, enabling scalable quality control and object detection for clients like Audi. Berte also discusses the strategic trade-offs between cloud and edge computing for industrial applications and the role of flexible robotics in replacing tedious human tasks.
Where this conversation describes AI being used to build or deliver a product. Part of the AI in practice tracker.
Quality & testRoboVision AI softwareprevents wasting hundreds of meters of textile
The AI automatically detects anomalies in woven textile, such as broken needles, to stop production and prevent wasting hundreds of meters of material.
“It's like to detect these anomalies in woven textile, and as such, stop the production process if a needle breaks, so not to waste any more material.”
Jonathan Berte · Hear it at 9:33
Quality & testRoboVision AI software
The AI checks if high-tech glue has been applied correctly to car parts at the Audi factory, automating a visual quality check that previously required manual engineering.
“For instance, a glue process, which we did for the Audi factory in Brussels, like many parts of a car being glued with high-tech glues, but then, of course, you need to check if the glue has been put well on the material and this kind of processes can be automated with AI in a visual way…”
Jonathan Berte · Hear it at 10:30
Engineering & simulationRoboVision platform
The platform automates the creation of visual algorithms, removing the need for PhD-level engineers to manually engineer background subtraction and feature detection.
“We had a, I mean, designing a platform that made algorithm creation in the visual domain automatic. And that's, yeah, that's the basis of the company, basically. Like you can create algorithms. whether it's quality control in the industry or COVID detection in healthcare. You can automate this process, which is the biggest disruption of all, Joe.”
Jonathan Berte · Hear it at 3:01
Engineering & simulation
Deep learning automated the feature detection process that previously required manual engineering by PhD-level specialists, commoditizing difficult visual algorithms.
“And it's, of course, a big game changer because those are the most expensive profiles in the industry is the algorithm creators. So if you can just automate the smartest people, all of a sudden you can just do many more use cases and make many more algorithms and that's the era we live in.”
Jonathan Berte · Hear it at 7:19
companies scaling up
Other shows on the same ground, closest first.
Host Chris Luecke interviews Laura Hoffman, Patty Ketseris of Locust Robotics, Matt Charles of Robex, Melanie Weiss of Zebra Technologies, and Alex Ciccaney of A3 at the 2022 Vision Show in Boston. The group discusses the convergence of machine vision and mobile robotics, the shift toward cloud-based automation, and how manufacturing can learn from the repeatability of warehousing. They also cover Robex's acquisition by private equity, Zebra's acquisition of Fetch Robotics, and the growing adoption of AI and vision in non-traditional verticals like medical devices.
Jordan Kretchmer, CEO and Co-Founder of Rapid Robotics, explains his transition from MarTech to robotics and how his company is scaling robotic solutions to address the US labor shortage. He details Rapid Robotics's strategic refocus from heavy deployment services to robust computer vision and AI software, enabling robots to self-learn and adapt without programming. Kretchmer discusses how this shift allows for scalable, affordable automation that maintains profitability while keeping investors and customers aligned through transparency.
Chris Luecke and Jake Hall interview leaders from Plus One Robotics, IDS Imaging, Calvary Robotics, CMES Robotics, Lincode Labs, KUKA, and Locus Robotics at the A3 Vision Show and AMRL Conference in Boston. The guests discuss the persistent 'technology hesitancy' in warehouse automation, the critical role of machine vision in enabling mobile robots to operate in unstructured environments, and the workforce shortages driving adoption. They also share insights on market entry strategies, AI deployment for quality inspection, and the cultural differences between scaling a startup and operating within an enterprise.
Jeff Bier, president of BDTI and founder of the Edge AI and Vision Alliance, discusses the evolution of machine vision from constrained manufacturing environments to complex natural settings. He explains how the industry shifted from handcrafted algorithms to data-driven deep learning, enabling applications like mask detection and elder care monitoring. Bier also highlights the necessity of sensor fusion, such as combining vision with radar or LiDAR, to overcome environmental limitations like fog or darkness.
Priyansha Bagaria, founder and CEO of Loopr.ai, explains how her company uses AI-powered computer vision to automate quality inspection in aerospace, automotive, and general manufacturing. She details the deployment of their software applications, IQ Inspect for defect identification and IQ Verify for assembly verification, which are hardware-agnostic and integrate with existing camera infrastructure. Bagaria highlights key industry pain points, including the high cost of quality, labor shortages, and the loss of institutional knowledge as experienced workers retire, arguing that AI provides the consistency and scalability that human inspection cannot. She also discusses Loopr's strategic partnership with Microsoft and the importance of starting with high-value pilot projects to demonstrate clear ROI before scaling across factory floors.
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