IBM has launched an Internet of Things system as part of Watson. The tools is called Cognitive Visual Inspection, and the idea is to provide manufacturers with a “cognitive assistant” on the factory floor to minimize defects and increase product quality. According to IBM, in early production-cycle testing, Watson was able to reduce 80% of the inspection time while reducing manufacturing defects by 7-10%.
The system uses an ultra-high definition camera and adds cognitive capabilities from Watson to create a tool that captures images of products as they move through production and assembly. Together with human inspectors, Watson recognizes defects in products, including scratches or pinhole-size punctures.
“Watson brings its cognitive capabilities to image recognition,” Bret Greenstein, VP of IoT at IBM, told Design News. “We’re applying this to a wide range of industries, including electronics and automotive.”
The Inspection Eye That Never Tires
The system continuously learns based on human assessment of the defect classifications in the images. The tool was designed to help manufacturers achieve specialization levels that were not possible with previous human or machine inspection. “We created a system and workflow to feed images of good and bad products into Watson and train it with algorithms,” said Greenstein. “This is a system that you can be trained in advance to see what acceptable products look like.”
According to IBM, more than half of product quality checks involve some form of visual confirmation. Visual checking helps ensure that all parts are in the correct location, have the right shape or color or texture, and are free from scratches, holes or foreign particles. Automating these visual checks is difficult due to volume and product variety. Add to that the challenge from defects that can be any size, from a tiny puncture to a cracked windshield on a vehicle.
Some of the inspection training precedes Watson’s appearance on the manufacturing line. “There are several components. You define the range of images, and feed the images into Watson. When it produces the confidence level you need, you push it to the operator stations,” said Greenstein. “Watson concludes whether the product awesome or defective. You let the system make the decision.”
The ultimate goal is to keep Watson on a continuous learning curve. “We can push this system out to different manufacturing lines, and we can train it based on operators in the field and suggest changes to make the system smarter, creating an evolving inspection process,” said Greenstein.
The ABB Partnership
As part of its move into the factory, IBM has formed a strategic collaboration with ABB. The goal is to combine ABB’s domain knowledge and digital solutions with IBM’s artificial intelligence and machine-learning capabilities. The first two joint industry solutions powered by ABB Ability and Watson were designed to bring real-time cognitive insights to the factory floor and smart grids.
The suite of solutions developed by ABB and IBM are intended to help companies improve quality control, reduce downtime, and increase speed and yield. The goal is to improve on current connected systems that simply gather data. Instead, Watson is designed to use data to understand, sense, reason, and take actions to help industrial workers to reduce inefficient processes and redundant tasks.
According to Greenstein, Watson is just getting its industry sea legs. In time, the thinking machine will take on increasing industrial tasks. “We found a wide range of uses. We’re working with drones to look at traffic flows in retail situation to analyze things that are hard to see from a human point of view,” said Greenstein. “We’re also applying Watson’s capabilities to predictive maintenance.
Rob Spiegel has covered automation and control for 17 years, 15 of them for Design News. Other topics he has covered include supply chain technology, alternative energy, and cyber security. For 10 years, he was owner and publisher of the food magazine Chile Pepper.
Image courtesy of IBM