Author: Will Knight
This post originally appeared on Business Latest
In 1913, Henry Ford revolutionized car-making with the first moving assembly line, an innovation that made piecing together new vehicles faster and more efficient. Some hundred years later, Ford is now using artificial intelligence to eke more speed out of today’s manufacturing lines.
At a Ford Transmission Plant in Livonia, Michigan, the station where robots help assemble torque converters now includes a system that uses AI to learn from previous attempts how to wiggle the pieces into place most efficiently. Inside a large safety cage, robot arms wheel around grasping circular pieces of metal, each about the diameter of a dinner plate, from a conveyor and slot them together.
Ford uses technology from a startup called Symbio Robotics that looks at the past few hundred attempts to determine which approaches and motions appeared to work best. A computer sitting just outside the cage shows Symbio’s technology sensing and controlling the arms. Toyota and Nissan are using the same tech to improve the efficiency of their production lines.
The technology allows this part of the assembly line to run 15 percent faster, a significant improvement in automotive manufacturing where thin profit margins depend heavily on manufacturing efficiencies.
“I personally think it is going to be something of the future,” says Lon Van Geloven, production manager at the Livonia plant. He says Ford plans to explore whether to use the technology in other factories. Van Geloven says the technology can be used anywhere it’s possible for a computer to learn from feeling how things fit together. “There are plenty of those applications,” he says.
AI is often viewed as a disruptive and transformative technology, but the Livonia torque setup illustrates how AI may creep into industrial processes in gradual and often imperceptible ways.
Automotive manufacturing is already heavily automated, but the robots that help assemble, weld, and paint vehicles are essentially powerful, precise automatons that endlessly repeat the same task but lack any ability to understand or react to their surroundings.
Adding more automation is challenging. The jobs that remain out of reach for machines include tasks like feeding flexible wiring through a car’s dashboard and body. In 2018, Elon Musk blamed Tesla Model 3 production delays on the decision to rely more heavily on automation in manufacturing.
Researchers and startups are exploring ways for AI to give robots more capabilities, for example enabling them to perceive and grasp even unfamiliar objects moving along conveyor belts. The Ford example shows how existing machinery can often be improved by introducing simple sensing and learning capabilities.