AI-powered robot automates Serrano ham labeling, first in meat industry

Until this deployment, Serrano ham labeling—a necessary step early in the 10 to 18 month aging process—remained a purely manual operation despite the scale of production. A medium-sized producer processes more than 5,000 hams per day, making the accumulated human effort substantial. The core technical challenge was that hams are irregular natural objects; bones, which account for 30 to 40 percent of the weight in an 8 to 12 kg ham, do not occupy consistent positions across pieces. This variability made it impossible for conventional robots to perform the task, since the application tool cannot penetrate bone and the operator must avoid these zones to prevent equipment damage and physical injury.

Timpolot's solution addresses this constraint by layering traditional computer vision with AI-powered analysis. The vision system identifies each ham's position and orientation as it moves on the conveyor belt, then the AI algorithm predicts bone locations precisely enough that a standard robotic arm can reliably inject the label without breakage. The coordination loop—camera to PC (running Timpolot's custom software) to PLC (Omron NX1P2) to robot via Ethernet/IP—handles the constant variation in product geometry, ensuring consistent label placement across thousands of pieces. For the client operating the line, this means improved traceability through centralized IT management, elimination of the physical and cognitive burden on human operators, and the ability to redeploy those workers to higher-value tasks. The solution represents a narrowly scoped but concrete example of how AI vision, applied to an irregular real-world manufacturing constraint, can unlock automation where it was previously impossible.