AI Resilience Report forFoundry Mold and Coremakers

If you're worried about robots taking over foundry work, here's the honest picture: parts of mold and coremaking are being automated, but the field still depends heavily on skilled human hands. Most foundry automation today targets the heaviest, hottest, and most repetitive steps rather than replacing entire jobs. More foundries are turning to automation to tackle age-old challenges in metal casting operations, from heavy material handling to worker safety.

A typical example is robotic sand mold and core milling systems, automated core shooters, and 3D sand printing systems that can produce complex molds and cores with tight tolerances, reducing defects and improving casting quality.

AI is showing up mostly as an augmentation tool that helps human coremakers, not replaces them. The first Aluminum Casting Performance Initiative project aims to develop a machine-learning-based image recognition tool to diagnose aluminum casting defects, intended to capture knowledge and assist training of new team members, while improving inspection speed and costs. The American Foundry Society also rolled out an AI Library Search Tool ^[1] that delivers clear, concise summaries with source citations in seconds—making it easier than ever to digest complex research.

Tasks like rotating sweep boards, smoothing repairs on cores, and spraying parting agents still rely on the judgment and dexterity of human workers.

Adoption is steady but slow, and several real-world forces shape the pace. The biggest push comes from a shrinking labor pool: 79% of manufacturing executives say the skilled-labor shortage continues to be their biggest challenge, and 69% are investing in robots, equipment and other hardware to fill the workforce gap—9% higher than in 2025. Deloitte adds the long-view: in 2024, production occupations made up 48.7% of total manufacturing employment—lower than their 51.9% share in 2003, a decline that stems from automation, rising productivity due to new technology, and higher outsourcing ^[2].

On the other hand, brakes on adoption are very real. The high initial costs associated with advanced robotic systems and necessary infrastructure upgrades can be a substantial barrier, particularly for smaller foundries operating on tight budgets. Foundries also have well-established workflows and legacy systems that may not easily interface with new automation technologies, and ensuring flexibility to handle product variety and changes in demand is another significant hurdle because every casting job can be different.

The encouraging takeaway for young people: human coremakers who learn to program, operate, and troubleshoot these AI-assisted machines become more valuable, not less—skilled trades plus tech literacy is the combination employers are actively recruiting for in today's tight labor market ^[2].