Targeted AI advances could revolutionise manufacturing
07 May 2025
Image courtesy of University of Notre Dame
Product quality and employee safety could be greatly improved through improvements in artificial intelligence, according to a new study.
The study, co-authored by an interdisciplinary team of experts from the University of Notre Dame, explores how a class of AI tools capable of processing multiple types of inputs and reasoning can affect the future of work. These tools, which include ChatGPT, are known as multimodal large language models.
While most studies on AI and work have focused on office work, this new research examined production work settings, where the benefits of AI may seem less apparent.
Notre Dame researchers collaborated with Indiana welding experts at the Elkhart Area Career Center, Plymouth High School, Career Academy South Bend, Plumbers & Pipefitters Local Union 172 and Ivy Tech Community College to gather images for the study, leveraging relationships cultivated through the work of the University's iNDustry Labs.
Northern Indiana has one of the highest concentrations of manufacturing jobs in the United States, and iNDustry Labs has collaborated with more than 80 companies in the region on more than 200 projects.
Research focused on welding across several industries: RV and marine, aeronautical and farming. The study examined how accurately large language models assessed weld images to determine whether the welds shown would work for different products.
Researchers found that while these AI tools showed promise in assessing weld quality, they performed significantly better analysing curated online images than actual welds.
"This discrepancy underscores the need to incorporate real-world welding data when training these AI models, and to use more advanced knowledge distillation strategies when interacting with AI," said co-author Nitesh Chawla.
"That will help AI systems ensure that welds work as they should. Ultimately, this will help improve worker safety, product quality and economic opportunity."
Researchers discovered that context-specific prompts may enhance the performance of AI models in some cases, and noted that the size or complexity of the models did not necessarily lead to better performance.
Ultimately, the study's co-authors recommended that future studies focus on improving models' ability to reason in unfamiliar domains.
"Our study shows the need to fine-tune AI to be more effective in manufacturing and to provide more robust reasoning and responses in industrial applications," said Grigorii Khvatski, a doctoral student in Notre Dame's Department of Computer Science and Engineering and a Lucy Family Institute Scholar.
Yong Suk Lee, Associate Professor of technology, economy and global, said the study's findings have important implications for the future of work. "As AI adoption in industrial contexts grows, practitioners will need to balance the trade-offs between using complex, expensive general-purpose models and opting for fine-tuned models that better meet industry needs," Lee said.
"Integrating explainable AI into these decision-making frameworks will be critical to ensuring that AI systems are not only effective but also transparent and accountable."