Artificial intelligence-powered large language models (LLM) need to be trained on massive datasets to make accurate predictions - but what if researchers don't have enough of the right type of data?
A team at Penn State recently developed an integration framework that uses minimal new experimental data to identify relevant information in existing scientific literature. By combining information from existing research and their own experiments, the LLM-powered framework can derive numeric equations that accurately predict physical phenomena in high-speed laser welding, a manufacturing technique capable of highly precise welds on items like fuel cells in electric vehicles. Using their new approach, the researchers said they can begin to optimize the welding technique, which typically has a high potential for technical failure.
Their research, available online today, will be featured in the October issue of the International Journal of Machine Tools and Manufacture.
According to Zhengxiao Yu, a doctoral candidate studying industrial and manufacturing engineering (IME) and co-author of the paper, traditional methods of developing equations are time consuming for researchers and require tons of existing numeric data. Researchers must either produce more than 1,000 data points from their own experiments, or review and interpret data points from prior studies by other researchers to accurately formulate an equation.
"With our model, we can simply input the literature data and substantially speed up the process," Yu said.
The numeric equations enable researchers to better understand the connections between various parameters, leading to highly detailed insights into why and when certain physical responses appear during welding. According to Zen-Hao Lai, a doctoral candidate studying materials science and engineering and co-author of the paper, one of the most prevalent phenomena their LLM can help explain is humping - a common defect in laser welding that occurs when metal is welded too quickly. Equations detailing the specific parameters involved in humping errors could help researchers address the problem in future welds.
Using an equation also allows for the team to effectively incorporate data taken from prior experiments when predicting the physical properties of a new weld, even if the physical characteristics of the old welds - like metal type or the speed of the welding system - are not identical.
