AdditiveLLM: Large language models predict defects in metals additive manufacturing

In this work we investigate the ability of large language models to predict additive manufacturing defect regimes given a set of process parameter inputs. For this task we utilize a process parameter defect dataset to fine-tune a collection of models, titled AdditiveLLM , for the purpose of predicting potential defect regimes including Keyholing , Lack of Fusion , and Balling . We compare different methods of input formatting in order to gauge the model’s performance to correctly predict defect regimes on our sparse Baseline dataset and our natural language Prompt dataset. The model displays robust predictive capability, achieving a Baseline accuracy of 94% and Prompt accuracy of 82% when asked to provide the defect regimes associated with a set of process parameters. The incorporation of natural language input further simplifies the task of process parameters selection, enabling users to identify optimal settings specific to their build. • Explores usage of LLMs to predict process parameter dependent defects. • Compares fine-tuning of 4 LLMs including DistilBERT, SciBERT, Llama, and T5. • Achieves an accuracy of 93% for sparse inputs and 88% for NLP inputs. • Utilizes dataset comprised of simulation and experimental data.