摘要详情
81 / 2025-05-14 20:13:14
Multi-task Wear Prediction Based on Large Language Model
Wear Prediction,Large Language Models,,Edge Deployment,,Knowledge-driven AI
全文录用
With the rapid advancement of large language
models (LLMs), the integration of knowledge-driven and gen
erative AI into various industries has emerged as a prominent
research focus. In the field of tribology, several longstanding
challenges persist. One major issue is the variability of wear
conditions across different friction pairs, necessitating separate
model training for each working condition. This approach is
often constrained by limited training data, resulting in reduced
usability and the common problem of black-box models lacking
interpretability. Recent research suggests that LLMs possess
certain reasoning and abstraction capabilities, enabling them
to incorporate prior knowledge into their outputs for more
explainable results. Motivated by this, we constructed a fine
tuning dataset specifically for friction and wear, and used
it to train a lightweight large model for wear prediction.
Experimental results demonstrate that our model outperforms
mainstream medium-parameter LLMs deployed on edge de
vices.
重要日期
-
会议日期
08月01日
2025
至08月04日
2025
-
06月15日 2025
初稿截稿日期
主办单位
中国机械工程学会设备智能运维分会
承办单位
新疆大学
联系方式
- Mrs. Yanruqiang
- ya******@xjtu.edu.cn
历届会议
-
2025年08月01日 中国 wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023年09月21日 中国 Hefei
第一届（国际）设备智能运维大会
