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
全文待审
Zhongning Wang / beihang University
Yihu Tang / 中国船舶集团有限公司第七一一研究所
Feng Zhu / National Key Laboratory of Marine Engine Science and Technology
Kai Jiang / BeihangUniversity
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

    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
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