摘要
针对传统刀具磨损预测精度低、泛化能力差、难以实现磨损状态实时预测的问题,提出一种融合YOLOv11-Seg模型与Transformer模型的刀具磨损多步向前实时预测方法。搭建数控机床刀具磨损在机监测实验平台,采用PMC编程实现刀具自动切削和磨损区域自动拍照。采用改进YOLOv11-Seg模型进行刀具磨损量检测,引入CoordAtt坐标注意力机制和Shape-IoU损失函数以提高磨损区域分割的精度。基于刀具磨损时序数据和实时磨损量数据构建改进Transformer多步向前刀具磨损预测(AFTWP)模型,并在改进Transformer模型中引入残差校正机制,提高了MFTWP模型预测的精度和稳定性。采用公开数据集和实验数据集测试改进模型,将结果与传统预测模型进行比较,验证了提出的多步向前实时预测模型的精确性和泛化能力。
To address the problems of low prediction accuracy,poor generalization capability,and difficulty in achieving real-time prediction of tool wear states in traditional methods,a multi-step forward realtime prediction method for tool wear was proposed by integrating the YOLOv11-Seg model with a Transformer model.An on-machine monitoring experimental platform for tool wear of CNC machines was constructed,and PMC programming was employed to realize automatic cutting of tools and automatic photographing of wear regions.An improved YOLOv11-Seg model was adopted for tool wear measurement,where the CoordAtt coordinate attention mechanism and the Shape-IoU loss function were introduced to improve the accuracy of wear region segmentation.Based on tool wear time-series data and real-time wear measurement data,an improved Transformer multi-step forward real-time prediction model was established under the MFTWP mode,where the residual error correction mechanism was introduced to enhance the accuracy and stability of MFTWP mode prediction.The proposed model was tested on both of public datasets and experimental datasets,and was compared with traditional prediction models.The results show that the proposed multi-step forward real-time prediction model exhibits high accuracy and good generalization capability.
作者
肖御风
张超勇
赛希亚拉图
孟一帆
朱传军
XIAO Yufeng;ZHANG Chaoyong;Saixiyalatu;MENG Yifan;ZHU Chuanjun(School of Mechanical Engineering,Hubei University of Technology,Wuhan,430068;School of Mechanical Science and Engineering,Huazhong University of Science and Technology,Wuhan,430074)
出处
《中国机械工程》
北大核心
2025年第12期2944-2951,共8页
China Mechanical Engineering
基金
高端数控机床与基础制造装备科技重大专项(2024ZD0707501)
国家重点研发计划政府间国际科技创新合作专项(2022YFE0114200)。
