Scholarlink

<p>Accurate real-time prediction of tool wear under multi-condition operations is critical for ensuring machining precision and industrial productivity. This study proposes an end-to-end tool wear prediction framework integrating a 1D Convolutional Neural Network (1DCNN) and Transformer architecture, enhanced by dynamic parameter adaptation and transfer learning strategies. The core innovation lies in two aspects: (1) A novel Conditional Layer Normalization (CLN) mechanism that dynamically adjusts normalization parameters based on operational conditions (cutting depth, feed rate, material), enabling effective feature alignment across diverse scenarios; (2) A hybrid 1DCNN-Transformer structure that synergizes local temporal feature extraction through convolutional layers with global dependency modeling via multi-head attention. Transfer learning is systematically implemented by freezing backbone parameters while fine-tuning task-specific layers, ensuring efficient knowledge transfer from single-condition pre-trained models to multi-condition applications. The model was rigorously validated through two complementary approaches: NASA milling dataset analysis (16 operational conditions) and titanium alloy TC17 machining experiments. Quantitative evaluations demonstrated superior performance, achieving R² scores of 0.9069, 0.9058 and 0.9105 in material-variation, cutting-depth variation and feed-rate adaptation tasks, respectively, with RMSE consistently below 0.0936. These results confirm the framework’s capability to establish robust signal-to-wear mapping relationships under heterogeneous conditions. The proposed condition-aware architecture provides a scalable solution for industrial tool health monitoring systems. </p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Condition-adaptive 1DCNN-transformer for multi-condition tool wear prediction

  • 期刊论文

  • 发表日期：

    2026年1月17日

查看全文
  • Nan Zhang,
  • Long Li,
  • Ze Yu,
  • Lin Ma

摘要

Accurate real-time prediction of tool wear under multi-condition operations is critical for ensuring machining precision and industrial productivity. This study proposes an end-to-end tool wear prediction framework integrating a 1D Convolutional Neural Network (1DCNN) and Transformer architecture, enhanced by dynamic parameter adaptation and transfer learning strategies. The core innovation lies in two aspects: (1) A novel Conditional Layer Normalization (CLN) mechanism that dynamically adjusts normalization parameters based on operational conditions (cutting depth, feed rate, material), enabling effective feature alignment across diverse scenarios; (2) A hybrid 1DCNN-Transformer structure that synergizes local temporal feature extraction through convolutional layers with global dependency modeling via multi-head attention. Transfer learning is systematically implemented by freezing backbone parameters while fine-tuning task-specific layers, ensuring efficient knowledge transfer from single-condition pre-trained models to multi-condition applications. The model was rigorously validated through two complementary approaches: NASA milling dataset analysis (16 operational conditions) and titanium alloy TC17 machining experiments. Quantitative evaluations demonstrated superior performance, achieving R² scores of 0.9069, 0.9058 and 0.9105 in material-variation, cutting-depth variation and feed-rate adaptation tasks, respectively, with RMSE consistently below 0.0936. These results confirm the framework’s capability to establish robust signal-to-wear mapping relationships under heterogeneous conditions. The proposed condition-aware architecture provides a scalable solution for industrial tool health monitoring systems.