Scholarlink

<p>In the advanced manufacturing field, the prediction of surface roughness during milling processes is of crucial importance as it directly influences product quality, tool life, and manufacturing efficiency. However, current surface roughness prediction methods exhibit limitations, particularly in terms of feature extraction and prediction accuracy. This study introduces a novel surface roughness prediction method based on a hybrid neural network, which uses time–frequency image and feature vector as multiple inputs and integrates a convolutional neural network (CNN) and a multi-head self-attention (MHA) mechanism. In this method, the input signals are initially subjected to noise reduction using variational mode decomposition (VMD), which effectively extracts cleaner signal features. Subsequently, continuous wavelet transform (CWT) is applied to generate time–frequency maps of the signals, providing a rich source of information for CNN. Furthermore, the multi-head attention mechanism is incorporated to enhance the model’s comprehension of global signal characteristics. A multi-input hybrid neural network model that intricately integrates CNN and MHA is constructed for surface roughness prediction. Through this model, a unique mechanism is designed to make the various inputs work synergistically, ensuring the complementary information from different sources is fully exploited. The proposed method shows a significant improvement in prediction accuracy compared to other state-of-the-art methods.</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

Prediction of surface roughness based on multi-input CNN-MHA in milling

  • 期刊论文

  • 发表日期：

    2025年7月29日

查看全文
  • Miaoxian Guo,
  • Shouheng Wei,
  • Chentong Han,
  • Weicheng Guo,
  • Zhiwen Huang

摘要

In the advanced manufacturing field, the prediction of surface roughness during milling processes is of crucial importance as it directly influences product quality, tool life, and manufacturing efficiency. However, current surface roughness prediction methods exhibit limitations, particularly in terms of feature extraction and prediction accuracy. This study introduces a novel surface roughness prediction method based on a hybrid neural network, which uses time–frequency image and feature vector as multiple inputs and integrates a convolutional neural network (CNN) and a multi-head self-attention (MHA) mechanism. In this method, the input signals are initially subjected to noise reduction using variational mode decomposition (VMD), which effectively extracts cleaner signal features. Subsequently, continuous wavelet transform (CWT) is applied to generate time–frequency maps of the signals, providing a rich source of information for CNN. Furthermore, the multi-head attention mechanism is incorporated to enhance the model’s comprehension of global signal characteristics. A multi-input hybrid neural network model that intricately integrates CNN and MHA is constructed for surface roughness prediction. Through this model, a unique mechanism is designed to make the various inputs work synergistically, ensuring the complementary information from different sources is fully exploited. The proposed method shows a significant improvement in prediction accuracy compared to other state-of-the-art methods.