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A practical prediction method for grinding accuracy based on multi-source data fusion in manufacturing

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Abstract

The quality of workpieces is affected by many factors, such as machine tool errors, and their machining accuracy needs to be improved. Therefore, an accuracy prediction method based on the attention convolutional long short-term memory neural network (ACLSTM) is proposed in this paper. According to an analysis of the operational data of certain equipment, such as the temperature, the current and the rotational speed of each motion axis of the machine tool, this method completes the prediction of the workpiece grinding accuracy. The experimental results show that the ACLSTM method is able to quickly and accurately predict the actual workpiece size after processing. The result of the proposed method was compared with other conventional regression prediction methods, and the performance of ACLSTM is significantly better than other methods, which can be practically applied to the workpiece size prediction in industrial processing to further control processing quality.

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Data availability

The datasets used during the current study are available from the corresponding author on reasonable request.

Code availability

The codes used during the current study are available from the corresponding author on reasonable request.

Abbreviations

ACLSTM:

Attention convolutional long short-term memory neural network

CLSTM:

Convolutional long short-term memory neural network

LSTM:

Long short-term memory neural network

ACNN:

Attention convolutional neural network

KNN:

K-nearest-neighbor regression

SVR:

Support vector regression

MLP:

Multi-layer perceptron regression

AE:

Autoencoder

AM:

Attention mechanism

SE_Block:

Squeeze and excitation network block

ReLU:

Rectified linear unit

LeakyReLU:

Leaky rectified linear unit

FC:

Full connection

MSE:

Mean squared error

MAE:

Mean absolute error

EVS:

Explained variance score

R 2 :

Coefficient of determination score

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Funding

This work is supported by the National Key Research and Development Program of China under grant 2018YFB1701203.

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Authors and Affiliations

  1. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400030, China

    Haipeng Wu, Zhihang Li, Qian Tang & Penghui Zhang

  2. Qinchuan Machine Tool Headquarters Technology Research Institute, Qinchuan Machine Tool & Tool Group Corp, Baoji, 721009, Shaanxi, China

    Dong Xia & Lianchang Zhao

Authors
  1. Haipeng Wu
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  2. Zhihang Li
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  3. Qian Tang
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  4. Penghui Zhang
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  5. Dong Xia
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Contributions

Haipeng Wu, Writing—Original Draft, Visualization, Typesetting; Zhihang Li, Writing—Review & Editing, Experimental arrangement; Qian Tang, Project administration, Funding acquisition, Supervision; Penghui Zhang, Investigation, Data Curation, Methodology; Dong Xia, Resources, Supervision; Lianchang Zhao, Resources, Experimental arrangement

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Correspondence to Qian Tang.

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Wu, H., Li, Z., Tang, Q. et al. A practical prediction method for grinding accuracy based on multi-source data fusion in manufacturing. Int J Adv Manuf Technol 127, 1407–1417 (2023). https://doi.org/10.1007/s00170-023-11561-4

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