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Thin-walled parts 2023 dataset released from Shandong University

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HMoTP

High-speed milling of thin-walled parts (HMoTP) dataset 2024 released by Prof. Qinghua Song's Lab from Shandong University. This is a dataset for high-performance machining of titanium alloy thin-walled parts containing milling force, milling bending moment, workpiece vibration, and tool wear data that can be used for tool wear monitoring and chatter detection researches.

Paper

This is a dataset for our accepted paper 'R. Wang, Q. Song, Y. Peng, et al. Toward digital twins for high-performance manufacturing: Tool wear monitoring in high-speed milling of thin-walled parts using domain knowledge, Robot Comput-Integr Manuf 2024;88:102723 https://doi.org/10.1016/j.rcim.2024.102723'. If this dataset is useful for your research, please cite our paper.

Dataset structure

Data of Each cutting in the dataset is stored as an independent CSV file. The cutting data of the three tools are stored in folders named CuttingsignalsT01/CuttingsignalsT02/CuttingsignalsT03 respectively. Each cutting tool has 100 sets of cutting data. Taking the T1 tool as an example, its cutting data is saved in 100 CSV files named T01_001.CSV to T01_100.CSV. Each CSV file of cutting data contains data for 7 channels: Fx, Fy, Fz, Mz, Ax, Ay, and Az. Each cutting data corresponds to a tool wear value, and the wear values of all tools are saved in files named ToolWeaT01.csv/ToolWeaT02.csv/ToolWeaT03.csv.

Download

All the data can be downloaded from one of the following links

  1. Baidu Netdisk: https://pan.baidu.com/s/1PFtNowJL_QUmHMtoHvRHJA Extraction code: sdum
  2. Google Drive: https://drive.google.com/drive/folders/1eBhTSXMd8jVITfQoJifC50fE_NIFqb89?usp=sharing
  3. Personal website: https://runqiong.wang/dataset/

Contact infor

Please contact Runqiong Wang (wangrunqiong@163.com), the first author of the paper, if you encounter any problems in using it.

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Thin-walled parts 2023 dataset released from Shandong University

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