Replication Data for: A framework to decompose process noise in fineblanking
收藏DataCite Commons2025-03-12 更新2025-04-15 收录
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https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/A1B09Y
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<h2>Dataset Description</h2>
<p>This dataset contains features extracted with the Python library catch22 with sliding windows (window size 50, stride 10) from force profiles acquired from 39,941 fineblanking shearing phases during a continuous (i.e., one complete tool lifecycle without disassembly of the tool during or between machine runs) experiment. The raw data was preprocessed with drift and tilt correction before feature extraction.</p>
<p>The features were provided for:
<ol>
<li>The full shearing path (fullsignal)</li>
<li>Extracted between 1.5 mm and 4.5 mm of the shearing path (croppedsignal)</li>
</ol>
</p>
<p>Furthermore, tearing data was visually evaluated every 200th process cycle and interpolated.</p>
<p>Additionally, SHAP values from feature importance analysis of XGBoost regression models that regressed the features to the tearing data are contained within the dataset both for 'fullsignal' and 'croppedsignal'.</p>
<p>All files are .npy arrays with the shape (n_samples, n_features).</p>
提供机构：
Harvard Dataverse
创建时间：
2025-03-12
