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<p>The dimensional accuracy of thin-walled parts is critical to the performance of high-value mechanical systems but remains difficult to guarantee in production because of low structural stiffness, process-dependent deformation, and strongly noisy cutting-force signals. This work proposes a Multi-Scale Spatial Pyramid Pooling Variational Autoencoder (Multi-SPP-VAE) for feature-level dimensional error prediction in thin-walled 6061 aluminum machining. The model performs multiscale convolutional extraction of cutting-force signatures, applies residual shrinkage–based noise suppression with attention-guided latent encoding, and fuses static machining parameters (spindle speed, feed rate, depth of cut) directly into the latent space before supervised regression. Key hyperparameters are automatically tuned using an Enhanced Grey Wolf Optimization (EGWO) strategy to improve repeatability across machining conditions without manual retuning. The framework is evaluated under multiple sliding-window constructions and compared against established sequence modeling baselines. The proposed model consistently outperforms baselines across all datasets, yielding lower MSE, RMSE, and MAE and exhibiting higher stability under varying cutting conditions. It also achieves a high tolerance conformity rate. The improvements are statistically significant across repeated runs, indicating that the approach is suitable for in-process dimensional quality monitoring on standard workstation-class hardware.</p>

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Quality prediction using multiscale convolutional VAEs for thin plate parts

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月21日

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  • Xin Su,
  • Yichen Liu,
  • Ji Li

摘要

The dimensional accuracy of thin-walled parts is critical to the performance of high-value mechanical systems but remains difficult to guarantee in production because of low structural stiffness, process-dependent deformation, and strongly noisy cutting-force signals. This work proposes a Multi-Scale Spatial Pyramid Pooling Variational Autoencoder (Multi-SPP-VAE) for feature-level dimensional error prediction in thin-walled 6061 aluminum machining. The model performs multiscale convolutional extraction of cutting-force signatures, applies residual shrinkage–based noise suppression with attention-guided latent encoding, and fuses static machining parameters (spindle speed, feed rate, depth of cut) directly into the latent space before supervised regression. Key hyperparameters are automatically tuned using an Enhanced Grey Wolf Optimization (EGWO) strategy to improve repeatability across machining conditions without manual retuning. The framework is evaluated under multiple sliding-window constructions and compared against established sequence modeling baselines. The proposed model consistently outperforms baselines across all datasets, yielding lower MSE, RMSE, and MAE and exhibiting higher stability under varying cutting conditions. It also achieves a high tolerance conformity rate. The improvements are statistically significant across repeated runs, indicating that the approach is suitable for in-process dimensional quality monitoring on standard workstation-class hardware.