用混合对抗谱损失生成高精度切削力信号，解决超精密加工表面粗糙度预测的数据不足问题。
Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction
- 融合对抗损失与频域谱损失，优化高频切削力信号生成
- 生成520+合成样本后预测误差从31.4%降至约9%
- 适合超精密制造中数据稀缺场景的模型增强
超精密加工（UPM）中的表面粗糙度精确预测对实时质量控制至关重要，但小样本数据限制了模型性能。本文提出HAS-CGAN，一种结合对抗损失与频域谱损失的条件生成对抗网络，用于有效生成UPM加工过程中的1维切削力信号。在五种CGAN变体中，HAS-CGAN在高频信号生成方面表现最优，通过傅里叶域优化实现>0.85的波形相干性。将生成信号与加工参数融合后，显著提升预测精度。实验采用传统机器学习（SVR、RF、LSTM）与深度学习模型（BPNN、1DCNN、CNN-Transformer），结果表明：使用520+个合成样本进行训练，可使预测误差从原始52样本时的31.4%降低至约9%，有效缓解了UPM粗糙度预测中的数据稀缺问题。
原文摘要 · Abstract (English)
Accurate surface roughness prediction in ultra-precision machining (UPM) is critical for real-time quality control, but small datasets hinder model performance. We propose HAS-CGAN, a Hybrid Adversarial Spectral Loss CGAN, for effective UPM data augmentation. Among five CGAN variants tested, HAS-CGAN excels in 1D force signal generation, particularly for high-frequency signals, achieving >0.85 wavelet coherence through Fourier-domain optimization. By combining generated signals with machining parameters, prediction accuracy significantly improves. Experiments with traditional ML (SVR, RF, LSTM) and deep learning models (BPNN, 1DCNN, CNN-Transformer) demonstrate that augmenting training data with 520+ synthetic samples reduces prediction error from 31.4% (original 52 samples) to ~9%, effectively addressing data scarcity in UPM roughness prediction."
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