Small-sample and imbalanced milling chatter detection: Improved GAN with attention and hybrid deep learning

Chatter detection during milling processes plays a pivotal role in ensuring machining quality and efficiency. While the accuracy of chatter detection heavily relies on experimental data, systems tend to exhibit overfitting phenomena under conditions of limited training samples, resulting in diminished detection precision. To address this limitation, this study presents a data augmentation algorithm based on an Improved Generative Adversarial Network (IGAN). This algorithm integrates advanced techniques including Wasserstein distance metrics, cycle consistency constraints, and channel attention mechanisms, effectively enhancing the quality of generated data. An innovative milling chatter detection deep learning model (MNBGA) is constructed, synthesizing cutting-edge architectures such as multi-scale convolutional neural networks, bidirectional gated recurrent neural networks, and attention mechanisms. To optimize model performance, the Ivy algorithm is employed for hyperparameter optimization of the MNBGA model. When the training dataset comprises 40 or more samples, the proposed method achieves detection accuracy exceeding 90%. Notably, under extreme imbalanced data conditions (24:1:1 ratio), the detection accuracy maintains 84.32%. The processing time for 40 samples requires only 76.17 ms, meeting real-time monitoring requirements. This research presents a novel technical solution for addressing the challenge of milling chatter detection under small-sample conditions.