In machining operations, dynamometers are typically used to directly measure the forces acting on cutting tools. However, their high cost and complex setup restrict their use to laboratory environments, making them unsuitable for real-time monitoring in general production settings. To overcome this limitation, this study proposes an autoencoder-based learning model for estimating cutting forces using only spindle vibration signals acquired during milling. The model features a deep neural network (DNN) that takes processed spindle vibration signals as input and predicts latent features derived from cutting force signals through an autoencoder. These predicted latent features are then fed into a pretrained decoder to reconstruct the corresponding cutting force signals. To enhance the model's accuracy and robustness, the raw vibration signals sampled at 20 kHz were filtered with a bandpass filter that spans the effective frequency range of 20–2500 Hz, effectively removing irrelevant noise. For validation, an accelerometer was mounted on the spindle head of a milling machine, and vibration data were collected during cutting. The estimated cutting forces were compared to ground truth measurements obtained from a dynamometer. The model achieved a Pearson correlation coefficient of 0.943, demonstrating that reliable cutting force estimation is achievable using only low-cost vibration sensors.

Anomaly detection models using big data generated from facilities and equipment have been adopted for predictive maintenance in the manufacturing industry. When facility faults or defects occur, different patterns of abnormal data are shown owing to their component behaviors. By detecting these pattern changes, it is possible to determine whether a facility abnormality occurs. This study evaluated the anomaly detection results from a combined driving system consisting of three driving motors for about six months at a manufacturing site. The learning data with an autoencoder model for about a month at the beginning of vibration data collection and continuous monitoring of anomalies using reconstruction errors showed that a component defect occurred in one driving motor, and the reconstruction error increased progressively about three months earlier than a facility manager found the failure. In addition, the micro-electro-mechanical systems sensor showed high amplitude in the entire frequency domain when high reconstruction errors occurred. However, the integrated electronics piezoelectric sensor showed different patterns as high amplitude in a specific frequency domain. The results of this study will be helpful for detecting facility abnormalities in combined driving systems using vibration sensors.

Deep learning-based fault diagnosis systems for prognostics and health management of mechanical systems is an active research topic. Notably, the absence and class imbalance of fault data (insufficient fault data compared to normal data) have been shown to cause many challenges in developing fault diagnosis systems for the manufacturing fields. Therefore, this paper presents case studies using deep learning algorithms in the absence or class imbalance of fault data. Auto-encoder-based anomaly detection method, which can be used when fault data is absent, was applied to diagnose faults in a robotic spot welding process. The anomaly detection threshold was set based on the reconstruction error of trained normal data and the confidence level of the distribution of normal data. The anomaly detection performance of the auto-encoder was verified using non-trained normal data and three sets of fault data through the threshold. As a case study for insufficient fault data, synthetic data was generated based on cGAN and applied to diagnose fault of bearing. Using the imbalanced dataset to generate synthetic fault data and to reduce the imbalance ratio, it was confirmed that the accuracy of the synthetic data generation-based 2DCNN fault diagnosis model was improved.