A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges

Data-driven methods provided smart manufacturing with unprecedented opportunities to facilitate the transition toward Industry 4.0-based production. Machine learning and deep learning play a critical role in developing intelligent systems for descriptive, diagnostic, and predictive analytics for machine tools and process health monitoring. This paper reviews the opportunities and challenges of deep learning (DL) for intelligent machining and tool monitoring. The components of an intelligent monitoring framework are introduced. The main advantages and disadvantages of machine learning (ML) models are presented and compared with those of deep models. The main DL models, including autoencoders, deep belief networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), were discussed, and their applications in intelligent machining and tool condition monitoring were reviewed. The opportunities of data-driven smart manufacturing approach applied to intelligent machining were discussed to be (1) automated feature engineering, (2) handling big data, (3) handling high-dimensional data, (4) avoiding sensor redundancy, (5) optimal sensor fusion, and (6) offering hybrid intelligent models. Finally, the data-driven challenges in smart manufacturing, including the challenges associated with the data size, data nature, model selection, and process uncertainty, were discussed, and the research gaps were outlined.

Automated summary

This paper reviews the opportunities and challenges of deep learning for intelligent machining and tool monitoring, discusses the main DL models, and introduces the components of an intelligent monitoring framework. It also highlights the advantages of machine learning models and compares them with deep models offering a hybrid intelligent models. Challenges associated with data size, data nature, model selection, and process uncertainty are outlined.