Using a data fusion neural network in the tool wear monitoring of a computer numerical control turning machine

Tool wear monitoring is essential in precision manufacturing to improve surface quality, increase machining efficiency, and reduce manufacturing cost. Although tool wear can be reflected by measurable signals in automatic machining operations, with the increase of collected data, features are manually extracted and optimized, which lowers monitoring efficiency and increases prediction error. For addressing the aforementioned problems, this paper proposes a tool wear monitoring method using vibration signal based on short-time Fourier transform (STFT) and deep convolutional neural network (DCNN) in milling operations. First, the image representation of acquired vibration signals is obtained based on STFT, and then the DCNN model is designed to establish the relationship between obtained time-frequency maps and tool wear, which performs adaptive feature extraction and automatic tool wear prediction. Moreover, this method is demonstrated by employing three tool wear experimental datasets collected from three-flute ball nose tungsten carbide cutter of a high-speed CNC machine under dry milling. Finally, the experimental results prove that the proposed method is more accurate and relatively reliable than other compared methods.

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Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network

Applied Sciences ◽

10.3390/app112412041 ◽

2021 ◽

Vol 11 (24) ◽

pp. 12041

Author(s):

Qun Wang ◽

Hengsheng Wang ◽

Liwei Hou ◽

Shouhua Yi

Keyword(s):

Neural Network ◽

Neural Networks ◽

Tool Wear ◽

Network Architecture ◽

Manufacturing Systems ◽

Convolution Neural Network ◽

Tool Wear Monitoring ◽

Monitoring Methods ◽

Deep Convolutional Neural Networks ◽

Wear Monitoring

Tool wear monitoring is of great significance for the development of manufacturing systems and intelligent manufacturing. Online tool condition monitoring is a crucial technology for cost reduction, quality improvement, and manufacturing intelligence in modern manufacturing. However, it remains a difficult problem to monitor the status of tools online, in real-time and accurately in the industry. In the research status of mainstream technology, the convolution neural network may be a good solution to this problem, based on the appropriate sensor system and correct signal processing methods. Therefore, this paper outlines the state-of-the-art systems encountered in the open access literature, focusing on information collection, feature selection–extraction technologies based on deep convolutional neural networks, and monitoring network architecture and modeling methods. Based on typical cases, this paper focuses on the application of the convolution neural network in tool wear monitoring. From the application results, it is feasible and reliable to apply convolution neural networks in tool wear monitoring. Additionally, it can improve the prediction accuracy, which is of great significance for the future development of technology. This paper can be a guide for the researchers and manufacturers in the area of tool wear monitoring for explaining the latest trends and requirements.

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