Adaptive Quantization Range Division Technique for Electronic Control Data Compression in CNC Machine Tools

With the development of new technologies such as artificial intelligence and big data, Industry 4.0 in manufacturing has been launched. As the core pillar of industrial manufacturing, computer numerical control (CNC) machine tools face significant challenges in data acquisition transmission and stor...

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Bibliographic Details
Published in:Electronics (Basel) 2023-08, Vol.12 (16), p.3387
Main Authors: Hu, Weiqi, Zhou, Huicheng, Yang, Jianzhong, Hui, Enming, Dai, Chaoren
Format: Article
Language:English
Subjects:
Accuracy
Adaptive control
Algorithms
Artificial intelligence
Big Data
Control data (computers)
Data acquisition
Data collection
Data compression
Data points
Data transmission
Digital twins
Efficiency
Electronic control
Fourier transforms
Heat of transformation
Industry 4.0
Internet of Things
Machine tools
Manufacturing
Massive data points
Methods
New technology
Numerical control
Numerical controls
Signal processing
Technical services
Wavelet transforms
Working conditions
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Summary:With the development of new technologies such as artificial intelligence and big data, Industry 4.0 in manufacturing has been launched. As the core pillar of industrial manufacturing, computer numerical control (CNC) machine tools face significant challenges in data acquisition transmission and storage due to their complex structure, high volume of data points, strong time-series characteristics, and large amounts of data. To address the shortcomings of existing compression algorithms in quantization methods for large amounts of data in the instruction-domain, this paper proposes a quantization method based on distortion rate evaluation and linear fitting entropy reduction transformation, which aims to compress state signals such as the load power and load current while ensuring the availability of the data. This approach provides technical support for the transmission of high-frequency big data and meets the lightweight data acquisition requirements of digital twins for CNC machine tools. Compared to the empirical approach, this approach was more accurate and more computationally efficient.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12163387