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Online Recognition of Single Crystal Diamond Tool Grinding Direction Based on PSO-BP and Multi-information Fusion
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Xue-wen FENG1, Bin ZHAO1, Hai-tao MA1, *, Jia-yu WU1, Jirigalantu2
Science Technology and Engineering | 2025, 25(7) : 2784 - 2791
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Science Technology and Engineering | 2025, 25(7): 2784-2791
Papers·Petroleum and Natural Gas Industry
Online Recognition of Single Crystal Diamond Tool Grinding Direction Based on PSO-BP and Multi-information Fusion
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Xue-wen FENG1, Bin ZHAO1, Hai-tao MA1, *, Jia-yu WU1, Jirigalantu2
Affiliations
  • 1 School of Electrical &Electronic Engineering, Changchun University of Technology, Changchun 130012, China
  • 2 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
Published: 2025-03-08 doi: 10.12404/j.issn.1671-1815.2307547
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In order to improve the online recognition accuracy of the grinding direction of single crystal diamond tools and address the limitation of acquiring limited information from a single sensor in grinding monitoring, this study a method for online recognition of the grinding direction of single crystal diamond tools based on multi-information fusion and particle swarm optimization (PSO) algorithm for optimizing the BP(back propagation) neural network was proposed. Vibration signals and acoustic emission (AE) signals were collected during the grinding process. The wavelet packet decomposition method was applied to analyze the vibration signals of the tool and identify the characteristic frequency bands strongly correlated with the grinding direction. The parameter analysis method was used to analyze the AE signals and extract the characteristic parameters. The energy values of the characteristic frequency bands in the vibration signals and the characteristic parameters of the AE signals were taken as the feature parameters for identifying the grinding direction of the tool. These feature parameters were then used as inputs to the BP neural network model for fusion and online recognition of the grinding direction. To overcome the disadvantage of the BP neural network easily getting stuck in local minima, the PSO algorithm was utilized to optimize the weights and thresholds of the neural network, effectively solving the problem of local minima. The experimental results show that the accuracy of online identification of the grinding direction of single crystal diamond tools is effectively improved by PSO-BP and multi-information fusion, reaching an accuracy of 85%, providing a new method for online identification of the grinding direction of single crystal diamond tools.

single crystal diamond tool  /  grinding direction  /  multi-information fusion  /  online identification  /  PSO-BP
Xue-wen FENG, Bin ZHAO, Hai-tao MA, Jia-yu WU, Jirigalantu. Online Recognition of Single Crystal Diamond Tool Grinding Direction Based on PSO-BP and Multi-information Fusion[J]. Science Technology and Engineering, 2025 , 25 (7) : 2784 -2791 . DOI: 10.12404/j.issn.1671-1815.2307547
Year 2025 volume 25 Issue 7
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Article Info
doi: 10.12404/j.issn.1671-1815.2307547
  • Receive Date:2023-09-25
  • Online Date:2026-03-30
  • Published:2025-03-08
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  • Received:2023-09-25
  • Revised:2024-07-09
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    1 School of Electrical &Electronic Engineering, Changchun University of Technology, Changchun 130012, China
    2 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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