A Neural-Fuzzy Pattern Recognition Algorithm Based Cutting Tool Condition Monitoring Procedure

Metal cutting operations constitute a large percentage of the manufacturing activity. One of the most important objectives of metal cutting research is to develop techniques that enable optimal utilization of machine tools, improved production efficiency, high machining accuracy and reduced machine downtime and tooling costs. Machining process condition monitoring is certainly the important monitoring requirement of unintended machining operations. A multi-purpose intelligent tool condition monitoring technique for metal cutting process will be introduced in this paper. The knowledge based intelligent pattern recognition algorithm is mainly composed of a fuzzy feature filter and algebraic neurofuzzy networks. It can carry out the fusion of multi-sensor information to enable the proposed intelligent architecture to recognize the tool condition successfully.

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Tool condition monitoring in the milling process based on multisource pattern recognition model

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-021-08012-3 ◽

2021 ◽

Author(s):

Wei Dai ◽

Kui Liang ◽

Tingting Huang ◽

Zhiyuan Lu

Keyword(s):

Pattern Recognition ◽

Condition Monitoring ◽

Milling Process ◽

Tool Condition Monitoring ◽

Recognition Model ◽

Tool Condition

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Method for Grade Identification of Tobacco Based on Machine Vision

Transactions of the ASABE ◽

10.13031/trans.12627 ◽

2018 ◽

Vol 61 (5) ◽

pp. 1487-1495

Author(s):

Yan He ◽

Haijun Wang ◽

Shiping Zhu ◽

Tao Zeng ◽

Zhenzhen Zhuang ◽

...

Keyword(s):

Pattern Recognition ◽

Machine Vision ◽

Recognition Algorithm ◽

Identification System ◽

Tobacco Leaves ◽

Tobacco Leaf ◽

Fuzzy Pattern Recognition ◽

Fuzzy Pattern ◽

Model Set

Abstract. Tobacco grading is the first step in the transfer of tobacco leaves from agricultural products to commodities and is key to determining the quality of tobacco. Manual grading is conventionally used for tobacco grading. However, it is time-consuming, expensive, and may require specialized labor. To overcome these limitations, a method for grade identification of tobacco leaves based on machine vision is proposed in this article. Based on a fuzzy pattern recognition algorithm, the tobacco leaf samples of the model set and prediction set could be classified by extracting appearance characteristics of the tobacco leaves. The identification system for tobacco leaves based on fuzzy pattern recognition was developed in MATLAB. The rate of correct grading was 85.81% and 80.23% for the modeling set and prediction set, respectively. This result shows that machine vision based automatic tobacco grading has a great advantage over manual grading, and this method can be explored for viable commercial use. Keywords: Fuzzy pattern recognition, Grade identification, Machine vision, Tobacco leaf.

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Tool Condition Monitoring in Milling Process Based on Multi-Dource Pattern Recognition Model

10.21203/rs.3.rs-332412/v1 ◽

2021 ◽

Author(s):

Kui Liang ◽

Wei Dai ◽

Tingting Huang ◽

Zhiyuan Lu

Keyword(s):

Pattern Recognition ◽

Condition Monitoring ◽

Milling Process ◽

Tool Condition Monitoring ◽

Monitoring Methods ◽

Model Framework ◽

Recognition Model ◽

Practical Applications ◽

Transfer Path ◽

Tool Condition

Abstract In the milling process of metallic parts, appropriate tool condition is essential to reducing processing faults and ensuring manufacturing quality. However, the existing condition monitoring methods are usually limited by recognizing intermediate abnormal states in milling processing, which is inefficient and impractical for real practical applications. Therefore, this paper proposes a Tool condition monitoring (TCM) method in milling process based on multi-source pattern recognition and state transfer path. Firstly, improved K-Means clustering method is used to generate multiple patterns of tool wear. Secondly, a multi-source pattern recognition model framework is developed, and the multiple observation windows and the pattern transfer path are considered in multi-source pattern recognition model. Lastly, PHM2010 datasets are used to verify the feasibility of the proposed method, and the results demonstrate the applicability of the proposed method in practice for tool condition monitoring.

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