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

Dai, Wei; Liang, Kui; Huang, Tingting; Lu, Zhiyuan

doi:10.1007/s00170-021-08012-3

Cited by 7 publications

(3 citation statements)

References 47 publications

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“…Kalteh and Babouei 26 improve the control chart recognition accuracy by intelligently identifying control chart patterns with the improved adaptive neural fuzzy inference system. Dai et al 27 use an improved K-means clustering method to generate multiple tool wear patterns. The Support vector machine (SVM) is also a commonly used pattern recognition method.…”

Section: Related Previous Workmentioning

confidence: 99%

Diesel engine quality abnormal patterns recognition based on feature fusion and adaptive decision fusion

Wang,

Zhang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part B:

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The current assembly process of marine diesel engines is low in intelligence and the control chart pattern classifier with unstable performance, which makes it difficult to control and identify the quality control chart pattern. This paper proposes a new assembly quality control diagram recognition method based on an adaptive decision model to address these problems. Through characteristics and changes of the diesel engine assembly process analyses, the triangular norm is used to fuse the extracted shape features and statistical features to reduce the influence of data fluctuation and imbalance on pattern recognition. An adaptive decision fusion model of the assembly process is established by defining multiple weights with considering the complexity and uncontrollability of the diesel engine assembly process. Based on these, the fusion coefficients within the adaptive decision model are optimized by the Ant Lion Optimization algorithm (ALO) to improve the decision efficiency and classification precision. To validate the proposed model, diesel engine exhaust pressure is selected as a case for abnormal pattern recognition, and the ability of the model is discussed in terms of recognition accuracy and stability.

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Section: Related Previous Workmentioning

confidence: 99%

Diesel engine quality abnormal patterns recognition based on feature fusion and adaptive decision fusion

Wang,

Zhang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…The long-term use of milling cutter will cause certain wear. This will cause defects on the surface of the workpiece to be processed, reduce the quality of workpiece, and seriously cause the milling cutter to crack and vibrate [26]. As a result, the machine tool will be damaged, resulting in serious processing failures.…”

Section: Casementioning

confidence: 99%

A new complex system fault detection method based on belief rule base for unreliable interval values

Zhou¹,

Zhao²,

Yang³

et al. 2023

Meas. Sci. Technol.

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Equipment failures such as milling machines and inertial navigation systems can affect their normal operation, resulting in economic losses and personal injury in severe cases. Therefore, fault detection is of great importance. Belief rule base (BRB) is an expert system that plays an important role in fault detection. The traditional BRB has some problems in the explosion of the number of combination rules, the process of model inference, and the process of parameter optimization. To better deal with the above problems, this paper proposes a complex system fault detection method based on an interval-valued belief rule base (FDIR) and provides the construction and inference process of the method. In the method construction, the form of interval value and disjunction rules are introduced to solve the problem of the number explosion of combination rules, the indicator reliability is added to improve the accuracy of the method, and a new calculation method of rule availability is proposed. In the inference process, twice fusions are made based on evidence reasoning (ER) analysis algorithm and ER rule algorithm respectively to deal with the interval uncertainties. Moreover, the proposed FDIR method is optimized by the projection covariance matrix adaptive evolutionary strategy algorithm (P-CMA-ES). Finally, the effectiveness of the proposed method was verified through the research on milling fault detection and the experimental verification of inertial navigation system fault detection. The superiority of the model was also confirmed through comparative experiments.

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“…It establishes a mapping relationship with tool wear. This method provides online monitoring and aligns well with real-time production needs [5]. The indirect monitoring method involves the stages of signal acquisition and preprocessing, feature extraction, feature selection and identification model development.…”

Section: Introductionmentioning

confidence: 96%

Tool Wear State Identification Based on SVM Optimized by the Improved Northern Goshawk Optimization

Wang,

Xiang,

Cheng

et al. 2023

Sensors

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Tool wear condition significantly influences equipment downtime and machining precision, necessitating the exploration of a more accurate tool wear state identification technique. In this paper, the wavelet packet thresholding denoising method is used to process the acquired multi-source signals and extract several signal features. The set of features most relevant to the tool wear state is screened out by the support vector machine recursive feature elimination (SVM-RFE). Utilizing these selected features, we propose a tool wear state identification model, which utilizes an improved northern goshawk optimization (INGO) algorithm to optimize the support vector machine (SVM), hereby referred to as INGO-SVM. The simulation tests reveal that INGO demonstrates superior convergence efficacy and stability. Furthermore, a milling wear experiment confirms that this approach outperforms five other methods in terms of recognition accuracy, achieving a remarkable accuracy rate of 97.9%.

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

Cited by 7 publications

References 47 publications

Diesel engine quality abnormal patterns recognition based on feature fusion and adaptive decision fusion

Diesel engine quality abnormal patterns recognition based on feature fusion and adaptive decision fusion

A new complex system fault detection method based on belief rule base for unreliable interval values

Tool Wear State Identification Based on SVM Optimized by the Improved Northern Goshawk Optimization

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