Analytical and comparative study of using a CNC machine spindle motor power and infrared technology for the design of a cutting tool condition monitoring system

Elgargni, M; Al‐Habaibeh, Amin

doi:10.1109/indin.2015.7281836

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…Many scholars have conducted numerous explorations on tool wear conditions monitoring technology using power signals. In the feature extraction method, Elgargni and Al-Habaibeh (2015) used discrete wavelet transformation (DWT) to extract the features of the collected power signals and found that the approximate coefficient curve of the normal tool gradually shifted to the left compared with the worn tool, which can clearly distinguish the tool wear conditions. As for the model establishment method, Rodrigo da Silva et al (2016) collected acoustic emission signals and power signals as samples and used a probabilistic neural network to establish a tool wear conditions monitoring model to identify the tool wear conditions.…”

Section: Introductionmentioning

confidence: 99%

Discrete wavelet transformation and genetic algorithm – back propagation neural network applied in monitoring woodworking tool wear conditions in the milling operation spindle power signals

Dong

Guo

et al. 2021

BioRes

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Tool wear conditions monitoring is an important mechanical processing system that can improve the processing quality of wood plastic composite furniture and reduce industrial energy consumption. An appropriate signal, feature extraction method, and model establishment method can effectively improve the accuracy of tool wear monitoring. In this work, an effective method based on discrete wavelet transformation (DWT) and genetic algorithm (GA) – back propagation (BP) neural network was proposed to monitor the tool wear conditions. The spindle power signals under different spindle speeds, depths of milling, and tool wear conditions were collected by power sensors connected to the machine tool control box. Based on the feature extraction method, the approximate coefficients of spindle power signal were extracted by DWT. Then, the extracted approximate coefficients, spindle speeds, depths of milling, and tool wear conditions were taken as samples to train the monitoring model. Threshold and weight of BP neural network were optimized by GA, and the accuracy of monitoring model established by the GA – BP neural network can reach 100%. Thus, the proposed monitoring method can accurately monitor tool wear conditions with different milling parameters, which can achieve the purpose of improving the processing quality of wood plastic composite furniture and reducing energy consumption.

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Section: Introductionmentioning

confidence: 99%

Discrete wavelet transformation and genetic algorithm – back propagation neural network applied in monitoring woodworking tool wear conditions in the milling operation spindle power signals

Dong

Guo

et al. 2021

BioRes

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show abstract

“…The vibration and power signals are used as input to determine the condition of the cutting tool when used with materials that are difficult to process. Elgargni and Al-Habaibeh [7] also use wavelet analysis to process infrared images and the power of the spindle and recognize the condition of cutting tool. Therefore, the wavelet analysis is a technique widely used in monitoring the condition of cutting tool.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Application of Synchronized Chaotic Systems of Different Fractional Orders for Cutting Tool Wear Diagnosis and Identification

et al. 2019

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Machining refers to a variety of processes, in which a cutting tool is used to remove the unwanted material from a workpiece. Tool wear, advertently or inadvertently, occurs after long-time use. It is crucial to monitor the tool wear so that the cutting tool can deliver the best performance and meet the technological challenges nowadays. In this paper, through different fractional-order chaotic systems, i.e., Chen-Lee, Lorenz, and Sprott, extension theory is proposed to predict the tool life. The results of the three chaotic systems are compared. The centroid of the 2-D plane of dynamic errors is used as the characteristics. Four wear states are defined in accordance with different levels of surface roughness, i.e., normal, slight, moderate, and severe. The boundaries of the four states are identified according to the locations of the centroid generated with the systems of different fraction orders. The boundaries are then fed into the extension model, and the relational function calculation is performed. In this way, the identification of tool state can be easily achieved. The experiment results indicate that Chen-Lee system and the Lorenz systems exhibit the same diagnosis rate (97.375%), higher than that of the Sprott system (35.75%). It is demonstrated that the two chaotic systems are fit for use with the method proposed in this paper. It is also proven that Chen-Lee and Lorenz fractional-order master-slave chaotic systems are very effective for tool life monitoring. The robustness of diagnosis is also greatly improved. INDEX TERMS Fractional order chaotic system, cutting, wear, machine tool.

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Technical data-driven tool condition monitoring challenges for CNC milling: a review

Wong

Chuah

Yap

2020

Int J Adv Manuf Technol

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Analytical and comparative study of using a CNC machine spindle motor power and infrared technology for the design of a cutting tool condition monitoring system

Cited by 6 publications

References 12 publications

Discrete wavelet transformation and genetic algorithm – back propagation neural network applied in monitoring woodworking tool wear conditions in the milling operation spindle power signals

Discrete wavelet transformation and genetic algorithm – back propagation neural network applied in monitoring woodworking tool wear conditions in the milling operation spindle power signals

A Study on the Application of Synchronized Chaotic Systems of Different Fractional Orders for Cutting Tool Wear Diagnosis and Identification

Technical data-driven tool condition monitoring challenges for CNC milling: a review

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