A method based on spindle motor current harmonic distortion measurements for tool wear monitoring

Akbari, Asghar; Danesh, Mahdi; Khalili, Khalil

doi:10.1007/s40430-017-0762-4

Cited by 28 publications

(18 citation statements)

References 10 publications

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“…Nie detected tool wear by using db8 to decompose the Acoustic Emission (AE) signals and extracting the feature vector of frequency band [ 3 ]. Akbari proposed a method that monitoring the tools wear by quantizing the harmonic distortion of spindle current and characteristic analysis in time and frequency domain [ 7 ]. In these studies, features are selected manually by signal analysis to observe tool wear changes, but the features used are relatively single and do not have the learning ability, so they are often poor in judging complex and changeable signals.…”

Section: Introductionmentioning

confidence: 99%

Deep Anomaly Detection for CNC Machine Cutting Tool Using Spindle Current Signals

Chen

et al. 2020

Sensors

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In recent years, industrial production has become more and more automated. Machine cutting tool as an important part of industrial production have a large impact on the production efficiency and costs of products. In a real manufacturing process, tool breakage often occurs in an instant without warning, which results a extremely unbalanced ratio of the tool breakage samples to the normal ones. In this case, the traditional supervised learning model can not fit the sample of tool breakage well, which results to inaccurate prediction of tool breakage. In this paper, we use the high precision Hall sensor to collect spindle current data of computer numerical control (CNC). Combining the anomaly detection and deep learning methods, we propose a simple and novel method called CNN-AD to solve the class-imbalance problem in tool breakage prediction. Compared with other prediction algorithms, the proposed method can converge faster and has better accuracy.

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Section: Introductionmentioning

confidence: 99%

Deep Anomaly Detection for CNC Machine Cutting Tool Using Spindle Current Signals

Chen

et al. 2020

Sensors

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“…Since the indirect methods can implement Sensors 2020, 20, 6113 2 of 20 online monitoring, it has been widely adopted. The most commonly used indirect monitoring signals include cutting force signal [2][3][4], vibration signal [5][6][7], acoustic emission signal [8,9], machined surface image [10,11], and current signal [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…Although multiple sensors can get more information related to the tool wear condition than a single current sensor, the costs and the interference caused by sensors increases with an increasing amount of sensors; therefore, in recent years, some researchers have made some achievements in the application of single-current sensors for tool wear monitoring. For example, Akbari et al [12] indicated that the total harmonic distortion (THD) and crest factor measures of the current signal can detect tool wear degree; however, they need additional harmonic analysis equipment to calculate the THD. Further, the tool wear condition will be misjudged if the tool cuts in or cuts out of the workpiece or if the power supply system is affected by the interference signal.…”

Section: Introductionmentioning

confidence: 99%

Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning

Yuan

Liu

Yang

et al. 2020

Sensors

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Most online tool condition monitoring (TCM) methods easily cause machining interference. To solve this problem, we propose a method based on the analysis of the spindle motor current signal of a machine tool. Firstly, cutting experiments under multi-conditions were carried out at a Fanuc vertical machining center, using the Fanuc Servo Guide software to obtain the spindle motor current data of the built-in current sensor of the machine tool, which can not only apply to the actual processing conditions but, also, save costs. Secondly, we propose the variational mode decomposition (VMD) algorithm for feature extraction, which can describe the tool conditions under different cutting conditions due to its excellent performance in processing the nonstationary current signal. In contrast with the popular wavelet packet decomposition (WPD) method, the VMD method was verified as a more effective signal-processing technique according to the experimental results. Thirdly, the most indicative features that relate to the tool condition were fed into the ensemble learning (EL) classifier to establish a nonlinear mapping relationship between the features and the tool wear level. Compared with existing TCM methods based on current sensor signals, the operation process and experimental results show that using the proposed method for the monitoring signal acquisition is suitable for the actual processing conditions, and the established tool wear prediction model has better performance in both accuracy and robustness due to its good generalization capability.

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“…Other researchers reported that harmonic had great impact on the magnitude of current. However, the reverse magnetic properties of the rotating shaft materials were not taken into account [17][18][19]. Many researchers [20][21][22][23] proposed some new energy-saving management, which can enhance efficiency through optimizing the motor current and torque, but the research studies did not involve a better fit for the ceramic spindle in some special cases.…”

Section: Introductionmentioning

confidence: 99%

Research on Ceramic Spindle Loss Based on Ceramic Reverse Magnetic Effect

Zhang

Wang

Bai

et al. 2020

Mathematical Problems in Engineering

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Ceramic motorized spindle is a multifield, nonlinear, and strong coupling system. The present model ignores the reverse magnetic effect and has a poor accuracy on the loss of the spindle system. In the paper, Park transformation was used to establish the electromagnetic physical model of ceramic motorized spindle. By combining with the Jiles–Atherton theory, an eccentric magnetization model of a ceramic motorized spindle considering reverse magnetic properties was established. Energy consumption parameters were calculated under various working conditions. The influence of ceramic reverse magnetic characteristics on the motorized spindle loss was analyzed and verified by experiments. The results show that the simulation of the ceramic motorized spindle loss model is in good agreement with the experimental results. Compared with the experimental results, the average loss error was 2.1%. Due to the reverse magnetic characteristics, the application of ceramic motorized spindle can help reduce the system loss. The ceramic motorized spindle model provides a theoretical basis for the development of ceramic spindle energy loss and efficiency.

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A method based on spindle motor current harmonic distortion measurements for tool wear monitoring

Cited by 28 publications

References 10 publications

Deep Anomaly Detection for CNC Machine Cutting Tool Using Spindle Current Signals

Deep Anomaly Detection for CNC Machine Cutting Tool Using Spindle Current Signals

Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning

Research on Ceramic Spindle Loss Based on Ceramic Reverse Magnetic Effect

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