A multi-sensor approach to the monitoring of end milling operations

Chung, Kwok Wai; Geddam, A.

doi:10.1016/s0924-0136(03)00175-4

Cited by 41 publications

(12 citation statements)

References 8 publications

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“…Extensive research has been performed for tool condition monitoring in milling operations. In these methods, various signals [4] such as cutting force, acoustic emission (AE), machine vibration, motor current have been used in monitoring tool condition [5][6][7][8][9][10][11][12][13].Cutting force measurement requires installation of a force-torque sensor. This has a negative influence on the dynamics and stiffness of the machine, while at the same time the cost of such a device is considerably high.…”

Section: Introductionmentioning

confidence: 99%

On the Estimation of Tool-wear for Milling Operations based on Multi- Sensorial Data

et al. 2013

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This paper shows the results of a preliminary experimental investigation on tool-wear in end milling. Spindle torque and vibration signals were recorded during the process. A correlation between measured signals and tool-wear was attempted. The preliminary results showed a promising application laying the foundation for an experimental modelling approach of the toolwear phenomenon. Power consumption, as depicted from the current draw signal, can be associated with the sustainability evaluation of the milling operation, due to their directly correlation to the toolwear level.

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Section: Introductionmentioning

confidence: 99%

On the Estimation of Tool-wear for Milling Operations based on Multi- Sensorial Data

et al. 2013

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“…With the development of machine tools, in the direction of automation, integration, and unmanned development, the research of intelligent tool monitoring technology is particularly important [3]. erefore, experts at home and abroad have carried out many related researches on it, such as taking acoustic emission, vibration, power, force, and current signals as monitoring signals, collecting features related to tool status, and indirectly monitoring tool status [4][5][6][7][8]. Since the data collected by sensors are large and the physical dimensions of signals collected by different sensors are different, simultaneous interpreting of tool wear state is adopted.…”

Section: Introductionmentioning

confidence: 99%

Wear Identification of Vibration Drilling Bit Based on Improved LMD and BP Neural Network

Shi

Liu

Zhang

et al. 2020

Mathematical Problems in Engineering

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In view of the low accuracy of single signal monitoring for the wear state of vibration drilling bit, a multisignal acquisition system for the wear state of ultrasonic axial vibration drilling bit is built to collect the drilling force, vibration, and acoustic emission signals under three different wear states. The drilling force, vibration and acoustic emission signals of the bit in the drilling process are processed by using wavelet decomposition technology, and the signals are extracted from the wear state of the bit, The wavelet energy coefficient with high state correlation is used as the feature parameter to identify the bit wear state. The feature parameter is trained by the combination of noise assisted LMD method and BP neural network. The experiment of single signal and multisignal fusion monitoring bit wear state is carried out, and the neural network structure is optimized according to the error. The results show that the accuracy of monitoring bit wear with a single signal of drilling force is 83.3%, the accuracy of monitoring bit wear with a single signal of vibration is 91.6%, the accuracy of monitoring bit wear with a single signal of acoustic emission is 91.6%, and the accuracy of monitoring bit wear with multisignal fusion is 95.8%; when the number of network layer is 4, the vibration is monitored with the fusion of force signal, acoustic emission signal, and vibration signal The accuracy of the state of drilling tool is up to 100%. The structure model of neural network is optimized reasonably to improve the recognition rate of bit wear in vibration drilling.

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“…Extensive research has been performed to establish methods for monitoring tool condition in milling operations. In these methods, various signals such as acoustic emission, machine vibration, cutting force, motor torque and motor current have been used to predict tool condition [10–16]. A detailed study of the correlation between tool wear and many of these signal types is presented in [17].…”

Section: Introductionmentioning

confidence: 99%

A Data Processing Pipeline for Prediction of Milling Machine Tool Condition from Raw Sensor Data

Ferguson

Bhinge²,

Park

et al. 2018

Smart Sustain. Manuf. Syst.

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With recent advances in sensor and computing technology, it is now possible to use real-time machine learning techniques to monitor the state of manufacturing machines. However, making accurate predictions from raw sensor data is still a difficult challenge. In this work, a data processing pipeline is developed to predict the condition of a milling machine tool using raw sensor data. Acceleration and audio time series sensor data is aggregated into blocks that correspond to the individual cutting operations of the Computer Numerical Control (CNC) milling machine. Each block of data is preprocessed using well-known and computationally efficient signal processing techniques. A novel kernel function is proposed to approximate the covariance between preprocessed blocks of time series data. Several Gaussian process regression models are trained to predict tool condition, each with a different covariance kernel function. The model with the novel covariance function outperforms the models that use more common covariance functions. The trained models are expressed using the Predictive Model Markup Language (PMML), where possible, to demonstrate how the predictive model component of the pipeline can be represented in a standardized form. The tool condition model is shown to be accurate, especially when predicting the condition of lightly worn tools.

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A multi-sensor approach to the monitoring of end milling operations

Cited by 41 publications

References 8 publications

On the Estimation of Tool-wear for Milling Operations based on Multi- Sensorial Data

On the Estimation of Tool-wear for Milling Operations based on Multi- Sensorial Data

Wear Identification of Vibration Drilling Bit Based on Improved LMD and BP Neural Network

A Data Processing Pipeline for Prediction of Milling Machine Tool Condition from Raw Sensor Data

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