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Research Papers

Energy Efficiency State Identification Based on Continuous Wavelet Transform—Fast Independent Component Analysis

[+] Author and Article Information
Yun Cai, Xinhua Shi, Jianjian Yuan

School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China

Hua Shao

School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: caiyun@sjtu.edu.cn

1Corresponding author.

Manuscript received April 30, 2018; final manuscript received September 21, 2018; published online December 24, 2018. Assoc. Editor: Karl R. Haapala.

J. Manuf. Sci. Eng 141(2), 021012 (Dec 24, 2018) (10 pages) Paper No: MANU-18-1282; doi: 10.1115/1.4041568 History: Received April 30, 2018; Revised September 21, 2018

In metal cutting operations, energy efficiency can have significant consequences for the environment and for sustainable development (such as ever-increasing demand for cost saving and quality improvements), particularly when the processes are practiced on a very large scale. The energy efficiency state is a cutting process condition that coexists with other conditions such as cutter state, workpiece quality state, or machine tool state. It must be monitored by operators to avoid system failure of low energy efficiency state, on-line energy efficiency state monitoring is becoming more and more important in intelligent manufacturing and green manufacturing. The idea of energy efficiency state identification is proposed and the monitoring strategy of energy efficiency state is established for this subject. A combined application method of continuous wavelet transform (CWT) and fast independent component analysis (FICA) is proposed for feature extraction of low or high energy efficiency state. The feature of energy efficiency state is extracted by CWT on the premise of determining the state of high and low energy efficiency based on modeling of energy efficiency state and experiment data. The feature signal is reconstructed by FICA and the reconstruction signal is verified by short time Fourier transform (STFT). The feature tracing of cutting system is carried out. It is illustrated that the feature of energy efficiency state can be extracted and the different energy efficiency states also can be identified for milling processes. The proposed method will be helpful for energy efficiency state monitoring.

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Figures

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Fig. 1

Block diagram of the strategy

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Fig. 2

The experiment system. 1 Milling machine tool, 2 Cutter, 3 work piece, 4 Power sensor, 5 Force sensor, 6 Acquisition card, 7 PC.

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Fig. 3

The experiment result based on empirical model. (The left coordinate axis is specific energy consumption, the right coordinate axis is the instantaneous energy efficiency and the abscissa is material removal rate).

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Fig. 4

The time domain signals of energy efficiency state: (a) low energy efficiency state (150/12/1) and (b) high energy efficiency state (760/270/1)

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Fig. 5

Energy efficiency feature based on CWT: (a) low energy efficiency state (150/12/1) and (b) high energy efficiency state (760/270/1)

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Fig. 6

Energy efficiency feature based on FICA: (a) low energy efficiency state (150/12/1) and (b) high energy efficiency state (760/270/1)

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Fig. 7

Energy efficiency feature based on STFT: (a) low energy efficiency state (150/12/1) and (b) high energy efficiency state (760/270/1)

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Fig. 8

The worn gear of main spindle

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