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Shanben Chen - One of the best experts on this subject based on the ideXlab platform.

  • automated control of welding penetration based on audio sensing technology
    Journal of Materials Processing Technology, 2017
    Co-Authors: Shanben Chen
    Abstract:

    Abstract This paper presents a technology for welding quality control in pulse gas tungsten arc welding (GTAW). An automated welding penetration control system and effective controller are designed to achieve real-time collection and analysis of the welding acoustic Signal. A special preprocessing method called “auditory attention” is proposed to optimize the extraction of the arc Sound Signal, which includes region of interest (ROI) extraction and denoising. The penetration feature extraction is implemented in the preprocessed Signal. A Sound channel feature based on linear prediction cepstrum coefficient (LPCC) is proposed for inclusion in the feature extraction method. Using these penetration features, a typical back propagation artificial neural network (BPANN) prediction model is introduced for identification of the penetration state during the welding process. Through training using a large number of data, the prediction rate reached 80–90%. The BPANN-piecewise (BPANN-PW) controller is used to achieve online control of welding penetration via arc Sound Signal for pulse GTAW welding using work-piece of different shapes. The results showed that this controller could adjust the welding current accurately and promptly depending on the variation of the arc Sound Signal. The controlling effect was good for the online monitoring of automated robotic GTAW welding.

  • real time control of welding penetration during robotic gtaw dynamical process by audio sensing of arc length
    The International Journal of Advanced Manufacturing Technology, 2014
    Co-Authors: Jiyong Zhong, Huabin Chen, Tao Lin, Shanben Chen
    Abstract:

    This paper presents a new technology of real-time arc length monitoring and sag-depression prediction through arc Sound Signal, which is essential to realize the welding penetration control during gas tungsten arc welding of arc length. A set of automatic measurement and control system have been proposed to achieve real-time arc length control via audio sensing system. After preprocessing of arc Sound Signal, the piecewise linear models of arc Sound Signal were established under two different arc length variation 3–4 and 4–5–6 mm, analyzing the prediction errors of linear model, which were proved to be good enough for online monitoring of arc length in pulse GTAW. Based on the linear relationship between arc Sound and arc length, the linear fitting model was implemented on predicting the surface height of weld pool. A segmented self-adaptive PID controller was proposed to achieve the monitoring and controlling of arc length;, the confirmatory experiments have been designed to test the control effect of arc-length monitoring based on arc Sound Signal.

  • audio sensing and modeling of arc dynamic characteristic during pulsed al alloy gtaw process
    Sensor Review, 2013
    Co-Authors: Na Lv, Zhifen Zhang, Yanling Xu, Jifeng Wang, Bo Chen, Shanben Chen
    Abstract:

    Purpose – The purpose of this paper is to study the relationship between arc Sound Signal and arc height through arc Sound features of GTAW welding, which is aimed at laying foundation work for monitoring the welding penetration and quality by using the arc Sound Signal in the future.Design/methodology/approach – The experiment system is based on GTAW welding with acoustic sensor and Signal conditioner on it. The arc Sound Signal was first processed by wavelet analysis and wavelet packet analysis designed in this research. Then the features of arc Sound Signal were extracted in time domain, frequency domain, for example, short‐term energy, AMDF, mean strength, log energy, dynamic variation intensity, short‐term zero rate and the frequency features of DCT coefficient, also the wavelet packet coefficient. Finally, a ANN (artificial neural networks) prediction model was built up to recognize different arc height through arc Sound Signal.Findings – The statistic features and DCT coefficient can be absolutely ...

  • research on detection of welding penetration state during robotic gtaw process based on audible arc Sound
    Industrial Robot-an International Journal, 2013
    Co-Authors: Na Lv, Huabin Chen, Jiyong Zhong, Yanling Xu, Jifeng Wang, Shanben Chen
    Abstract:

    – Penetration state is one of the most important factors for judging the quality of a gas tungsten arc welding (GTAW) joint. The purpose of this paper is to identify and classify the penetration state and welding quality through the features of arc Sound Signal during robotic GTAW process., – This paper tried to make a foundation work to achieve on‐line monitoring of penetration state to weld pool through arc Sound Signal. The statistic features of arc Sound under different penetration states like partial penetration, full penetration and excessive penetration were extracted and analysed, and wavelet packet analysis was used to extract frequency energy at different frequency bands. The prediction models were established by artificial neural networks based on different features combination., – The experiment results demonstrated that each feature in time and frequency domain could react the penetration behaviour, arc Sound in different frequency band had different performance at different penetration states and the prediction model established by 23 features in time domain and frequency domain got the best prediction effect to recognize different penetration states and welding quality through arc Sound Signal., – This paper tried to make a foundation work to achieve identifying penetration state and welding quality through the features of arc Sound Signal during robotic GTAW process. A total of 23 features in time domain and frequency domain were extracted at different penetration states. And energy at different frequency bands was proved to be an effective factor for identifying different penetration states. Finally, a prediction model built by 23 features was proved to have the best prediction effect of welding quality.

N C Karmaka - One of the best experts on this subject based on the ideXlab platform.

  • estimating rock properties using Sound Signal dominant frequencies during diamond core drilling operations
    Journal of rock mechanics and geotechnical engineering, 2019
    Co-Authors: Ch Vijaya Kuma, Ch. S. N. Murthy, Harsha Vardha, N C Karmaka
    Abstract:

    Abstract In many engineering applications such as mining, geotechnical and petroleum industries, drilling operation is widely used. The drilling operation produces Sound by-product, which could be helpful for preliminary estimation of the rock properties. Nevertheless, determination of rock properties is very difficult by the conventional methods in terms of high accuracy, and thus it is expensive and time-consuming. In this context, a new technique was developed based on the estimation of rock properties using dominant frequencies from Sound pressure level generated during diamond core drilling operations. First, Sound pressure level was recorded and Sound Signals of these Sound frequencies were analyzed using fast Fourier transform (FFT). Rock drilling experiments were performed on five different types of rock samples using computer numerical control (CNC) drilling machine BMV 45 T20. Using simple linear regression analysis, mathematical equations were developed for various rock properties, i.e. uniaxial compressive strength, Brazilian tensile strength, density, and dominant frequencies of Sound pressure level. The developed models can be utilized at early stage of design to predict rock properties.

Sandip Bhattacharya - One of the best experts on this subject based on the ideXlab platform.

  • investigation on arc Sound and metal transfer modes for on line monitoring in pulsed gas metal arc welding
    Journal of Materials Processing Technology, 2010
    Co-Authors: Sandip Bhattacharya
    Abstract:

    Abstract The arc Sound was found to be strongly related to both process parameters and weld quality, like voltage and current Signals, in gas metal arc welding. In this investigation, the acquired welding arc Sound Signal along with current and voltage Signals were analyzed in time domain as well as frequency domain to correlate them with the various process parameters and metal transfer modes. The arc Sound of continuous as well as pulsed gas metal arc welding at various process parameters was also compared. A major variation of auxiliary arc Sound frequency peaks was observed due to change of pulse shape as evidenced by frequency domain analysis. The arc Sound was also used to detect welding defects.

Ch. S. N. Murthy - One of the best experts on this subject based on the ideXlab platform.

  • estimating rock properties using Sound Signal dominant frequencies during diamond core drilling operations
    Journal of rock mechanics and geotechnical engineering, 2019
    Co-Authors: Ch Vijaya Kuma, Ch. S. N. Murthy, Harsha Vardha, N C Karmaka
    Abstract:

    Abstract In many engineering applications such as mining, geotechnical and petroleum industries, drilling operation is widely used. The drilling operation produces Sound by-product, which could be helpful for preliminary estimation of the rock properties. Nevertheless, determination of rock properties is very difficult by the conventional methods in terms of high accuracy, and thus it is expensive and time-consuming. In this context, a new technique was developed based on the estimation of rock properties using dominant frequencies from Sound pressure level generated during diamond core drilling operations. First, Sound pressure level was recorded and Sound Signals of these Sound frequencies were analyzed using fast Fourier transform (FFT). Rock drilling experiments were performed on five different types of rock samples using computer numerical control (CNC) drilling machine BMV 45 T20. Using simple linear regression analysis, mathematical equations were developed for various rock properties, i.e. uniaxial compressive strength, Brazilian tensile strength, density, and dominant frequencies of Sound pressure level. The developed models can be utilized at early stage of design to predict rock properties.

  • Quantification of Rock Properties Using Frequency Analysis During Diamond Core Drilling Operations
    Journal of The Institution of Engineers (India): Series D, 2019
    Co-Authors: Ch. Vijaya Kumar, Harsha Vardhan, Ch. S. N. Murthy
    Abstract:

    Rock drilling is one of the most essential operations in mining and allied industries. This study focuses on the quantification of physico-mechanical rock properties using dominant frequencies from the Sound Signal generated through diamond core drilling operations. The rock drilling experiments were performed on five different types of rock samples using a computer numerical control drilling machine. Using simple linear regression analysis, satisfactory mathematical equations were developed between various physico-mechanical rock properties, namely, uniaxial compressive strength, Brazilian tensile strength, density and dominant frequencies of Sound level were generated during diamond core drilling operations. The developed models can be utilised for quantification of rock properties with an acceptable degree of accuracy in realistic applications.

Tianshuang Qiu - One of the best experts on this subject based on the ideXlab platform.

  • optimum heart Sound Signal selection based on the cyclostationary property
    Computers in Biology and Medicine, 2013
    Co-Authors: Tianshuang Qiu, Hong Tang
    Abstract:

    Noise often appears in parts of heart Sound recordings, which may be much longer than those necessary for subsequent automated analysis. Thus, human intervention is needed to select the heart Sound Signal with the best quality or the least noise. This paper presents an automatic scheme for optimum sequence selection to avoid such human intervention. A quality index, which is based on finding that sequences with less random noise contamination have a greater degree of periodicity, is defined on the basis of the cyclostationary property of heart beat events. The quality score indicates the overall quality of a sequence. No manual intervention is needed in the process of subsequence selection, thereby making this scheme useful in automatic analysis of heart Sound Signals.

  • separation of heart Sound Signal from noise in joint cycle frequency time frequency domains based on fuzzy detection
    IEEE Transactions on Biomedical Engineering, 2010
    Co-Authors: Hong Tang, Ting Li, Yongwan Park, Tianshuang Qiu
    Abstract:

    Noise is generally unavoidable during recordings of heart Sound Signal. Therefore, noise reduction is one of the important preprocesses in the analysis of heart Sound Signal. This was achieved in joint cycle frequency-time-frequency domains in this study. Heart Sound Signal was decomposed into components (called atoms) characterized by time delay, frequency, amplitude, time width, and phase. It was discovered that atoms of heart Sound Signal congregate in the joint domains. On the other hand, atoms of noise were dispersed. The atoms of heart Sound Signal could, therefore, be separated from the atoms of noise based on fuzzy detection. In a practical experiment, heart Sound Signal was successfully separated from lung Sounds and disturbances due to chest motion. Computer simulations for various clinical heart Sound Signals were also used to evaluate the performance of the proposed noise reduction. It was shown that heart Sound Signal can be reconstructed from simulated complex noise (perhaps non-Gaussian, nonstationary, and colored). The proposed noise reduction can recover variations in the both waveform and time delay of heart Sound Signal during the reconstruction. Correlation coefficient and normalized residue were used to indicate the closeness of the reconstructed and noise-free heart Sound Signal. Correlation coefficient may exceed 0.90 and normalized residue may be around 0.10 in 0-dB noise environment, even if the phonocardiogram Signal covers only ten cardiac cycles.