Laser Cutting Defect Recognition Using Conversion of Processing Light Information into Spectrogram Images – Spectroscopic Measurements in Multiple Work Surface Conditions and Extraction of Spectral Data Features Based on Processing Principle –

Mizuki Ishiguro; Rui Fukui; Shin’ichi Warisawa; Naoyasu Narita; Hironobu Miyoshi

doi:10.20965/ijat.2021.p0728

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IJAT Vol.15 No.5 pp. 728-739

doi: 10.20965/ijat.2021.p0728

(2021)

Paper:

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Laser Cutting Defect Recognition Using Conversion of Processing Light Information into Spectrogram Images – Spectroscopic Measurements in Multiple Work Surface Conditions and Extraction of Spectral Data Features Based on Processing Principle –

Mizuki Ishiguro^,†, Rui Fukui^, Shin’ichi Warisawa^*, Naoyasu Narita^, and Hironobu Miyoshi^

^*The University of Tokyo
5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8563, Japan

^†Corresponding author

^**Amada Co., Ltd., Isehara, Japan

Received:

February 8, 2021

Accepted:

May 18, 2021

Published:

September 5, 2021

Keywords:

laser processing, defect detection, spectrogram, image processing, data-driven analysis

Abstract

At urban production sites, laser cutting is an essential technology for high-speed flexible sheet-metal processing. This study aims to detect defective cuts by sensing laser-cutting-induced light emission and elucidate meaningful features for processing-based detection. The proposed method comprises three steps. In the first step, the sensors installed in the laser head acquire the spectra of light generated during processing, and data analysis software converts the spectral data into spectrograms and stacked-graph images. In the second step, image processing software extracts the edges of both images and emphasizes the periodic features in normal laser cutting. In the final step, a one-class support vector machine recognizes defective cuts from the extracted features. Verification tests using multiple normal and abnormal cut data confirmed that the proposed method accurately detected defective cuts.

Cite this article as:

M. Ishiguro, R. Fukui, S. Warisawa, N. Narita, and H. Miyoshi, “Laser Cutting Defect Recognition Using Conversion of Processing Light Information into Spectrogram Images – Spectroscopic Measurements in Multiple Work Surface Conditions and Extraction of Spectral Data Features Based on Processing Principle –,” Int. J. Automation Technol., Vol.15 No.5, pp. 728-739, 2021.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] M. Tsuji, “High Power Fiber Laser and the Newest Applications,” J. of High Temperature Society, Vol.35, No.4, pp. 171-175, 2009.

[2] [2] K. A. Ghany et al., “Cutting of 1.2 mm thick austenitic stainless steel sheet using pulsed and CW Nd:YAG laser,” J. of Materials Processing Technology, Vol.168, No.3, pp. 438-447, 2005.

[3] [3] T. Arai, “Fundamental engineering science for laser materials processing,” Maruzen Publishing, 2013.

[4] [4] M. Pacher et al., “Quantitative identification of laser cutting quality relying on visual information,” Proc. of the Laser in Manufacturing Conf. 2017, pp. 1-11, 2017.

[5] [5] B. S. Yilbas, “Laser cutting quality assessment and thermal efficiency analysis,” J. of Materials Processing Technology, Vol.155-156, pp. 2106-2115, 2004.

[6] [6] P. Wen et al., “Quality detection and control during laser cutting progress with coaxial visual monitoring,” J. of Laser Applications, Vol.24, No.3, 032006, 2012.

[7] [7] K. Sakai, “Monitoring techniques of Laser Welding,” J. of the Japan Welding Society, Vol.72, No.4, pp. 256-259, 2003.

[8] [8] M. Schleiler et al., “Cross-Correlation-Based Algorithm for Monitoring Laser Cutting With High-Power Fiber Lasers,” IEEE Sensor J., Vol.18, No.4, pp. 1585-1590, 2017.

[9] [9] J. Keuster et al., “Monitoring of high-power CO₂ laser cutting by means of an acoustic microphone and photodiodes,” Int. J. of Advanced Manufacturing Technology, Vol.35, pp. 115-126, 2006.

[10] [10] G. Santolini et al., “Cut Quality Estimation in Industrial Laser Cutting Machines: A Machine Learning Approach,” Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 1-9, 2019.

[11] [11] J. E. Sansonetti and W. C. Martin, “Handbook of Basic Atomic Spectroscopic Data,” 2005. https://srd.nist.gov/jpcrdreprint/1.1800011.pdf [Accessed April 8, 2021]

[12] [12] ARIOS Inc., “Emission spectra of nitrogen molecules.” https://www.arios.co.jp/library/p21.html [Accessed December 16, 2020]

[13] [13] FINTECH, “Emissivity (absorption rate) of various materials.” https://www.fintech.co.jp/etc-data/housharitsu.htm [Accessed December 16, 2020]

[14] [14] F. P. Instruments, “Emissivity – Metal.” https://www.flukeprocessinstruments.com/ja/service-and-support/knowledge-center/infrared-technology/emissivity-metals [Accessed December 16, 2020]

[15] [15] T. Akiyama et al., “Thermal Conductivities of Dense Iron Oxides,” The J. of the Iron and Steel Institute of Japan, Vol.77, No.2, pp. 231-235, 1991.

[16] [16] J. Canny, “A Computational Approach to Edge Detection,” J. of IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.8, No.6, pp. 679-698, 1986.

Laser Cutting Defect Recognition Using Conversion of Processing Light Information into Spectrogram Images – Spectroscopic Measurements in Multiple Work Surface Conditions and Extraction of Spectral Data Features Based on Processing Principle –

Mizuki Ishiguro*,†, Rui Fukui*, Shin’ichi Warisawa*, Naoyasu Narita**, and Hironobu Miyoshi**

Mizuki Ishiguro^,†, Rui Fukui^, Shin’ichi Warisawa^*, Naoyasu Narita^, and Hironobu Miyoshi^