Abstract
With the development of information technology in hardware, more and more photosensitive elements are integrated by charge coupled device (CCD), which makes the arc spectrum data collected by linear array CCD exhibit many characteristics such as large number of dimensions, large scale and complicated structure. How to efficiently and accurately dig out the characteristic information from the rich spectral data that can be used to guide the welding process monitoring is a difficult problem that needs to be solved. Dimension reduction is one of the effective solutions. It can remove redundant data information, reduce computational complexity, improve analysis efficiency, and realize high-dimensional data classification and visualization. It is widely used in the processing of massive high-dimensional data, including images, hyperspectral, etc. In this chapter, the AC P-GTAW welding of 5A06 aluminum alloys is taken as the application background. In view of the complex and changeable arc spectral information, dimensional reduction of spectral data is conducted to analyze and compare the effects of different methods on arc spectral data. Finally, a local linear embedding (LLE) algorithm suitable for welding arc spectral data processing is selected. Aiming at solving the limitations of LLE algorithm, a supervised method based on maximum margin criterion (MMC) was proposed to extract effective features and improve the classification recognition rate. Furthermore, he relationship between these features and welding defects in the dynamic welding process was studied. Then, in order to improve the classification accuracy of pores, a statistical analysis was conducted on the hydrogen spectral lines and six characteristic values were extracted. Combining with the characteristics of light intensity ratio, a high classification accuracy of pores was realized with the support vector machine (SVM) whose optimal parameters were found based on the genetic algorithm (GA).
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Huang, Y., Chen, S. (2020). Dimension Reduction of Arc Spectrum for Porosity Detection in P-GTAW Process. In: Key Technologies of Intelligentized Welding Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-13-7549-1_5
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DOI: https://doi.org/10.1007/978-981-13-7549-1_5
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