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基于PSO-OSSART算法的非轴对称电弧发射系数重建质量评价

洪海涛, 王璐, 韩永全, 昌乐

洪海涛, 王璐, 韩永全, 昌乐. 基于PSO-OSSART算法的非轴对称电弧发射系数重建质量评价[J]. 焊接学报, 2022, 43(7): 63-68. DOI: 10.12073/j.hjxb.20210823001
引用本文: 洪海涛, 王璐, 韩永全, 昌乐. 基于PSO-OSSART算法的非轴对称电弧发射系数重建质量评价[J]. 焊接学报, 2022, 43(7): 63-68. DOI: 10.12073/j.hjxb.20210823001
HONG Haitao, WANG Lu, HAN Yongquan, CHANG Le. Reconstructed quality evaluation of asymmetric arc emission coefficient by PSO-OSSART algorithm[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(7): 63-68. DOI: 10.12073/j.hjxb.20210823001
Citation: HONG Haitao, WANG Lu, HAN Yongquan, CHANG Le. Reconstructed quality evaluation of asymmetric arc emission coefficient by PSO-OSSART algorithm[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(7): 63-68. DOI: 10.12073/j.hjxb.20210823001

基于PSO-OSSART算法的非轴对称电弧发射系数重建质量评价

基金项目: 国家自然科学基金资助项目(51665044);内蒙古自治区自然科学基金资助项目(2019LH05017);内蒙古自治区科技计划项目(2020GG0313).
详细信息
    作者简介:

    洪海涛,博士,讲师;主要从事高能束焊接机理方面的研究;Email: honghaitao@imut.edu.cn

  • 中图分类号: TG 403

Reconstructed quality evaluation of asymmetric arc emission coefficient by PSO-OSSART algorithm

  • 摘要: 针对有序子集-联合重建算法(ordered subsets-simultaneous algebraic reconstruction technique, OSSART)重建参数选取主观性强的不足,提出以重建区域误差最小为适应度的随机优化粒子群算法(particle swarm optimization, PSO)来获取最佳重建参数,并对非轴对称电弧发射系数稀疏角度重建质量进行评价. 结果表明,与最大似然函数-期望值最大化算法相比,基于粒子群的OSSART算法不仅能够在大投射角度间隔条件下使重建误差明显降低,而且具有更强的边缘保持能力,能够有效提高电弧中心区域的重建质量.采用OSSART算法,应在180°范围内至少等间距采集6次特征谱线投影,才能保证变极性等离子-熔化极气体保护复合焊(variable polarity plasma arc-metal inert gas, VPPA-MIG复合焊)电弧发射系数场的重建质量.试验结果为非轴对称电弧可靠光谱诊断提供理论依据.
    Abstract: Due to the shortcomings of strong subjectivity for selecting the parameters of ordered subsets-simultaneous algebraic reconstruction technique(OSSART), in this paper, a random particle swarm optimization algorithm with reconstruction area minimal error as fitness function is proposed to obtain the best reconstruction parameters. The quality of reconstruction from sparse angle of the asymmetric arc emission coefficient is evaluated. The results show that, compared with the maximum likelihood expectation maximum (MLEM) algorithm, the OSSART algorithm based on particle swarm optimization can not only reduce the reconstruction error significantly under the condition of large projection angle interval, but also have a stronger edge retention ability, which can effectively improve the reconstruction quality of the central area of the arc. In order to ensure the reconstructed quality of VPPA-MIG hybrid welding arc emission coefficient using OSSART algorithm, six feature line projections with equal spacing should be collected at least in the range of 180 degrees. The results provide a theoretical basis for the reliable study of asymmetric arc spectroscopic diagnostics.
  • 图  1   VPPA-MIG复合焊电弧特征谱线图像

    Figure  1.   Characteristic spectrum image of VPPA-MIG hybrid welding arc

    图  2   不同电弧高度的谱线强度分布

    Figure  2.   Spectrum intensity distribution at different arc heights. (a) section of 1 mm below the nozzle; (b) section of 9 mm below the nozzle

    图  3   模拟发射系数分布

    Figure  3.   Distribution of simulated emission coefficients. (a) model A; (b) model B

    图  4   迭代过程中重建误差的变化

    Figure  4.   Changes in reconstructed error during iterations. (a) OSSART algorithm of reconstructed model A; (b) MLEM algorithm of reconstructed model A; (c) OSSART algorithm of reconstructed model B; (d) MLEM algorithm of reconstructed model B

    图  5   不同投射角度间隔下模型A的发射系数分布中间行的重建质量

    Figure  5.   Reconstructed quality of the emission distribution middle row of model A at different projection angle intervals. (a) projection angle interval 20°; (b) projection angle interval 30°; (c) projection angle interval 35°; (d) projection angle interval 40°

    图  6   不同投射角度间隔下模型B的发射系数分布中间行的重建质量

    Figure  6.   Reconstructed quality of the emission distribution middle row of model B at different projection angle intervals. (a) projection angle interval 20°; (b) projection angle interval 30°; (c) projection angle interval 35°; (d) projection angle interval 40°

    表  1   粒子群算法参数

    Table  1   Parameters for particle swarm optimization

    种群规模N(个)粒子维度D最大惯性权重wmax最小惯性权重wmin最大迭代次数kmax(次)学习因子c
    4030.950.40502
    下载: 导出CSV

    表  2   发射系数模型参数

    Table  2   Parameters of emission coefficient model

    模型幅值 E(a.u.)标准差 σ(a.u.)曲线中心 b/mm
    符号取值符号取值符号取值
    AE1205.90σ10.42b1−1.28
    E270.70σ20.15
    BE3225.80σ32.00b31.10
    E4164.60σ40.87b4−2.09
    下载: 导出CSV

    表  3   OSSART算法重建参数

    Table  3   Reconstructed parameters of OSSART algori thm

    模型投射角度
    间隔θ/(°)
    子集数
    T/unit
    松弛因子
    λ(a. u. )
    松弛因子衰减
    系数λr(a. u. )
    A10186.01980.9924
    15122.36340.9986
    2095.43910.9931
    2585.45750.9885
    3066.13090.9907
    3563.81990.9957
    4053.42860.9792
    B10100.52731
    1570.64701
    2096.25360.9684
    2582.78280.9844
    3062.81020.9874
    3566.95300.9856
    4052.76300.9949
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-08-22
  • 网络出版日期:  2022-04-25
  • 刊出日期:  2022-07-24

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