Laser & Optoelectronics Progress, Volume. 59, Issue 2, 0214001(2022)
A Configurable BP Neural Network Accelerator for Laser Welding Parameter Calculation
Figures & Tables(10)
Fig. 5. Running time comparison between accelerator and embedded processor platform
Fig. 6. Running time comparison of accelerator and embedded processor platform under typical number neurons and maximum number of neurons. (a) Typical number of neurons; (b) maximum number of neurons
Table 1. Typical application of artificial neural network in laser technology in recent years
View tableTable 1. Typical application of artificial neural network in laser technology in recent years
Literature Application direction Network model used Result Literature［ 9 ］Laser induced breakdown spectroscopy Radial basis function neural network The accuracy of some elements is improved Literature［ 10 ］Optimization of laser cutting parameters Convolution neural network About 92% accuracy Literature［ 11 ］Laser additive manufacturing control Alex net Effectively used in the process of image segmentation process Literature［ 12 ］Analysis of laser ranging data Deep neural network It is impossible for network to find deep information from satellite laser ranging data Literature［ 13 ］Spectral analysis BP neural network The modeling effect is improved Literature［ 14 ］Color laser marking BP neural network The feasibility is demonstrated Literature［ 15 ］Intensity calibration of laser scanner BP neural network The system response time is effectively shortened Literature［ 16 ］Laser induced breakdown spectroscopy BP neural network The results are satisfactory Literature［ 17 ］Target detection of optical genetic laser projection system Convolution neural Highly accurate detection effect is realized Literature［ 2 ］Optimization of laser welding parameters BP neural network The design goals of high precision，high quality，and high stability are realized Literature［ 18 ］Optimization of laser welding parameters BP neural network The relative error is small and the effect is good Literature［ 19 ］Laser welding control Cellular neural network The algorithm complexity are reduced and the control rate is high Literature［ 20 ］Optimization of laser cutting parameters BP neural network It has achieved obvious success
Table 2. Typical mapping relationship of parameters in data set
View tableTable 2. Typical mapping relationship of parameters in data set
Scanning speed /（m⋅min-1） Power /W Defocus /mm Weld width /μm Penetration /μm Ratio of penetration to weld width 1.3 1000 10 1037.3130 1985.92 1.914484 1.5 800 0 733.7967 1859.60 2.534217 1.5 1000 3 1482.8600 1989.30 1.341529 1.8 1000 0 819.4533 1976.73 2.412255 2.5 1500 3 1166.8770 1958.60 1.678498 3.0 1500 0 776.9867 1858.92 2.392473 5.3 2000 3 617.0433 1900.31 3.079703 5.5 2000 0 597.3000 1855.23 3.106027 7.0 2500 0 676.2867 1856.02 2.744428
Table 3. Comparison of accelerator processing speed
View tableTable 3. Comparison of accelerator processing speed
Number of neurons Processing time of bp1 tbp1 /ms Processing time of bp2 tbp2 /ms Input layer Hidden layer Output layer Accelerator Embedded processor platform Accelerator Embedded processor platform 3 4 3 0.651360 3.217697 0.557184 2.809763 3 6 4 0.645399 4.746675 0.546455 5.123854 3 8 3 0.649691 6.266117 0.546694 4.949093 3 9 3 0.665188 7.077932 0.545502 5.591869 3 9 4 0.668287 6.937027 0.582457 7.375479 3 10 2 0.664234 7.764816 0.548601 4.140139 3 10 3 0.663519 7.673979 0.619888 6.249666 3 10 4 0.653505 7.800817 0.561714 8.289099 4 4 3 0.648975 4.116058 0.545502 2.835274 4 6 4 0.647545 5.889177 0.549316 5.116224 4 8 3 0.658512 7.790327 0.544310 5.586386 4 9 3 0.666857 8.561611 0.621557 5.743742 4 9 4 0.665903 8.777618 0.571012 7.631302 4 10 2 0.678539 9.542465 0.555515 4.715443 4 10 3 0.666857 9.572506 0.617504 6.770372
Table 4. Comparison of processing speed between accelerators
View tableTable 4. Comparison of processing speed between accelerators
Number of neurons Processing time of bp1 tbp1_a /ms Processing time of bp2 tbp2_a /ms Proportion Input layer Hidden layer Output layer General accelerator Proposed accelerator General accelerator Proposed accelerator 16 32 16 1.061678 0.909328 0.926495 0.851393 0.8877 16 32 32 0.994444 0.916243 1.302719 1.132965 0.8955 32 64 32 2.145052 1.786947 2.032518 1.715899 0.8386 32 64 64 2.478838 1.778364 3.809452 2.868414 0.7352 64 128 64 6.696224 5.238771 6.526232 5.165577 0.7869 64 128 128 6.678343 5.263567 12.558460 9.703398 0.7804
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Boyu Fan, Zaifeng Shi, Zhe Wang, Shaoxiong Li, Tao Luo. A Configurable BP Neural Network Accelerator for Laser Welding Parameter Calculation[J]. Laser & Optoelectronics Progress, 2022, 59(2): 0214001
Paper Information
Category: Lasers and Laser Optics
Received: Jan. 29, 2021
Accepted: Mar. 9, 2021
Published Online: Dec. 23, 2021
The Author Email: Fan Boyu (fanboyu@tju.edu.cn), Shi Zaifeng (shizaifeng@tju.edu.cn)
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