Parameters optimization of laser brazing in crimping butt using Taguchi and BPNN-GA

Rong, Youmin; Zhang, Zhen; Zhang, Guojun; Chen, Yue; Gu, Ya-fei; Huang, Yu; Wang, Chunming; Shao, Xinyu

doi:10.1016/j.optlaseng.2014.10.009

Cited by 59 publications

(14 citation statements)

References 29 publications

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“…The optimal values of the process parameters, LP, WS, FP and GAP, are 2500.974 W, 3.491 m/min, -0.856 and 0.021 mm, respectively, as presented in Table 8. The obtained optimum F is 232.041 lm and the optimized values are in line with the existing literatures [10,32]. …”

Section: The Optimization Resultssupporting

confidence: 88%

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Process modeling and parameter optimization using radial basis function neural network and genetic algorithm for laser welding of dissimilar materials

Shao

Jiang

et al. 2015

Appl. Phys. A

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The welded joints of dissimilar materials have been widely used in automotive, ship and space industries. The joint quality is often evaluated by weld seam geometry, microstructures and mechanical properties. To obtain the desired weld seam geometry and improve the quality of welded joints, this paper proposes a process modeling and parameter optimization method to obtain the weld seam with minimum width and desired depth of penetration for laser butt welding of dissimilar materials. During the process, Taguchi experiments are conducted on the laser welding of the low carbon steel (Q235) and stainless steel (SUS301L-HT). The experimental results are used to develop the radial basis function neural network model, and the process parameters are optimized by genetic algorithm. The proposed method is validated by a confirmation experiment. Simultaneously, the microstructures and mechanical properties of the weld seam generated from optimal process parameters are further studied by optical microscopy and tensile strength test. Compared with the unoptimized weld seam, the welding defects are eliminated in the optimized weld seam and the mechanical properties are improved. The results show that the proposed method is effective and reliable for improving the quality of welded joints in practical production.

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Section: The Optimization Resultssupporting

confidence: 88%

“…It has been successfully used in a wide variety of fields such as mixed model assembly, design optimization, process parameters optimization, and so on [27][28][29][30][31][32]. The GA operates over a set of individuals which is generated in the search space randomly.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Process modeling and parameter optimization using radial basis function neural network and genetic algorithm for laser welding of dissimilar materials

Shao

Jiang

et al. 2015

Appl. Phys. A

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“…Then, two different methods, namely BPNN-GA and NSGA-II (coded by using Matlab 2014a), are proposed to optimize the process parameters of WEDM for obtaining the optimal surface integrity with minimizing SR,WLT, and SCD, which is the main objective of this research. Moreover, these two approaches have proven their effectiveness because they have been successfully implemented to find out the optimal parameters on material removal rate (MRR) and 3D surface quality of WEDM [11][12][13] or to optimize the process of laser brazing [14]. Finally, the confirmation experiment is conducted to verify the efficiency of these methods applied in optimizing surface integrity characteristics.…”

Section: Outline Of the Workmentioning

confidence: 99%

Optimization of process parameters on surface integrity in wire electrical discharge machining of tungsten tool YG15

Zhang

Ming

Huang

et al. 2015

Int J Adv Manuf Technol

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This study mainly investigates the effect and optimization of process parameters on surface integrity including white layer thickness and crack density in wire electrical discharge machining (WEDM) of tungsten tool YG15 which is one of the most important hardened stainless steel alloys used in the mold industries. In this paper, four input process parameters including pulse-on time, pulse current, water pressure, and feed rate were set during WEDM experiment, and three output characteristics including surface roughness (SR), white layer thickness (WLT), and surface crack density (SCD) were taken as the performance criteria of surface integrity. Then an experiment for central composite design (CCD) of processing the tungsten tool YG15 has been conducted according to response surface methodology (RSM). After analyzing the experimental results and optimizing the WEDM process using two different methods namely backpropagation neural network combined with genetic algorithm (BPNN-GA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the optimal solution can be obtained. The analysis results manifest that the pulseon time and pulse current have a significant effect on the SR, WLT, and SCD. Moreover, several confirmation tests were carried out to verify the efficiency of the optimization methods, and then the more appropriate method was demonstrated by the comparison of optimization results. According to analysis and discussion of results, the most suitable process parameter combinations can be obtained to guide the actual machine, which contribute to increase the surface integrity and accuracy of WEDM and simultaneously reduce the ratio of disqualification for industrial application.

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“…Despite the fact that evolutionary algorithms do not guarantee the global optimum, their convergence speeds to the optimal results (nearly global) are better than those of the traditional techniques. Thus, evolutionary algorithms have been used for optimization of real-world problems in many applications instead of traditional techniques [44][45][46][47]. Therefore, genetic algorithm was implemented to solve the process parameter selection and optimization problem.…”

Section: Process Parameters Selection and Optimisationmentioning

confidence: 99%

Laser dimpling process parameters selection and optimization using surrogate-driven process capability space

Özkat

Franciosa

Ceglarek

2017

Optics & Laser Technology

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Parameters optimization of laser brazing in crimping butt using Taguchi and BPNN-GA

Cited by 59 publications

References 29 publications

Process modeling and parameter optimization using radial basis function neural network and genetic algorithm for laser welding of dissimilar materials

Process modeling and parameter optimization using radial basis function neural network and genetic algorithm for laser welding of dissimilar materials

Optimization of process parameters on surface integrity in wire electrical discharge machining of tungsten tool YG15

Laser dimpling process parameters selection and optimization using surrogate-driven process capability space

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