基于Kriging-PSO智能算法优化焊接工艺参数
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摘要
焊接工艺参数是影响焊接成型质量的关键因素.由于工艺参数和焊接接头的力学性能之间的关系是多维隐式的,因此,提出了一种Kriging模型和粒子群相结合的优化算法,解决了在交流钨极氩弧焊中3. 5mm厚镁合金薄板的工艺参数优化问题.首先通过田口正交法构建样本集,其次建立输出和输入之间的Kriging代理模型,并通过提出的算法获得最优工艺参数组合及其力学性能.结果表明:通过该算法获得的最优工艺参数组合,其对应的焊接接头的抗拉强度、屈服强度和平均显微硬度分别达到母材的97. 6%,98%和91. 5%,减少了经济和时间成本,提高了焊接工艺设计能力.
Welding process parameters are the key factors affecting the quality of welding. Since the relationship between process parameters and the mechanical properties of welded joints is multi-dimensional and implicit,an optimization algorithm combining Kriging model and particle swarm optimization is proposed to optimize the process parameters of 3. 5 mm magnesium alloy sheet in AC_TIG welding. Firstly,the sample set is constructed by Taguchi orthogonal method.Secondly,the Kriging surrogate model is established between output and input,and then the optimal combination of process parameters and its mechanical properties are obtained by the proposed algorithm. The results showthat such optimal process parameters as tensile strength,yield strength and average micro-hardness of the welded joints reach 97. 6%,98% and 91. 5% of the base metal respectively. The proposed algorithm not only reduces economic and time costs,but also improves the welding process design capabilities.
Welding process parameters are the key factors affecting the quality of welding. Since the relationship between process parameters and the mechanical properties of welded joints is multi-dimensional and implicit,an optimization algorithm combining Kriging model and particle swarm optimization is proposed to optimize the process parameters of 3. 5 mm magnesium alloy sheet in AC_TIG welding. Firstly,the sample set is constructed by Taguchi orthogonal method.Secondly,the Kriging surrogate model is established between output and input,and then the optimal combination of process parameters and its mechanical properties are obtained by the proposed algorithm. The results showthat such optimal process parameters as tensile strength,yield strength and average micro-hardness of the welded joints reach 97. 6%,98% and 91. 5% of the base metal respectively. The proposed algorithm not only reduces economic and time costs,but also improves the welding process design capabilities.
引文
[1]徐河,刘静安,谢水生.镁合金制备与加工技术[M].北京:冶金工业出版社,2007.(Xu He,Liu Jing-an,Xie Shui-sheng.Magnesium alloy preparation and processing technology[M].Beijing:M etallurgical Industry Press,2007.)
[2]Kulekci M K.Magnesium and its alloys applications in automotive industry[J].International Journal of Advanced Manufacturing Technology,2009,39(9/10):851-865.
[3]Feng J L,Sun Z L,Sun H Z,et al.Optimization of structure parameters for angular contact ball bearings based on Kriging model and particle sw arm optimization algorithm[J].Proceedings of the Institution of Mechanical Engineers,Part C:Journal of Mechanical Engineering Science,2017,231(23):4298-4308.
[4]Singh A,Cooper D E,Blundell N J,et al.Gibbons,modelling of w eld-bead geometry and hardness profile in laser w elding of plain carbon steel using neural netw orks and genetic algorithms[J].International Journal of Computer Integrated Manufacturing,2014,27(7):656-674.
[5]Korra N N,Vasudevan M,Balasubramanian K R.Multiobjective optimization of activated tungsten inert gas welding of duplex stainless steel using response surface methodology[J].International Journal of Advanced Manufacturing Technology,2015,77(1/2/3/4):67-81.
[6]Sathiya P,Panneerselvam K,Jaleel M Y A.Optimization of laser welding process parameters for super austenitic stainless steel using artificial neural networks and genetic algorithm[J].Materials&Design,2012,36:490-498.
[7]Lin H.The use of the Taguchi method with grey relational analysis and a neural network to optimize a novel GMA welding process[J].Journal of Intelligent Manufacturing,2012,23(5):1671-1680.
[8]Gao Z,Shao X,Jiang P,et al.Parameters optimization of hybrid fiber laser-arc butt welding on 316L stainless steel using Kriging model and GA[J].Optics&Laser Technology,2016,83:153-162.
[9]Wang J,Sun Z.The stepwise accuracy-improvement strategy based on the Kriging model for structural reliability analysis[J].Structural&Multidisciplinary Optimization,2018(4):1-18.
[10]Yang G.A modified particle swarm optimizer algorithm[C]//International Conference on Electronic Measurement.Xi’an,2007:675-679.
[2]Kulekci M K.Magnesium and its alloys applications in automotive industry[J].International Journal of Advanced Manufacturing Technology,2009,39(9/10):851-865.
[3]Feng J L,Sun Z L,Sun H Z,et al.Optimization of structure parameters for angular contact ball bearings based on Kriging model and particle sw arm optimization algorithm[J].Proceedings of the Institution of Mechanical Engineers,Part C:Journal of Mechanical Engineering Science,2017,231(23):4298-4308.
[4]Singh A,Cooper D E,Blundell N J,et al.Gibbons,modelling of w eld-bead geometry and hardness profile in laser w elding of plain carbon steel using neural netw orks and genetic algorithms[J].International Journal of Computer Integrated Manufacturing,2014,27(7):656-674.
[5]Korra N N,Vasudevan M,Balasubramanian K R.Multiobjective optimization of activated tungsten inert gas welding of duplex stainless steel using response surface methodology[J].International Journal of Advanced Manufacturing Technology,2015,77(1/2/3/4):67-81.
[6]Sathiya P,Panneerselvam K,Jaleel M Y A.Optimization of laser welding process parameters for super austenitic stainless steel using artificial neural networks and genetic algorithm[J].Materials&Design,2012,36:490-498.
[7]Lin H.The use of the Taguchi method with grey relational analysis and a neural network to optimize a novel GMA welding process[J].Journal of Intelligent Manufacturing,2012,23(5):1671-1680.
[8]Gao Z,Shao X,Jiang P,et al.Parameters optimization of hybrid fiber laser-arc butt welding on 316L stainless steel using Kriging model and GA[J].Optics&Laser Technology,2016,83:153-162.
[9]Wang J,Sun Z.The stepwise accuracy-improvement strategy based on the Kriging model for structural reliability analysis[J].Structural&Multidisciplinary Optimization,2018(4):1-18.
[10]Yang G.A modified particle swarm optimizer algorithm[C]//International Conference on Electronic Measurement.Xi’an,2007:675-679.
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