Prediction of residual stress in selective laser melting based on neural network

Jing Yanlong; Li Jie; Shi Wentian; Yan Xiaoling

doi:10.11884/HPLPB202133.210223

Volume 33 Issue 10

Oct. 2021

Turn off MathJax

Article Contents

Article Navigation > High Power Laser and Particle Beams > 2021 > 33(10): 109001

Jing Yanlong, Li Jie, Shi Wentian, et al. Prediction of residual stress in selective laser melting based on neural network[J]. High Power Laser and Particle Beams, 2021, 33: 109001. doi: 10.11884/HPLPB202133.210223

Citation:

Jing Yanlong, Li Jie, Shi Wentian, et al. Prediction of residual stress in selective laser melting based on neural network[J]. High Power Laser and Particle Beams, 2021, 33: 109001. doi: 10.11884/HPLPB202133.210223

Citation:

PDF( 1420 KB)

Prediction of residual stress in selective laser melting based on neural network

doi: 10.11884/HPLPB202133.210223

School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China

Received Date: 2021-06-04
Rev Recd Date: 2021-09-15

Available Online: 2021-10-19

Publish Date: 2021-10-15

Abstract

Abstract

At present, numerical simulation is the main method for predicting residual stress produced by selective laser melting. However, due to different factors such as equipment, environment and powder, it is difficult to establish a practical numerical model. The accuracy of numerical simulation needs to be verified. Based on the strong ability of neural network in predicting multivariable and complex linear information processing, a model for predicting residual stress in selective laser melting (SLM) is established. SLM technology is used to print a considerable number of samples with different process parameters, and ultrasonic wave is used to detect the internal residual stress. Training samples of neural network are used to train the neural network model, and the neural network with predictive function is obtained. The process parameters of the validated samples are input into the neural network, and the predicted residual stress value is calculated, which is in accordance with the actual situation. The detection values were compared. The experimental results show that the deviation between the predicted value and the measured value is small and the accuracy is high, which verifies the effectiveness of the proposed method.The method of predicting residual stress by neural network can quickly determine the residual stress corresponding to laser melting process parameters in different selection areas, avoid setting process parameters with high residual stress, effectively shorten the cycle of preparing high-quality workpiece samples and reduce the cost.
- additive manufacturing,
- residual stress,
- neural network,
- selective laser melting,
- ultrasonic detection,
- self-adaptivity

FullText(HTML)

References(21)

References

[1]	Sing S L, An Jia, Yeong W Y, et al. Laser and electron-beam powder-bed additive manufacturing of metallic implants: a review on processes, materials and designs[J]. Journal of Orthopaedic Research, 2016, 34(3): 369-385. doi: 10.1002/jor.23075
[2]	王晨光, 沈显峰, 王国伟, 等. 金属面曝光选区激光熔化原理装置及试验研究[J]. 强激光与粒子束, 2021, 33:029001. (Wang Chenguang, Shen Xianfeng, Wang Guowei, et al. Principle device and experimental research of surface exposure selective laser melting for metal powder[J]. High Power Laser and Particle Beams, 2021, 33: 029001 doi: 10.11884/HPLPB202133.200221
[3]	龚丞, 王丽芳, 朱刚贤, 等. 激光增材制造工艺参量对熔覆层残余应力的影响[J]. 激光技术, 2019, 43(2):263-268. (Gong Chen, Wang Lifang, Zhu Gangxian, et al. Influence of process parameters on the residual stress of cladding layers by laser additive manufacturing[J]. Laser Technology, 2019, 43(2): 263-268 doi: 10.7510/jgjs.issn.1001-3806.2019.02.021
[4]	杨健, 陈静, 杨海欧, 等. 激光快速成形过程中残余应力分布的实验研究[J]. 稀有金属材料与工程, 2004, 33(12):1304-1307. (Yang Jian, Chen Jing, Yang Haiou, et al. Experimental study on residual stress distribution of laser rapid forming process[J]. Rare Metal Materials and Engineering, 2004, 33(12): 1304-1307 doi: 10.3321/j.issn:1002-185X.2004.12.017
[5]	Yan Xiaoling, Pang Jincheng, Jing Yanlong. Ultrasonic measurement of stress in SLM 316L stainless steel forming parts manufactured using different scanning strategies[J]. Materials, 2019, 12: 2719. doi: 10.3390/ma12172719
[6]	李九霄, 李鸣佩, 杨东野, 等. 选择性激光熔化制造金属构件残余应力的研究进展[J]. 机械工程材料, 2018, 42(8):1-6. (Li Jiuxiao, Li Mingpei, Yang Dongye, et al. Research progress on residual stress in metal component manufactured by selective laser melting[J]. Materials for Mechanical Engineering, 2018, 42(8): 1-6 doi: 10.11973/jxgccl201808001
[7]	杜畅, 张津, 连勇, 等. 激光增材制造残余应力研究现状[J]. 表面技术, 2019, 48(1):200-207. (Du Chang, Zhang Jin, Lian Yong, et al. Research progress on residual stress in laser additive manufacturing[J]. Surface Technology, 2019, 48(1): 200-207
[8]	梁祖磊, 孙中刚, 张少驰, 等. 数值模拟在激光选区熔化中的应用及研究现状[J]. 航空制造技术, 2018, 61(22):87-91,97. (Liang Zulei, Sun Zhonggang, Zhang Shaochi, et al. Application and research status of numerical simulation in laser selective melting[J]. Aeronautical Manufacturing Technology, 2018, 61(22): 87-91,97
[9]	倪立斌, 刘继常, 伍耀庭, 等. 基于神经网络和粒子群算法的激光熔覆工艺优化[J]. 中国激光, 2011, 38:0203003. (Ni Libin, Liu Jichang, Wu Yaoting, et al. Optimization of laser cladding process variables based on neural network and particle swarm optimization algorithms[J]. Chinese Journal of Lasers, 2011, 38: 0203003 doi: 10.3788/CJL201138.0203003
[10]	Akbari M, Saedodin S, Panjehpour A, et al. Numerical simulation and designing artificial neural network for estimating melt pool geometry and temperature distribution in laser welding of Ti6Al4V alloy[J]. Optik, 2016, 127(23): 11161-11172. doi: 10.1016/j.ijleo.2016.09.042
[11]	Li Kun, Yan Shilin, Pan Wenfeng, et al. Warpage optimization of fiber-reinforced composite injection molding by combining back propagation neural network and genetic algorithm[J]. The International Journal of Advanced Manufacturing Technology, 2017, 90(1/4): 963-970.
[12]	雷凯云, 秦训鹏, 刘华明, 等. 基于神经网络的宽带激光熔覆熔池特征参数预测[J]. 光电子·激光, 2018, 29(11):1212-1220. (Lei Kaiyun, Qin Xunpeng, Liu Huaming, et al. Prediction on characteristics of molten pool in wide-band laser cladding based on neural network[J]. Journal of Optoelectronics·Laser, 2018, 29(11): 1212-1220
[13]	姜淑娟, 刘伟军, 南亮亮. 基于神经网络的激光熔覆高度预测[J]. 机械工程学报, 2009, 45(3):269-274,281. (Jiang Shujuan, Liu Weijun, Nan Liangliang. Laser cladding height prediction based on neural network[J]. Journal of Mechanical Engineering, 2009, 45(3): 269-274,281 doi: 10.3901/JME.2009.03.269
[14]	徐浪, 潘勤学, 宿亮, 等. 焊接残余应力超声无损检测技术[J]. 计测技术, 2012, 32(6):29-32,53. (Xu Lang, Pan Qinxue, Su Liang, et al. Compensation technology for nondestructive testing of welded residual stress by ultrasonic[J]. Metrology & Measurement Technology, 2012, 32(6): 29-32,53 doi: 10.3969/j.issn.1674-5795.2012.06.008
[15]	徐春广, 宋文涛, 潘勤学, 等. 残余应力的超声检测方法[J]. 无损检测, 2014, 36(7):25-31. (Xu Chunguang, Song Wentao, Pan Qinxue, et al. Residual stress nondestructive testing method using ultrasonic[J]. Nondestructive Testing, 2014, 36(7): 25-31
[16]	Liu Huaming, Qin Xunpeng, Huang Song, et al. Geometry characteristics prediction of single track cladding deposited by high power diode laser based on genetic algorithm and neural network[J]. International Journal of Precision Engineering and Manufacturing, 2018, 19(7): 1061-1070. doi: 10.1007/s12541-018-0126-8
[17]	朱剑英. 智能系统非经典数学方法[M]. 武汉: 华中科技大学出版社, 2001 Zhu Jianying. Non-classical mathematics for intelligent systems[M]. Wuhan: Huazhong University of Science and Technology Press, 2001
[18]	王东生, 杨友文, 田宗军, 等. 基于神经网络和遗传算法的激光多层熔覆厚纳米陶瓷涂层工艺优化[J]. 中国激光, 2013, 40:0903001. (Wang Dongsheng, Yang Youwen, Tian Zongjun, et al. Process optimization of thick nanostructured ceramic coating by laser multi-layer cladding based on neural network and genetic algorithm[J]. Chinese Journal of Lasers, 2013, 40: 0903001 doi: 10.3788/CJL201340.0903001
[19]	徐春广, 李焕新, 王俊峰, 等. 残余应力的超声横纵波检测方法[J]. 声学学报, 2017, 42(2):195-204. (Xu Chunguang, Li Huanxin, Wang Junfeng, et al. Ultrasonic shear and longitudinal wave testing method of residual stress[J]. Acta Acustica, 2017, 42(2): 195-204
[20]	黄本生, 陈权, 杨江, 等. Q345/316L异种钢焊接残余应力与变形数值模拟[J]. 焊接学报, 2019, 40(2):138-144. (Huang Bensheng, Chen Quan, Yang Jiang, et al. Numerical simulation of welding residual stress and distortion in Q345/316L dissimilar steel[J]. Transactions of the China Welding Institution, 2019, 40(2): 138-144
[21]	于宝义, 何亮, 郑黎, 等. SLM成形ALSi10Mg合金残余应力数值模拟及组织性能分析[J]. 特种铸造及有色合金, 2020, 40(4):349-355. (Yu Baoyi, He Liang, Zheng Li, et al. Numerical simulation and microstructure analysis of residual stress in SLM formed AlSi10Mg[J]. Special Casting & Nonferrous Alloys, 2020, 40(4): 349-355