High relative density tin bronze parts directly manufactured by selective laser melting

BAI Yu-chao; YANG Yong-qiang; WANG Di; WANG An-min

doi:10.3788/OPE.20162413.0484

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High relative density tin bronze parts directly manufactured by selective laser melting

更新时间：2020-08-13

- High relative density tin bronze parts directly manufactured by selective laser melting
- Optics and Precision Engineering Vol. 24, Issue 10s, Pages: 484-492(2016)
- 作者机构：
  
  华南理工大学机械与汽车工程学院, 广东广州 510640
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20162413.0484
  CLC： TN249;TG146.1
- Received：20 April 2016，
  
  Revised：17 May 2016，
  
  Published：14 November 2016
- 稿件说明：
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白玉超, 杨永强, 王迪等. 激光选区熔化直接成型高致密锡青铜零件[J]. 光学精密工程, 2016,24(10s): 484-492

BAI Yu-chao, YANG Yong-qiang, WANG Di etc. High relative density tin bronze parts directly manufactured by selective laser melting[J]. Editorial Office of Optics and Precision Engineering, 2016,24(10s): 484-492
白玉超, 杨永强, 王迪等. 激光选区熔化直接成型高致密锡青铜零件[J]. 光学精密工程, 2016,24(10s): 484-492 DOI： 10.3788/OPE.20162413.0484.

BAI Yu-chao, YANG Yong-qiang, WANG Di etc. High relative density tin bronze parts directly manufactured by selective laser melting[J]. Editorial Office of Optics and Precision Engineering, 2016,24(10s): 484-492 DOI： 10.3788/OPE.20162413.0484.

摘要

为了获取高致密度激光选区熔化锡青铜成型件，直接制造出具有典型曲面、薄壁、圆孔特征的锡青铜功能零件，对激光功率、扫描速度和扫描间距三个关键因素对致密度的影响进行研究。以激光选区熔化成型锡青铜成型件为研究对象，以成型件致密度为评价指标，设计三因素四水平正交实验。根据正交实验结果，通过BP神经网络和遗传算法对成型工艺参数进行优化。采用MicroCT技术和阿基米德排水法研究优化后的成型件致密性，并通过超景深立体成像技术研究其表面形貌，最后采用优化工艺参数成型具有典型结构特征的风轮模型。实验结果表明：激光功率、扫描间距、扫描速度对致密度均具有重要的影响，影响程度依次减弱；最优工艺参数为：激光功率177W，扫描速度327mm/s，扫描间距0.082mm，该工艺下样品实际测量致密度99.07%与预测值99.12%基本符合；MicroCT图像显示优化后样品内部致密性良好，无明显夹杂、间隙等缺陷；可直接成型出具有曲面、薄壁、圆孔典型特征的风轮模型，表面无球化、裂纹、悬垂物等缺陷。以正交实验为基础，采用BP神经网络与遗传算法对成型工艺参数优化是可行的，成型致密度满足要求。

Abstract

In order to obtain high density tin bronze parts and functional components with typical curved

thin-walled

round features manufactured by Selective Laser Melting

the influence of three key factors of laser power

scanning speed and scanning distance on the density of tin bronze parts manufactured by selective was researched. Design a three factors and four levels orthogonal experiment. And according to the results of experiment

the process parameters of parts are optimized by the method of BP neural network and genetic algorithm. The density of optimized parts has been researched by MicroCT and Archimedes drainage

and the surface topography of optimized parts has been studied by the technology of digital stereo imaging. Then a wind turbine model with typical structure characteristics has been manufactured by the optimized process parameters. The experimental results show that the laser power

scanning distance and scanning speed have important influence on the density of the laser

and the degree of the influence is weakened in turn. Optimum parameter is laser power of 177W

scanning speed of 327mm/s and scanning pitch of 0.082mm. under the process

the density that measured by experiment is 99.7%

which is nearly the same as the theoretical optimization results of 99.12%. So the error of the two results is very small. The MicroCT image shows that there is no obvious inclusions

gap and other defects inside the parts. The wind turbines model with the typical characteristics of curved surface

thin wall and round hole can be directly manufactured

and the surface of the part have no ball

crack

and other defects. Therefore

the method for optimizing molding process parameters

which is based on the orthogonal experiment and combined with BP neural network and genetic algorithm

is feasible. The relative density of parts meets the requirement.

关键词

Keywords

references

杨永强,王迪,吴伟辉. 金属零件选区激光熔化直接成型技术研究进展[J]. 中国激光, 2011,38(6):54-64. YANG Y Q, WANG D, WU W H. Research progress of direct manufacturing of metal parts by selective laser melting[J].Chinese Journal of Lasers, 2011,38(6):54-64.(in Chinese)

苏海军,蔚凯晨,郭伟,等. 激光快速成型技术新进展及其在高性能材料加工中的应用[J]. 中国有色金属学报, 2013,23(6):1567-1574. SU H J, WEI K CH, GUO W, et al.. New development of laser rapid forming and its application in high performance materials processing[J].The Chinese Journal of Nonferrous Metals, 2013, 23(6):1567-1574.

曾光,韩志宇,梁书锦,等. 金属零件3D打印技术的应用研究[J]. 中国材料进展, 2014, 33(6):376-382. ZENG G, HAN ZH Y, LIANG SH J, et al.. Application of metal parts by 3D printing technology[J]. Materials China, 2014, 33(6):376-382.

DAM M, SARAF D N. Design of neural networks using genetic algorithm for on-line property estimation of data mining applications[J].Computer and Chemical Engineering, 2006,30(4):722-729.

ZAREI J, POSHTAN J. Bearing fault detection using wavelet packet transform of induction motor stater current[J]. Tribology International, 2007,40(5):763-769.

LODES M A, RALFGUSCHLBAUER R,et al.. Process development for the manufacturing of 99.94% pure copper via selective electron beam melting[J]. Materials Letters. 2015,143:298-301.

POGSON S R, FOX P, SUTCLIFFE C J, et al.. The production of copper parts using DMLR[J]. Rapid Prototyping Jounral, 2003,9(5):334-343.

程大伟,杨永强,王池林,等. 铜磷合金粉末选区激光熔化成型研究[J]. 激光技术, 2009, 33(1):63-66. CHENG D W, YANG Y Q, WANG CH L, et al.. Study on copper-phosphorus alloy powder formed by selective laser melting[J]. Laser Technology, 2009, 33(1):63-66.(in Chinese)

王迪,杨永强,吴伟辉,等. 光纤激光选区熔化316L不锈钢工艺优化[J]. 中国激光, 2009,36(12):3233-3239. WANG D, YAGN Y Q, WU W H, et al.. Process optimization for 316L stainless steel by fiber laser selective melting[J].Chinese Journal of Lasers, 2009, 32(12):3233-3239.(in Chinese)

孙婷婷,杨永强,苏旭彬,等. 316L不锈钢粉末选区激光熔化成型致密化研究[J]. 激光技术,2010, 34(4):443-446. SUN T T, YANG Y Q, SU X B, et al.. Research on 316L stainless steel powder relative density by selective laser melting[J]. Laser technology, 2010, 34(4):443-446.(in Chinese)

MORGAN R, SUTCLIFFE C, ONEILL W. Destiny analysis of direct metal laser re-melted 316L stainless steel cubic primitives[J].Journal of Materials Science, 2004,39:1195-1205.

闫岸如,杨恬恬,王燕灵,等. 变能量激光选区熔化IN718镍基超合金的成形工艺及高温机械性能[J]. 光学精密工程,2015, 23(6):1695-1704. YAN A R, YANG T T, WAGN Y L, et al.. Forming process and high-temperature mechenical properties of variable energy laser selective melting manufacturing IN718 super alloy[J].Opt. Precision Eng., 2015, 23(6):1695-1704.(in Chinese)

王赟达,杨永强,宋长辉,等. 基于响应面法优化激光选区熔化成型CoCrMo合金工艺及其电化学行为[J]. 中国有色金属学报, 2014, 24(10):2497-2505. WANG Y D, YANG Y Q, SONG CH H. Process optimization and electrochemical behavior of CoCrMo alloy fabricated by selective laser melting based on response surface method[J].The Chinese Journal of Nonferrous Metals, 2014, 24(10):2497-2505.

LIU Y, YANG Y Q, MAI SH ZH, et al.. Investigation into spatter behavior during selective laser melting of ALSI 316L stainless steel powder[J].Materials&Design. 2015,87(15):797-806.

LOUVIS E,FOX P,SUTCLIFFE C J, et al.. Selective laser melting of aluminium components[J].Joural of Materials Processing Technology, 2011(211):275-284.

SUN Z, TAN X, TOR S B, et al.. Selective laser melting of stainless steel 316L with low porosity and high build rates[J].Materials & Design, 2016, 104:197-204.

闫盖,郑艳萍,张文彦,等. 基于正交实验的板料冲压成形工艺参数优化[J]. 热加工工艺, 2013, 42(17):94-97. YAN G, ZHENG Y P, ZHANG W Y, et al.. Process parameters optimization of sheet stamping forming based on orthogonal experiment[J].Hot Working Technology, 2013, 42(17):94-97.(in Chinese)

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