考虑熔融沉积成型工艺稳定性的参数优化设计

doi:10.3901/JME.2023.23.331

摘要/Abstract

摘要： 为了提高熔融沉积成型(Fused deposition modeling, FDM)工艺的生产速度和产品交货速度，需要增加3D打印设备数量或实施分布式本土化生产来扩大产能。因此，提高FDM工艺在批量化生产中的工艺稳定性和生产效率，将成为FDM技术未来广泛应用于工业领域的关键。该研究的目标是通过选择合适的FDM工艺参数设置，实现在较低的打印成本下相同型号或不同型号的3D打印机所打制件质量的一致性。首先，将高斯过程模型与裂区试验设计相结合，研究制件的压缩形变、打印时间和消耗线材的长度与关键工艺参数之间的关系。然后，考虑到工艺过程中的不确定性，利用区间数的距离来度量不同打印机所打制件压缩形变的裂区效应。最后，综合考虑裂区效应和打印成本建立多目标优化方案，获得FDM工艺的最优参数设计值。验证实验的结果说明了所提方法的有效性和可行性。该研究为实现FDM技术的标准化和产业化提供了重要思路。

关键词: 熔融沉积成型, 参数设计, 高斯过程, 裂区效应, 生产效率

Abstract: In order to improve the production speed and product delivery speed of the fused deposition modeling (FDM) process, it is necessary to increase the number of 3D printing equipment or implement distributed localization production to expand production capacity. Therefore, improving the process stability and production efficiency of the FDM process in mass production will become the key to the wide industrial application of FDM technology in the future. This study aims to achieve consistency in the quality of parts printed by the same model or different models of 3D printers at a low printing cost by selecting the appropriate FDM process parameter settings. Firstly, the Gaussian process model is combined with a split-plot experimental design to study the relationship between compressive deformation, printing time, the length of the consumed wire, and critical process parameters. Then, considering the uncertainty in the printing process, the distance of the interval number is used to measure the split-plot effect of the compressive deformation of parts printed by different printers. Finally, the multi-objective optimization scheme is established by considering the split-plot effect and printing cost to obtain the optimal parameter design value of the FDM process. The results of the validation experiments illustrate the effectiveness and feasibility of the proposed method. This study provides an important idea for the standardization and industrialization of FDM technology.

Key words: fused deposition modeling, parameter design, Gaussian process, split-plot effect, production efficiency

中图分类号:

TP391
TG156

翟翠红, 汪建均, 马义中, 冯泽彪, 任晓蕾, 丁春风. 考虑熔融沉积成型工艺稳定性的参数优化设计[J]. 机械工程学报, 2023, 59(23): 331-342.

ZHAI Cuihong, WANG Jianjun, MA Yizhong, FENG Zebiao, REN Xiaolei, DING Chunfeng. Parameter Optimization Design Considering the Stability of the Fused Deposition Modeling Process[J]. Journal of Mechanical Engineering, 2023, 59(23): 331-342.

参考文献

[1] AHMED S，NAUMAN S，KHAN Z M. Development of TPU/CNPs flexible composite strain sensors using Additive Manufacturing (AM) for Structural Health Monitoring (SHM) of aerospace components[C]//2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST). IEEE，2021: 47-54.
[2] NISHINO T，MATSUMOTO T，OHNISHI J，et al. Applications of AM for sports parts (Running shoes):Manufacturing of sole using reactive 3D printing[J]. Seikei-Kakou，2022，34(8):288-292.
[3] KUNKEL M E，VASQUES M T，PERFEITO J A J，et al. Mass-production and distribution of medical face shields using additive manufacturing and injection molding process for healthcare system support during COVID-19 pandemic in brazil[J]. 2020.
[4] ZHANG Yue，WESTERWEEL B，BASTEN R，et al. Distributed 3d printing of spare parts via ip licensing[J]. Manufacturing & Service Operations Management，2022，24(5):2685-2702.
[5] MANERO A，SMITH P，KOONTZ A，et al. Leveraging 3D printing capacity in times of crisis:Recommendations for COVID-19 distributed manufacturing for medical equipment rapid response[J]. International Journal of Environmental Research and Public Health，2020，17(13):4634.
[6] YANG Hui，RAO P，SIMPSON T，et al. Six-sigma quality management of additive manufacturing[J]. Proceedings of the IEEE，2021，109(4):347-376.
[7] WANG Chechung，LIN Tawei，HU Shrshiung. Optimizing the rapid prototyping process by integrating the Taguchi method with the gray relational analysis[J]. Rapid Prototyping Journal，2007，13(5):304-315.
[8] NANCHARAIAH T. Optimization of process parameters in FDM process using design of experiments[J]. International Journal on Emerging Technologies，2011，2(1):100-102.
[9] PATEL D M. Effects of infill patterns on time，surface roughness and tensile strength in 3D printing[J]. International Journal of Engineering Development and Research，2017，5(3):566-569.
[10] BALKIN S D，LIN D K J. A neural network approach to response surface methodology[J]. Communications in Statistics-Theory and Methods，2000，29:2215-2227.
[11] VINING G G，BOHN L L. Response surfaces for the mean and variance using a nonparametric approach[J]. Journal of Quality Technology，1998，30(3):282-291.
[12] SACKS J，WELCH W J，MITCHELL T J，et al. Design and analysis of computer experiments[J]. Statistical Science，1989，4(4):409-423.
[13] RASMUSSEN C E，WILLIAMS C K I. Gaussian processes for machine learning[M]. Cambridge:MIT Press，2006.
[14] KLEIJNEN J P C. Design and analysis of computational experiments:Overview[M]:Springer，2010.
[15] ALSHRAIDEHA H，CASTILLO E D. Gaussian process modeling and optimization of profile response experiments[J]. Quality and Reliability Engineering International，2014，30(4):449-462.
[16] GU Mengyang，BERGER J O. Parallel partial Gaussian process emulation for computer models with massive output[J]. The Annals of Applied Statistics，2016，10(3):1317-1347.
[17] FENG Zebiao，WANG Jianjun，MA Yan，et al. Integrated parameter and tolerance design based on a multivariate Gaussian process model[J]. Engineering Optimization，2021，53(8):1349-1368.
[18] GU Mengyang，XU Yanxun. Fast nonseparable Gaussian stochastic process with application to methylation level interpolation[J]. Journal of Computational and Graphical Statistics，2020，29(2):250-260.
[19] WANG Lening，CHEN Xiaoyu，KANG Sungku，et al. Meta-modeling of high-fidelity FEA simulation for efficient product and process design in additive manufacturing[J]. Additive Manufacturing，2020，35:101211.
[20] FENG Zebiao，WANG Jianjun，MA Yan，et al. Robust parameter design based on Gaussian process with model uncertainty[J]. International Journal of Production Research，2021，59(9):2772-2788.
[21] MOHAMED O A，MASOOD S H，BHOWMIK J L. Optimization of fused deposition modeling process parameters:a review of current research and future prospects[J]. Advances in Manufacturing，2015，3(1):42-53.
[22] LIU Zengguang，WANG Yanqing，WU Beicheng，et al. A critical review of fused deposition modeling 3D printing technology in manufacturing polylactic acid parts[J]. The International Journal of Advanced Manufacturing Technology，2019，102(9-12):2877-2889.
[23] CANO-VICENT A，TAMBUWALA M M，HASSAN S S，et al. Fused deposition modelling:Current status，methodology，applications and future prospects[J]. Additive Manufacturing，2021，47:102378.
[24] YODO N，DEY A. Multi-objective optimization for FDM process parameters with evolutionary algorithms[M]:Springer，Cham，2021.
[25] TIAN Xiaoyong，WU Lingling，GU Dongdong，et al. Roadmap for Additive Manufacturing:Toward Intellectualization and Industrialization[J]. Chinese Journal of Mechanical Engineering:Additive Manufacturing Frontiers，2022，1(1):100014.
[26] 王磊，卢秉恒. 我国增材制造技术与产业发展研究[J]. 中国工程科学，2022，24(4):202-211. WANG Lei，LU Bingheng. Development of additive manufacturing technology and industry in China[J]. Strategic Study of CAE，2022，24(4):202-211.
[27] 姜世杰，陈丕峰，胡科，等. 打印层厚度及路径宽度对熔丝成型结合颈的影响规律研究[J]. 机械工程学报，2022，58(9):291-297. JIANG Shijie，CHEN Pifeng，HU Ke，et al. Theoretical and experimental study on the bonding neck of fused filament fabrication parts[J]. Journal of Mechanical Engineering，2022，58(9):291-297.
[28] 赵广宾，秦勉，刘雨，等. 聚醚醚酮熔融沉积成形强度工艺参数的优化[J]. 机械工程学报，2020，56(3):216-222. ZHAO Guangbin，QIN Mian，LIU Yu，et al. Optimizing fused deposition molding process parameters for improving forming strength of polyetheretherketone[J]. Journal of Mechanical Engineering，2020，56(3):216-222.
[29] RAJU M，GUPTA M K，BHANOT N，et al. A hybrid PSO–BFO evolutionary algorithm for optimization of fused deposition modelling process parameters[J]. Journal of Intelligent Manufacturing，2018，30(7):2743-2758.
[30] GALETTO M，VERNA E，GENTA G. Effect of process parameters on parts quality and process efficiency of fused deposition modeling[J]. Computers and Industrial Engineering，2021，156:107238.
[31] NATH P，OLSON J D，MAHADEVAN S，et al. Optimization of fused filament fabrication process parameters under uncertainty to maximize part geometry accuracy[J]. Additive Manufacturing，2020，35:101331.
[32] DONG Guoying，WIJAYA G，TANG Yunlong，et al. Optimizing process parameters of fused deposition modeling by Taguchi method for the fabrication of lattice structures[J]. Additive Manufacturing，2018，19:62-72.
[33] WEBBE KEREKES T，LIM H，JOE W Y，et al. Characterization of process–deformation/damage property relationship of fused deposition modeling (FDM) 3D-printed specimens[J]. Additive Manufacturing，2019，25:532-544.
[34] MOSTAFAEI A，ELLIOTT A M，BARNES J E，et al. Binder jet 3D printing process parameters，materials，properties，modeling，and challenges[J]. Progress in Materials Science，2021，119:100707.
[35] 梁庆宣，尹浩宇，李赵辉，等. 超表面异质结构的熔融沉积复合成形工艺及其电磁伪装性能研究[J]. 机械工程学报，2022，58(3):276-283. LIANG Qingxuan，YIN Haoyu，LI Zhaohui，et al. Research on fused deposition integrated manufacturing process and electromagnetic camouflaging properties of metasurface heterostructure[J]. Journal of Mechanical Engineering，2022，58(3):276-283.
[36] BOX G. Split plot experiments[J]. Quality Engineering，1996，8(3):515-520.
[37] JOSEPH V R，GUL E，BA S. Designing computer experiments with multiple types of factors:The MaxPro approach[J]. Journal of Quality Technology，2020，52(4):343-354.
[38] QIAN P Z G，WU Huaiqing，WU C F J. Gaussian process models for computer experiments with qualitative and quantitative factors[J]. Technometrics，2008，50(3):383-396.
[39] ZHOU Qiang，QIAN P Z G，ZHOU Shiyu. A simple approach to emulation for computer models with qualitative and quantitative factors[J]. Technometrics，2011，53(3):266-273.
[40] ZHANG Yichi，TAO Siyu，CHEN Wei，et al. A latent variable approach to Gaussian process modeling with qualitative and quantitative factors[J]. Technometrics，2019，62(3):291-302.
[41] LI Wei，XIAO Mi，GAO Liang. Improved collaboration pursuing method for multidisciplinary robust design optimization[J]. Structural and Multidisciplinary Optimization，2019，59(6):1949-1968.
[42] TRAN L，DUCKSTEIN L. Comparison of fuzzy numbers using a fuzzy distance measure[J]. Fuzzy Sets and Systems，2002，130(3):331-341.
[43] HE Yingdong，HE Zhen，DENG Yujia，et al. IFPBMs and their application to multiple attribute group decision making[J]. Journal of the Operational Research Society，2017，67(1):127-147.