An Energy Efficiency Optimization Model of Fused Filament Fabrication 3D Printing Based on Support Vector Regression

doi:10.3969/j.issn.1004-132X.2022.18.008

Abstract

Abstract: The fused filament fabrication 3D printing energy efficiency was predicted and optimized based on response surface method and support vector regression model. Firstly, the Taguchi method was used to design the six factor three level orthogonal test, based on the response surface method, three factors that had a significant impact on processing energy efficiency were obtained, namely layer height, printing speed and hot bed temperature. Then, the prediction model of processing energy efficiency was established by support vector regression method, and compared with BP neural network method, the results show that the modeling and prediction performance of support vector regression method is better. Finally, the optimization model aiming at processing time and energy efficiency was established, and NSGA-Ⅱ, MOEA/D, SPEA2 and MOPSO were used to solve the model respectively, the Pareto front of the four algorithms was analyzed and compared, the results show that NSGA-Ⅱ performs best in solving this problem, the optimization results obtained by NSGA-Ⅱ algorithm were compared with the experimental results, which reflects the effectiveness and rationality of the optimization results of NSGA-Ⅱ algorithm.

Key words: , fused filament fabrication, energy efficiency, support vector regression, multi-objective optimization

摘要： 基于响应面法和支持向量回归模型对熔丝制造3D打印能效进行预测与优化。首先，利用田口方法设计六因素三水平正交试验，通过响应面法分析得出对加工能效影响较为显著的3个因素即层高、打印速度和热床温度；然后，通过支持向量回归方法建立加工能效预测模型，并与BP神经网络方法进行对比，结果表明支持向量回归方法建模预测性能更优；最后，建立以加工时间和能效为目标的优化模型，利用NSGA-Ⅱ、MOEA/D、SPEA2和MOPSO 4种算法分别对模型进行求解，分析比较4种算法的Pareto前沿，结果表明NSGA-Ⅱ算法在求解此问题时综合表现最佳，对比NGSA-Ⅱ算法求得的优化结果与试验结果可知，NSGA-Ⅱ算法具有有效性和合理性。

关键词: 熔丝制造, 能效, 支持向量回归, 多目标优化

CLC Number:

TP391

BAO Hong, YANG Jing, KE Qingdi, LI Hongzhen, YAO Yongzheng, . An Energy Efficiency Optimization Model of Fused Filament Fabrication 3D Printing Based on Support Vector Regression[J]. China Mechanical Engineering, 2022, 33(18): 2215-2226.

鲍宏, 杨靖, 柯庆镝, 李红真, 么永政, . 基于支持向量回归的熔丝制造3D打印能效优化模型[J]. 中国机械工程, 2022, 33(18): 2215-2226.

References

［1］BERMAN B. 3-D Printing:the New Industrial Revolution［J］. Business Horizons, 2012, 55(2):155-162.
［2］王运赣，王宣. 三维打印技术［M］. 武汉：华中科技大学出版社，2013.
WANG Yungan, WANG Xuan. Three-dimensional Printing Technology［M］. Wuhan:Huazhong University of Science and Technology Press, 2013.
［3］FOUNTAS N A, VAXEVANIDIS N M. Optimization of Fused Deposition Modeling Process Using a Virus-evolutionary Genetic Algorithm［J］. Computers in Industry, 2021, 125（3）:103371.
［4］NEGRETE C C. Optimization of Printing Parameters in Fused Deposition Modeling for Improving Part Quality and Process Sustainability［J］. International Journal of Advance Manufacturing Technology, 2020, 108(7/8):2131-2147.
［5］ARUP D, DAVID H, NITA Y. Optimizing Multiple Process Parameters in Fused Deposition Modeling with Particle Swarm Optimization［J］. International Journal of Interactive Design and Manufacturing, 2020, 14:393-405.
［6］TARUN S, VIMAL K P, ASHISH K S. Modeling and Optimization of Fused Deposition Modeling (FDM) Process through Printing PLA Implants Using Adaptive Neurofuzzy Inference System (ANFIS) Model and Whale Optimization Algorithm［J］. Journal of Brazilian Society of Mechanical Science and Engineering, 2020, 42:617.
［7］TIAN W M, MA J F, MORTEZA A. Energy Consumption Optimization with Geometric Accuracy Consideration for Fused Filament Fabrication Processes［J］. International Journal of Advance Manufacturing Technology, 2019, 103:3223-3233.
［8］段家烀，赵刚，杨少远，等. 面向高效低碳的FDM打印参数多目标模型研究［J］. 机械设计与制造, 2020, 11:102-106.
DUAN Jiahu, ZHAO Gang, YANG Shaoyuan, et al. Multi-objective FDM Printing Parameters Optimization Model for High Efficiency and Low Carbon［J］. Machinery Design & Manufacture, 2020, 11:102-106.
［9］胡伟岳，廖文和，刘婷婷，等. 熔融沉积成形制件表面粗糙度预测模型［J］. 中南大学学报(自然科学版),2020, 51(9):2460-2470.
HU Weiyue, LIAO Wenhe, LIU Tingting, et al. Prediction Model of Surface Roughness of Fused Deposition Modeling Parts［J］. Journal of Central South University(Science and Technology), 2020, 51(9):2460-2470.
［10］孙春华. FDM成形件精度预测模型的建立［J］. 机械科学与技术，2010, 29(3):399-403.
SUN Chunhua. A Model for Predicting the Precision of an FDM Rapid Prototype［J］. Mechanical Science and Technology for Aerospace Engineering, 2010, 29(3):399-403.
［11］潘丽娟，商正，贾桂红，等. 熔融沉积制造表面粗糙度参数显著性及预测模型研究［J］.图学学报，2020, 41(4):593-598.
PAN Lijuan, SHANG Zheng, JIA Guihong, et al. Investigation of Parameters Significance and Predicted Model on Surface Roughness on Fused Deposition Modeling［J］. Journal of Graphics, 2020, 41(4):593-598.
［12］王宏松，修辉平，韩燕. 基于BP神经网络的FDM成型件精度预测［J］. 九江职业技术学院学报， 2018（4）:8-11.
WANG Hongsong, XIU Huiping, HAN Yan. Precision Prediction of FDM Molded Parts Based on BP Neural Network［J］. Journal of Jiujiang Vocational & Technical College, 2018（4）:8-11.
［13］刘薇娜，李跃，李星燃，等. FDM型3D打印成型精度多元非线性回归模型［J］. 兰州工业学院学报，2018, 25(6):57-62.
LIU Weina, LI Yue, LI Xingran, et al. Multiple Nonlinear Regression Model of FDM 3D Printing Accuracy［J］. Journal of Lanzhou Institute of Technology, 2018, 25(6):57-62.
［14］吴天山，于鸿彬，李小青，等. 基于遗传算法的BP神经网络熔融沉积成型翘曲变形预测研究［J］. 热加工工艺，2019, 48(22):48-52.
WU Tianshan, YU Hongbin, LI Xiaoqing, et al. Study on Warp Deformation Prediction in FDM Process Based on Genetic Algorithm and BP Neural Network［J］. Hot Working Technology, 2019, 48(22):48-52.
［15］纪良波. 基于小波神经网络熔丝堆积三维打印精度预测模型［J］. 上海交通大学学报，2015, 49(3):375-378.
JI Liangbo. PrecisionPrediction Model in Fused Deposition Modeling of Three-dimensional Printing Based on Wavelet Neural Network［J］. Journal of Shanghai Jiao Tong University, 2015, 49(3):375-378.
［16］许双梅. SLM成形过程能耗分析方法与预测模型研究［D］. 杭州：浙江大学, 2018.
XU Shuangmei. Energy Analysis and Modeling Approach for Selective Laser Melting Processes［D］. Hangzhou:Zhejiang University, 2018.
［17］YI L, KRENKEL N, AURICH J C. An Energy Model of Machine Tools for Selective Laser Melting［J］. Procedia CIRP, 2018, 78:67-72.
［18］MA F, ZHANG H, HON K K B, et al. An Optimization Approach of Selective Laser Sintering Considering Energy Consumption and Material Cost［J］. Journal of Cleaner Production, 2018, 199:529-537.
［19］李玉霞. 选择性激光熔化增材制造碳效率评估方法及应用［D］. 重庆：重庆大学，2016.
LI Yuxia. Carbon Efficiency and Application of Selective Laser Melting Process for Addictive Manufacturing［D］. Chongqing:Chongqing University, 2016.
［20］张雷，张北鲲，鲍宏，等. 产品熔融沉积制造的碳排放量化方法［J］. 机械工程学报，2017,53(5):50-59.
ZHANG Lei, ZHANG Beikun, BAO Hong, et al. Carbon Emissions Quantitative Methodology of Product Fused Deposition Manufacturing［J］. Journal of Mechanical Engineering, 2017, 53(5):50-59.
［21］邹国林，郭东明，贾振元，等. 熔融沉积制造工艺参数的优化［J］. 大连理工大学学报,2002, 42(4):446-450.
ZOU Guolin, GUO Dongming, JIA Zhenyuan, et al. Optimization of Process Parameters for Fused Deposition Modeling［J］. Journal of Dalian University of Technology, 2002, 42(4):446-450.
［22］曾凤章，赵霞. 田口方法及其标准化设计［J］.机械工业标准化与质量，2003(11):7-9.
ZENG Fengzhang, ZHAO Xia. Taguchi Method and Its Standardized Design［J］. Machinery Industry Standardization & Quality, 2003(11):7-9.
［23］严建文，钟小虎，范煜，等. 基于整群抽样和支持向量回归模型的高功率半导体激光器剩余使用寿命预测［J］. 中国机械工程，2021, 32(13):1523-1529.
YAN Jianwen, ZHONG Xiaohu, FAN Yu, et al. RUL Prediction of High-power Semiconductor Lasers Based on Cluster Sampling and SVR Model［J］. China Mechanical Engineering, 2021, 32(13):1523-1529.
［24］李恩颖，王琥，李光耀. 基于支持向量机回归的材料参数反求方法［J］. 机械工程学报， 2012, 48(6):90-95.
LI Enying, WANG Hu, LI Guanyao. Material Parameter Inverse Technique Based on Support Vector Regression［J］. Journal of Mechanical Engineering, 2012, 48(6):90-95.
［25］周志华.机器学习［M］.北京：清华大学出版社，2016.
ZHOU Zhihua. Machine Learning［M］. Beijing:Tsinghua University Press, 2016.
［26］BIN M Z, KANESAN J, CHUAN J H, et al. A Multi-objective Particle Swarm Optimization Algorithm Based on Dynamicboundary Search for Constrained Optimization［J］. Applied Soft Computing, 2018, 70:680-700.
［27］DEB K, PRATAP A, AGARWAL S, et al.A Fast and Elitist Multiobjective Genetic Algorithm:NSGA-Ⅱ［J］. IEEE Transactions on Evolutionary Computation, 2002, 6(2):182-197.
［28］ZHANG Q F, LI H. MOEA/D:a Multiobjective Evolutionary Algorithm Based on Decomposition［J］. IEEE Transactions on Evolutionary Computation, 2007, 11(6):712-731.
［29］ZITZLER E, LAUMANNS M, THIELE L. SPEA2:Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization［R］. Zurich:Computer Engineering and Networks Laboratory(TIK), Swiss Federal Institute of Technology(ETH), 2001:19-21.
［30］CARLOS A, COELLO C, GREGORIO T P, et al. Handing Multiple Objectives with Particle Swarm Optimization［J］. IEEE Transactions on Evolutionary Computation, 2004, 8(3):256-279.
［31］BEHNAMIAN J, ZANDIEH M, FATEMI G M T. A Multi-phase Covering Pareto-optimal Front Method to Multi-objective Parallel Machine Scheduling［J］. International Journal of Production Research, 2010, 48(17):4949-4976.
［32］翁剑，庄可佳，浦栋麟，等. 基于机器学习和多目标算法的钛合金插铣优化［J］. 中国机械工程，2021, 32(7):771-777.
WENG Jian, ZHUANG Kejia, PU Donglin, et al. Plunge Milling of Titanium Alloys Based on Machine Learning and Multi-objective Optimization［J］. China Mechanical Engineering, 2021, 32(7):771-777.

[1]	LI Meng-Lei, GU Yo-Qin, ZHANG Hua-Liang, LIU Li-Qin, DU Juan, WEN Chu-Hua, LAN Guo-Sheng. Parallel Mechanism Structure Optimization Design Based on Multi-objective Differential Evolution Algorithm [J]. J4, 201016, 21(16): 1915-1920.
[2]	LIU Xu, SUN Yuli, ZHANG Guiguan, QIAN BingkunG, AO Hang, ZUO Dunwen. Simulation and Experimental Research about Temperature Fields of PDMS Cooled by Liquid Nitrogen Jet Impingement [J]. China Mechanical Engineering, 2022, 33(18): 2161-2171.
[3]	CONG Jianchen, NI Peixiang, SUN Jun, LYU Shijie. Experimental Research and Analysis on Torsional Fatigue Strength of Engine Crankshafts [J]. China Mechanical Engineering, 2022, 33(18): 2197-2204.
[4]	WANG Lanqing, HUANG Hui, CUI Changcai. Experimental Research of Surface Quality Evaluation Method for Wire Saw Sapphire Wafer [J]. China Mechanical Engineering, 2022, 33(17): 2023-2028.
[5]	JI Li, MA Xueqing, CHEN Zhenmin. Low Frequency Vibration Mechanism for AMB High-speed Motor Rotor Systems and Its Compensation Strategy [J]. China Mechanical Engineering, 2022, 33(17): 2053-2060.
[6]	ZHANG Jin, HAN Fuzhu, . Establishment and Research of Prediction Model of Discharge Gap Voltages in Discharge Arc Milling [J]. China Mechanical Engineering, 2022, 33(16): 1891-1896.
[7]	RONG Yu, DOU Tianci, ZHANG Xingchao, ZHANG LeiZ, HAO Jingyu. A Real-time Control Method for Optimal Overall Performances of Planar Redundant Robots [J]. China Mechanical Engineering, 2022, 33(16): 1928-1939.
[8]	LIU Liye, CHEN Yongliang, TANG Weili, WEI Yunpeng, SUO Shucan. Research on Open Control Systems and Tangent Tracking of Friction Stir Welding Machines [J]. China Mechanical Engineering, 2022, 33(16): 1948-1956.
[9]	XU Rongfei, FAN Kaiguo. Research on Thermal Characteristics of Motorized Spindles Based on Digital Twin [J]. China Mechanical Engineering, 2022, 33(16): 1965-1971.
[10]	SHU Zihao, PANG Qilong, KUANG Liangjie. Effects of Frequency Features Generated in Machined Surface on Subsurface Temperature Field of KH2PO4 Crystals [J]. China Mechanical Engineering, 2022, 33(16): 1983-1991.
[11]	SHI Keming, ZOU Yisheng, LIU Yongzhi, DING Kun, DING Guofu. A Multi Class Domain Adaptive Transfer Identification Method for Tool Wear States under Different Processing Conditions [J]. China Mechanical Engineering, 2022, 33(15): 1841-1849.
[12]	ZHANG Rongshen, WANG Yutian, HU Rongquan, YANG Lingfang, HUANG Zhi. Avoidance Planning for Intelligent Vehicles in Dilemma Based on Nominal Cost [J]. China Mechanical Engineering, 2022, 33(15): 1849-1856.
[13]	ZHANG Wenjun, ZHANG Yandong, LIN Wei, ZHAI Qianjun, BAI Zhenhua, . Research on Influence Model and Influence Factors of Welding Quality of Continuous Annealing Units [J]. China Mechanical Engineering, 2022, 33(15): 1876-1881.
[14]	ZHANG Xingui, SHI Xinglei, YU Zilin, ZHONG Zhenyuan, LI Jia. Drag Torque Algorithm of Wet DCT and Its ApplicationsLIU Bo [J]. China Mechanical Engineering, 2022, 33(15): 1882-1889.
[15]	XIE Zhijiang, CHENG Qing, DING Jun, HE Miao, FAN Naiji, WU Xiaoyong. Dimensional Design and Performance Analysis of a 6-DOF Parallel Manipulator with Two Limbs [J]. China Mechanical Engineering, 2022, 33(14): 1680-1690.