[1] Frazier W E.Metal Additive Manufacturing: A Review[J]. Journal of Materials Engineering and Performance (S1059-9495), 2014, 23(6): 1917-1928. [2] Pattinson S W, Hart A J.Additive Manufacturing of Cellulosic Materials with Robust Mechanics and Antimicrobial Functionality[J]. Advanced Materials Technologies (S2365-709X), 2017, 2(4): 1-6. [3] Tofail S A M, Koumoulos E P, Bandyopadhyay A, et al. Additive Manufacturing: Scientific and Technological Challenges, Market Uptake and Opportunities[J]. Materialstoday (S1369-7021), 2018, 21(1): 22-37. [4] Mohamed O A, Masood S H, Bhowmik J L.Optimization of Fused Deposition Modeling Process Parameters for Dimensional Accuracy Using I-optimality Criterion[J]. Measurement (S0263-2241), 2016, 81(11): 174-196. [5] Charles A, Elkaseer A, Thij L, et al.Effect of Process Parameters on the Generated Surface Roughness of Down-facing Surfaces in Selective Laser Melting[J]. Applied Sicences (S2076-3417), 2019, 9(6): 1256-1269. [6] Cunningham C R, Flynn J M, Shokrani A, et al.Invited Review Article: Strategies and Processes for High Quality Wire Arc Additive Manufacturing[J]. Additive Manufacturing (S2214-8604), 2018, 22: 672-686. [7] Wu B T.A Review of the Wire Arc Additive Manufacturing of Metals: Properties, Defects and Quality Improvement[J]. Journal of Manufacturing Processes (S1526-6125), 2018, 35(10): 127-139. [8] Mughal M P, Fawad H, Mufti R A.Three-dimensional Finite-element Modelling of Deformation in Weld-based Rapid Prototyping[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science (S0954-4062), 2006, 220(6): 875-885. [9] Rodrigues T A, Duarte V, Miranda R M, et al.Current Satus and Perspectives on Wire and Arc Additive Manufacturing (WAAM)[J]. Materials (S1996-1944), 2019, 12(7): 1121-1162. [10] 王晓光, 刘奋成, 方平, 等. CMT电弧增材制造316L不锈钢成形精度与组织性能分析[J]. 焊接学报, 2019, 40(5): 100-106. Wang Xiaoguang, Liu Fencheng, Fang Ping, et al.Forming Accuracy and Properties of Wire Arc Additive Manufacturing of 316L Components Using CMT Process[J]. Transactions of the China Weldeing Institution, 2019, 40(5): 100-106. [11] Hoefer K, Mayr P.3DPMD-arc-based Additive Manufacturing with Titanium Powder as Raw Material[J]. China Welding (S1004-5341), 2019, 28(1): 11-15. [12] Xiong J, Zhang G J, Gao H M, et al.Modeling of bead Section Profile and Overlapping Beads with Experimental Validation for Robotic GMAW-based rapid Manufacturing[J]. Robotics and Computer-Integrated Manufacturing (S0736-5845), 2013, 29(2): 417-523. [13] Geng H, Xiong J, Huang D, et al.A Prediction Model of Layer Geometrical Size in Wire and Arc Additive Manufacture Using Response Surface Methodology[J]. The International Journal of Advanced Manufacturing Technology (S0268-3768), 2015, 93(1/4): 175-186. [14] 柏久阳, 王计辉, 林三宝, 等. 铝合金电弧增材制造焊道宽度尺寸预测[J]. 焊接学报, 2015, 36(9): 87-90. Bai Jiuyang, Wang Jihui, Lin Sanbao, et al.Width Prediction of Aluminium Alloy Weld Additively Manu Factured by TIG Arc[J]. Transactions of the China Weldeing Institution, 2015, 36(9): 87-90. [15] 张金田, 王任甫, 王杏华. 船用钢电弧增材制造的焊道尺寸预测[J].材料开发与应用, 2018, 33(2): 17-22. Zhang Jintian, Wang Renpu, Wang Xinghua.The Weld Bead Dimensional Prediction of Wire and Arc Additive Manufacture for Ship Steel[J]. Journal of Materials Development and Application, 2018, 33(2): 17-22. [16] Hu Z Q, Qin X P, Li Y F, et al.Multi-bead Overlapping Model with Varying Cross-section Profile for Robotic GMAW-based Additive Manufacturing[J]. Journal of Intelligent Manufacturing (S0956-5515), 2020, 31: 1133-1147. [17] Karmuhilan M, Sood A K.Intelligent Process Model for Bead Geometry Prediction in WAAM[J]. Materials Today: Proceedings (S2214-7853), 2018, 5(11): 24005-24013. [18] 郑金勇, 刘保国, 冯伟. 基于遗传算法优化灰色神经网络的机床主轴热误差建模研究[J]. 机电工程, 2019, 36(6): 602-607. Zheng Jinyong, Liu Baoguo, Feng Wei.Machine Tool Spindle Thermal Error Modeling Based on Genetic Algorithm Optimization Grey Neural Network[J]. Journal of Mechanical & Electrical Engineering, 2019, 36(6): 602-607. [19] Hu Z Q, Qin X P, Li Y F, et al.Welding Parameters Prediction for Arbitrary Layer Height in Robotic Wire and Arc Additive Manufacturing[J]. Journal of Mechanical Science and Technology (S1738-494X), 2020, 34(4): 1683-1695. [20] 赵鹏, 吕彦明, 周文军, 等. 钨极惰性气体保护焊电弧增材制造单焊道尺寸预测[J]. 机械工程材料, 2020, 44(11): 78-82. Zhao Peng, Lü Yanming, Zhou Wenjun, et al.Prediction of Single Weld Based Size of TIG Welding Arc Additive Manufacturing[J]. Materials for Mechanical Engineering, 2020, 44(11): 78-82. [21] 李巍华, 单外平, 曾雪琼. 基于深度信念网络的轴承故障分类识别[J]. 振动工程学报, 2016, 29(2): 340-347. Li Weihua, Shan Waiping, Zeng Xueqiong.Bearing Fault Identification Based on Deep Belief Network[J]. Journal of Vibration Engineering, 2016, 29(2): 340-347. [22] 张籍, 薛儒涛, 刘慧, 等. 基于深度信念网络的不同行业中长期负荷预测[J]. 电力系统及其自动化学报, 2018, 31(9): 12-19. Zhang Ji, Xue Rutao, Liu Hui, et al.Medium-and Long-term Load Forecasting for Different Industries Based on Deep Belief Network[J]. Proceedings of the CSU-EPSA, 2018, 31(9): 12-19. [23] Yu Y, Chen Z M, Li M S, et al.Forecasting a Short-term Wind Speed Using a Deep Belief Network Combined with a Local Predictor[J]. IEEJ Transactions on Electrical and Electronic Engineering (S1931-4973), 2019, 14(2): 238-244. [24] Kong F, Li Ji, Jiang B, et al.Short-term Traffic Flow Prediction in Smart Multimedia System for Internet of Vehicles Based on Deep Belief Network[J]. Future Generation Computer Systems-The International Journal of Escience (S0167-739X), 2019, 93: 460-472. [25] 许鸿伟, 张洁, 吕佑龙, 等. 基于改进的连续型深度信念网络的晶圆良率预测方法[J]. 计算机集成制造系统, 2020, 26(9): 2388-2395. Xu Hongwei, Zhang Jie, Lü Youlong, et al.Wafer Yield Prediction Method Based on Improved Continuous Deep Belief Network[J]. Computer Integrated Manufacturing Systems, 2020, 26(9): 2388-2395. [26] Yang X S, Ded S.Engineering Optimization by Cuckoo Search[J]. International Journal of Mathematical Modeling & Numerical Optimization (S2040-3607), 2010, 1(4): 330-343. [27] 李正明, 梁彩霞, 王满商. 基于PSO-DBN神经网络的光伏短期发电出力预测[J]. 电力系统保护与控制, 2020, 48(8): 149-154. Li Zhengming, Liang Caixia, Wang Manshang.Short-term Power Generation Output Prediction Based on a PSO-DBN Neural Network[J]. Power System Protection and Control, 2020, 48(8): 149-154. [28] 吴伟杰, 吴杰康, 雷振. 基于CSO优化深度信念网络的园区能源需求预测方法[J]. 电网技术, 2021, 45(10): 3859-3868. Wu Weijie, Wu Jiekang, Lei Zheng.Energy Demand Forecasting Method of Park Based on CSO Optimized Deep Belief Network[J]. Power System Technology, 2021, 45(10): 3859-3868. |