Survey on Slicing Computing in 3D Printing

doi:10.3901/JME.2024.17.235

Abstract

Abstract: 3D printing technology has brought some changes in product design and manufacturing methodologies, but also brings new challenges for layering process planning. To improve manufacturing quality, the optimization problem of layering computation is first reviewed, and various planning methods are briefly described, especially emphasizing the advantages of multi-degree-of-freedom additive manufacturing systems in high-quality layered manufacturing. Subsequently, surrounded with notable achievements in layering computations achieved by 3D printing in recent years, we analyzed their goal, algorithmic ideas, and research results from two perspectives: planar layering strategies and non-planar layering strategies. Afterward, the progress of layering computation in advanced 3D printing technology is discussed, particularly with the developments in layering strategies from the perspectives of closed-loop printing and novel additive manufacturing control. Finally, these research achievements are summarized. With the current technological development, unresolved issues and future trends are proposed for the research and application perspectives to promote the continuous development of 3D printing optimization research.

Key words: 3D printing, additive manufacturing, slicing strategy, adaptive layering, curved layer slicing

CLC Number:

TH164

HUANG Jinjie, ZHAO Xin. Survey on Slicing Computing in 3D Printing[J]. Journal of Mechanical Engineering, 2024, 60(17): 235-262.

References

[1] IAN GIBSON I G. Additive manufacturing technologies 3D printing, rapid prototyping, and direct digital manufacturing[M]. London:Springer, 2015
[2] SINGH S, SINGH G, PRAKASH C, et al. Current status and future directions of fused filament fabrication[J]. Journal of Manufacturing Processes, 2020, 55:288-306.
[3] BHATT P M, MALHAN R K, SHEMBEKAR A V, et al. Expanding capabilities of additive manufacturing through use of robotics technologies:A survey[J]. Additive Manufacturing, 2020, 31:100933.
[4] ZHANG P, WANG Z, LI J, et al. From materials to devices using fused deposition modeling:A state-of-art review[J]. Nanotechnology Reviews, 2020, 9(1):1594-1609.
[5] VYAVAHARE S, TERAIYA S, PANGHAL D, et al. Fused deposition modelling:A review[J]. Rapid Prototyping Journal, 2020, 26(1):176-201.
[6] MERCADO RIVERA F J, ROJAS ARCINIEGAS A J. Additive manufacturing methods:Techniques, materials, and closed-loop control applications[J]. The International Journal of Advanced Manufacturing Technology, 2020, 109(1-2):17-31.
[7] BAI X, LIU Y, WANG G B, et al. The pattern of technological accumulation:The comparative advantage and relative impact of 3D printing technology[J]. Journal of Manufacturing Technology Management, 2017, 28(1):39-55.
[8] THOMAS-SEALE L E J, KIRKMAN-BROWN J C, ATTALLAH M M, et al. The barriers to the progression of additive manufacture:Perspectives from UK industry[J]. International Journal of Production Economics, 2018, 198:104-118.
[9] PENG G, DAOBING C, YAN Z, et al. Research status and prospect of additive manufacturing of intelligent materials[J]. Journal of Materials Engineering, 2022, 50(6):12-26.
[10] SEFENE E M. State-of-the-art of selective laser melting process:A comprehensive review[J]. Journal of Manufacturing Systems, 2022, 63:250-274.
[11] QIN J, HU F, LIU Y, et al. Research and application of machine learning for additive manufacturing[J]. Additive Manufacturing, 2022, 52:102691.
[12] REBAIOLI L, MAGNONI P, FASSI I, et al. Process parameters tuning and online re-slicing for robotized additive manufacturing of big plastic objects[J]. Robotics and Computer-Integrated Manufacturing, 2019, 55:55-64.
[13] 徐敬华, 盛红升, 张树有, 等. 基于邻接拓扑的流形网格模型层切多连通域构建方法[J]. 计算机辅助设计与图形学学报, 2018, 30(1):180-190. XU Jinghua, SHENG Hongsheng, ZHANG Shuyou, et al. Construction method of layer cut multiconnected domains for manifold mesh model based on adjacency topology[J]. Journal of Computer Aided Design and Graphics, 2018, 30(1):180-190.
[14] MINETTO R, VOLPATO N, STOLFI J, et al. An optimal algorithm for 3D triangle mesh slicing[J]. Computer-Aided Design, 2017, 92:1-10.
[15] 戴宁, 廖文和, 陈春美. STL数据快速拓扑重建关键算法[J]. 计算机辅助设计与图形学学报, 2005, 17(11):2447-2452. DAI Ning, LIAO Wenhe, CHEN Chunmei. Key algorithms for rapid topology reconstruction of STL data[J]. Journal of Computer-Aided Design and Computer Graphics, 2005, 17(11):2447-2452.
[16] BAHNINI I, UZ ZAMAN U K, RIVETTE M, et al. Computer-aided design (CAD) compensation through modeling of shrinkage in additively manufactured parts[J]. The International Journal of Advanced Manufacturing Technology, 2020, 106(9-10):3999-4009.
[17] LIU J, LIU C, BAI Y, et al. Layer-wise spatial modeling of porosity in additive manufacturing[J]. IISE Transactions, 2018, 51(2):109-123.
[18] VYAVAHARE S, KUMAR S, PANGHAL D. Experimental study of surface roughness, dimensional accuracy and time of fabrication of parts produced by fused deposition modelling[J]. Rapid Prototyping Journal, 2020, 26(9):1535-1554.
[19] ARMILLOTTA A. Simulation of edge quality in fused deposition modeling[J]. Rapid Prototyping Journal, 2019, 25(3):541-554.
[20] ZHANG Y, BERNARD A, GUPTA R K, et al. Feature based building orientation optimization for additive manufacturing[J]. Rapid Prototyping Journal, 2016, 22(2):358-376.
[21] AL-AHMARI A M, ABDULHAMEED O, KHAN A A. An automatic and optimal selection of parts orientation in additive manufacturing[J]. Rapid Prototyping Journal, 2018, 24(4):698-708.
[22] ZHANG Y, BERNARD A, HARIK R, et al. Build orientation optimization for multi-part production in additive manufacturing[J]. Journal of Intelligent Manufacturing, 2015, 28(6):1393-1407.
[23] CHRISTIANSEN A N, SCHMIDT R, BAERENTZEN J A. Automatic balancing of 3D models[J]. Computer-Aided Design, 2015, 58:236-241.
[24] MATOS M A, ROCHA A M A C, PEREIRA A I. Improving additive manufacturing performance by build orientation optimization[J]. The International Journal of Advanced Manufacturing Technology, 2020, 107(5-6):1993-2005.
[25] EZAIR B, MASSARWI F, ELBER G. Orientation analysis of 3D objects toward minimal support volume in 3D-printing[J]. Computers & Graphics, 2015, 51:117-124.
[26] OH Y, ZHOU C, BEHDAD S. Part decomposition and 2D batch placement in single-machine additive manufacturing systems[J]. Journal of Manufacturing Systems, 2018, 48:131-139.
[27] DEGLIN A, BERNARD A. A knowledge-based environment for modelling and computer-aided process planning of rapid manufacturing processes[C]//Proceedings of the 3rd Integrated Design and Manufacturing in Mechanical Engineering Conference, Montreal, Canada, F May, 2000. Springer:DORDRECHT, 2002.
[28] PAPAZETIS G, VOSNIAKOS G-C. Mapping of deposition-stable and defect-free additive manufacturing via material extrusion from minimal experiments[J]. The International Journal of Advanced Manufacturing Technology, 2018, 100(9-12):2207-19.
[29] ZAMAN U K U, BOESCH E, SIADAT A, et al. Impact of fused deposition modeling (FDM) process parameters on strength of built parts using Taguchi's design of experiments[J]. The International Journal of Advanced Manufacturing Technology, 2018, 101(5-8):1215-26.
[30] CAMPOSECO-NEGRETE C. Optimization of printing parameters in fused deposition modeling for improving part quality and process sustainability[J]. The International Journal of Advanced Manufacturing Technology, 2020, 108(7-8):2131-47.
[31] 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, 2016, 81:174-96.
[32] WANG S, D'HOOGE D R, DAELEMANS L, et al. The transferability and design of commercial printer settings in PLA/PBAT fused filament fabrication[J]. Polymers (Basel), 2020, 12(11):2573.
[33] MOHAMED O A, MASOOD S H, BHOWMIK J L. Mathematical modeling and FDM process parameters optimization using response surface methodology based on Q-optimal design[J]. Applied Mathematical Modelling, 2016, 40(23-24):10052-10073.
[34] ARMILLOTTA A, CAVALLARO M. Edge quality in fused deposition modeling:I. Definition and analysis[J]. Rapid Prototyping Journal, 2017, 23(6):1079-1087.
[35] ARMILLOTTA A, BIANCHI S, CAVALLARO M, et al. Edge quality in fused deposition modeling:II. experimental verification[J]. Rapid Prototyping Journal, 2017, 23(4):686-695.
[36] BOSCHETTO A, BOTTINI L. Accuracy prediction in fused deposition modeling[J]. Int J Adv Manuf Technol, 2014, 73(5-8):913-928.
[37] LALEHPOUR A, BARARI A. A more accurate analytical formulation of surface roughness in layer-based additive manufacturing to enhance the product's precision[J]. The International Journal of Advanced Manufacturing Technology, 2018, 96(9-12):3793-3804.
[38] WANG P, ZOU B, DING S. Modeling of surface roughness based on heat transfer considering diffusion among deposition filaments for FDM 3D printing heat-resistant resin[J]. Applied Thermal Engineering, 2019, 161:114064.
[39] RUPAL B S, ANWER N, SECANELL M, et al. Geometric tolerance and manufacturing assemblability estimation of metal additive manufacturing (AM) processes[J]. Materials & Design, 2020, 194:108842.
[40] D'AMICO A, PETERSON A M. An adaptable FEA simulation of material extrusion additive manufacturing heat transfer in 3D[J]. Additive Manufacturing, 2018, 21:422-430.
[41] AFONSO J A, ALVES J L, CALDAS G, et al. Influence of 3D printing process parameters on the mechanical properties and mass of PLA parts and predictive models[J]. Rapid Prototyping Journal, 2021, 27(3):487-495.
[42] WANG J, WANG Y, SHI J. On efficiency and effectiveness of finite volume method for thermal analysis of selective laser melting[J]. Engineering Computations, 2020, 37(6):2155-2175.
[43] KUNDAKCIOGLU E, LAZOGLU I, RAWAL S. Transient thermal modeling of laser-based additive manufacturing for 3D freeform structures[J]. The International Journal of Advanced Manufacturing Technology, 2015, 85(1-4):493-501.
[44] ARBEITER F, SPOERK M, WIENER J, et al. Fracture mechanical characterization and lifetime estimation of near-homogeneous components produced by fused filament fabrication[J]. Polymer Testing, 2018, 66:105-113.
[45] KUZNETSOV V E, TAVITOV A G, URZHUMTSEV O D, et al. Hardware factors influencing strength of parts obtained by fused filament fabrication[J]. Polymers (Basel), 2019, 11(11):1870.
[46] SALAZAR-MARTIN A G, GARCIA-GRANADA A A, REYES G, et al. Time-dependent mechanical properties in polyetherimide 3D-printed parts are dictated by isotropic performance being accurately predicted by the generalized time hardening model[J]. Polymers (Basel), 2020, 12(3):678.
[47] CASAVOLA C, CAZZATO A, MORAMARCO V, et al. Orthotropic mechanical properties of fused deposition modelling parts described by classical laminate theory[J]. Materials & Design, 2016, 90:453-458.
[48] LIGON S C, LISKA R, STAMPFL J, et al. Polymers for 3D printing and customized additive manufacturing[J]. Chem Rev, 2017, 117(15):10212-10290.
[49] BAHRAMI B, AYATOLLAHI M R, SEDIGHI I, et al. The effect of in-plane layer orientation on mixed-mode I-II fracture behavior of 3D-printed poly-carbonate specimens[J]. Engineering Fracture Mechanics, 2020, 231:107018.
[50] GUESSASMA S, BELHABIB S, NOURI H. Microstructure and mechanical performance of 3D printed wood-PLA/PHA using fused deposition modelling:Effect of printing temperature[J]. Polymers (Basel), 2019, 11(11):1778.
[51] ZISOPOL D G, PORTOACA A-I, PLOIESTI I N, et al. A Statistical approach of the flexural strength of PLA and ABS 3D printed parts[J]. Engineering Technology & Applied Science Research, 2022, 12(2):8248-8252.
[52] JAYASHURIYA M, GAUTAM S, ARAVINTH A N, et al. Studies on the effect of part geometry and process parameter on the dimensional deviation of additive manufactured part using ABS material[J]. Progress in Additive Manufacturing, 2022, 7(6):1183-1193.
[53] XIA C, PAN Z, POLDEN J, et al. Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning[J]. Journal of Intelligent Manufacturing, 2021, 33(5):1467-1482.
[54] MOHAMED O A, MASOOD S H, BHOWMIK J L. Experimental investigation for dynamic stiffness and dimensional accuracy of FDM manufactured part using IV-Optimal response surface design[J]. Rapid Prototyping Journal, 2017, 23(4):736-749.
[55] SRIVASTAVA M, MAHESHWARI S, KUNDRA T K, et al. Experimental investigation of process parameters for build time estimation in FDM process using RSM technique[C]//Proceedings of the 28th International Conference on CAD/CAM, Robotics and Factories of the Future (CARs and FoF), Int Soc Prod Enhancement, Kolaghat, INDIA, F Jan 06-08, 2016. Springer-Verlag Berlin:BERLIN, 2016.
[56] PATEL Y, KSHATTRIYA A, SINGAMNENI S B, et al. Application of curved layer manufacturing for preservation of randomly located minute critical surface features in rapid prototyping[J]. Rapid Prototyping Journal, 2015, 21(6):725-734.
[57] MOHAMED O A, MASOOD S H, BHOWMIK J L. A parametric investigation of the friction performance of PC-ABS parts processed by FDM additive manufacturing process[J]. Polymers for Advanced Technologies, 2017, 28(12):1911-1918.
[58] WANG W M, SHAO H L, LIU X P, et al. Printing direction optimization through slice number and support minimization[J]. IEEE Access, 2020, 8:75646-75655.
[59] DESWAL S, NARANG R, CHHABRA D. Modeling and parametric optimization of FDM 3D printing process using hybrid techniques for enhancing dimensional preciseness[J]. International Journal on Interactive Design and Manufacturing (IJIDeM), 2019, 13(3):1197-214.
[60] LI Z, ZHANG Z, SHI J, et al. Prediction of surface roughness in extrusion-based additive manufacturing with machine learning[J]. Robotics and Computer-Integrated Manufacturing, 2019, 57:488-95.
[61] OH Y, SHARP M, SPROCK T, et al. Neural network-based build time estimation for additive manufacturing:A performance comparison[J]. Journal of Computational Design and Engineering, 2021, 8(5):1243-1256.
[62] PADHI S K, SAHU R K, MAHAPATRA S S, et al. Optimization of fused deposition modeling process parameters using a fuzzy inference system coupled with Taguchi philosophy[J]. Advances in Manufacturing, 2017, 5(3):231-242.
[63] SAI T, PATHAK V K, SRIVASTAVA A K. Modeling and optimization of fused deposition modeling (FDM) process through printing PLA implants using adaptive neuro-fuzzy inference system (ANFIS) model and whale optimization algorithm[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2020, 42(12):617.
[64] 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.
[65] VAHABLI E, RAHMATI S. Improvement of FDM parts' surface quality using optimized neural networks-medical case studies[J]. Rapid Prototyping Journal, 2017, 23(4):825-842.
[66] ALICASTRO M, FERONE D, FESTA P, et al. A reinforcement learning iterated local search for makespan minimization in additive manufacturing machine scheduling problems[J]. Computers & Operations Research, 2021, 131:14.
[67] GE J Y, WANG Y, LI J Y, et al. A reinforcement learning-based path planning method for complex thin-walled structures in 3D printing[C]//Proceedings of the 5th International Conference on Innovation in Artificial Intelligence (ICIAI), Electr Network, F Mar 05-08, 2021. Assoc Computing Machinery:New York, 2021.
[68] CHENG R, JIN Y C, OLHOFER M, et al. A reference vector guided evolutionary algorithm for many-objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2016, 20(5):773-791.
[69] COELLO C A C, LAMONT G B, VAN VELDHUIZEN D A. Evolutionary algorithms for solving multi-objective problems[M]. New York:Springer, 2007.
[70] VAHABLI E, RAHMATI S. Improvement of FDM parts' surface quality using optimized neural networks-medical case studies[J]. Rapid Prototyping Journal, 2017, 23(4):825-842.
[71] 吴陈铭, 戴澄恺, 王昌凌, 等. 多自由度3D打印技术研究进展综述[J]. 计算机学报, 2019, 42(9):1918-1938. WU Chenming, DAI Chengkai, WANG Changling, et al. A review of research progress on multi-degree-of-freedom 3D printing technology[J]. Chinese Journal of Computers, 2019, 42(9):1918-1938.
[72] CUAN-URQUIZO E, MARTíNEZ-MAGALLANES M, CRESPO-SáNCHEZ S E, et al. Additive manufacturing and mechanical properties of lattice-curved structures[J]. Rapid Prototyping Journal, 2019, 25(5):895-903.
[73] JIN Y, DU J, HE Y, et al. Modeling and process planning for curved layer fused deposition[J]. The International Journal of Advanced Manufacturing Technology, 2016, 91(1-4):273-285.
[74] SHEN H, DIAO H, YUE S, et al. Fused deposition modeling five-axis additive manufacturing:machine design, fundamental printing methods and critical process characteristics[J]. Rapid Prototyping Journal, 2018, 24(3):548-561.
[75] SONG X, PAN Y, CHEN Y. Development of a low-cost parallel kinematic machine for multidirectional additive manufacturing[J]. Journal of Manufacturing Science and Engineering, 2015, 137(2):021005.
[76] EDOIMIOYA N, CHOU C H, OKWUDIRE C E. Vibration compensation of delta 3D printer with position-varying dynamics using filtered B-splines[J]. Int J Adv Manuf Technol, 2023, 125:2851-2868.
[77] ZHAO D, LI T, SHEN B, et al. A multi-DOF rotary 3D printer:machine design, performance analysis and process planning of curved layer fused deposition modeling (CLFDM)[J]. Rapid Prototyping Journal, 2020, 26(6):1079-1093.
[78] WU R, PENG H, GUIMBRETIÈRE F, et al. Printing arbitrary meshes with a 5DOF wireframe printer[J]. ACM Transactions on Graphics, 2016, 35(4):1-9.
[79] DAI C, WANG C C L, WU C, et al. Support-free volume printing by multi-axis motion[J]. ACM Transactions on Graphics, 2018, 37(4):1-14.
[80] JENSEN M L, MAHSHID R, D'ANGELO G, et al. Toolpath strategies for 5DOF and 6DOF extrusion-based additive manufacturing[J]. Applied Sciences, 2019, 9(19):4168.
[81] DING Y, DWIVEDI R, KOVACEVIC R. Process planning for 8-axis robotized laser-based direct metal deposition system:A case on building revolved part[J]. Robotics and Computer-Integrated Manufacturing, 2017, 44:67-76.
[82] LI L, HAGHIGHI A, YANG Y. A novel 6-axis hybrid additive-subtractive manufacturing process:Design and case studies[J]. Journal of Manufacturing Processes, 2018, 33:150-160.
[83] YERAZUNIS W S, BARNWELL III J C, NIKOVSKI D N. Strengthening ABS, nylon, and polyester 3D printed parts by stress tensor aligned deposition paths and five-axis printing[C]//Proceedings of the 2016 International Solid Freeform Fabrication Symposium, 2016. University of Texas at Austin.
[84] SHEMBEKAR A V, YOON Y J, KANYUCK A, et al. Trajecttory planning for conformal 3D printing using non-planer layers[C]//Proceedings of the 38th American-Society-of-Mechanical-Engineers (ASME) Computers and Information in Engineering (CIE) Conference in Conjunction with the International Design Engineering Technical Conferes (IDETC), Quebec City, CANADA, F Aug 26-29, 2018. Amer. Soc. Mechanical Engineers:New York, 2018.
[85] MCCLINTON E, WYLIE B, MOORE C A, et al. Creating a high and homogenous resolution workspace for SCARA based 3D Printers[C]//Proceedings of the 6th International Conference on Mechanical, Materials and Manufacturing (ICMMM), Boston, MA, Oct 12-14, 2019. IOP Publishing Ltd:BRISTOL, 2019.
[86] BEHANDISH M, NELATURI S, DE KLEER J. Automated process planning for hybrid manufacturing[J]. Computer-Aided Design, 2018, 102:115-127.
[87] LIAO Z R, AXINTE D A. On chip formation mechanism in orthogonal cutting of bone[J]. Int J Mach Tools Manuf, 2016, 102:41-55.
[88] STRONG D, SIRICHAKWAL I, MANOGHARAN G P, et al. Current state and potential of additive-hybrid manufacturing for metal parts[J]. Rapid Prototyping Journal, 2017, 23(3):577-588.
[89] WANG Y, GU Z, SONG L, et al. Speeding up 3D printing using multi-head slicing algorithms[C]//20175th International Conference on Enterprise Systems (ES). 2017:99-106.10.1109/es.2017.23
[90] HUANG Y J, ZHANG J Y, HU X, et al. Framefab:robotic fabrication of frame shapes[J]. Acm Transactions on Graphics, 2016, 35(6):11.
[91] CHALVIN M, CAMPOCASSO S, HUGEL V, et al. Layer-by-layer generation of optimized joint trajectory for multi-axis robotized additive manufacturing of parts of revolution[J]. Robotics and Computer-Integrated Manufacturing, 2020, 65:101960.
[92] SHEMBEKAR A V, YOON Y J, KANYUCK A, et al. Generating robot trajectories for conformal three-dimensional printing using nonplanar layers[J]. Journal of Computing and Information Science in Engineering, 2019, 19(3):031011.
[93] TSAO C-C, CHANG H-H, LIU M-H, et al. Freeform additive manufacturing by vari-directional vari-dimensional material deposition[J]. Rapid Prototyping Journal, 2018, 24(2):379-394.
[94] AHARI H, KHAJEPOUR A, BEDI S. Optimization of slicing direction in laminated tooling for volume deviation reduction[J]. Assembly Automation, 2013, 33(2):139-148.
[95] DAS P, CHANDRAN R, SAMANT R, et al. Optimum part build orientation in additive manufacturing for minimizing part errors and support structures[J]. Procedia Manufacturing, 2015, 1:343-354.
[96] LUO N, WANG Q. Fast slicing orientation determining and optimizing algorithm for least volumetric error in rapid prototyping[J]. The International Journal of Advanced Manufacturing Technology, 2015, 83(5-8):1297-1313.
[97] DANJOU S, KÖHLER P. Improving part quality and process efficiency in layered manufacturing by adaptive slicing[J]. Virtual and Physical Prototyping, 2010, 5(4):183-8.
[98] SINGHAL S K, JAIN P K, PANDEY P M. Adaptive slicing for sls prototyping[J]. Computer-Aided Design and Applications, 2008, 5(1-4):412-423.
[99] YU C, QIE L, JING S, et al. Personalized design of part orientation in additive manufacturing[J]. Rapid Prototyping Journal, 2019, 25(10):1647-1660.
[100] HERNANDEZ-CONTRERAS A, RUIZ-HUERTA L, CABALLERO-RUIZ A, et al. Extended CT void analysis in fdm additive manufacturing components[J]. Materials (Basel), 2020, 13(17):3831.
[101] BADINI C, PADOVANO E, DE CAMILLIS R, et al. Preferred orientation of chopped fibers in polymer-based composites processed by selective laser sintering and fused deposition modeling:Effects on mechanical properties[J]. Journal of Applied Polymer Science, 2020, 137(38):49152.
[102] PARK S J, PARK J H, LEE K H, et al. Deposition strength of specimens manufactured using fused deposition modeling type 3D printer[J]. Polymer Korea, 2016, 40(6):846.
[103] WANG K, LI S, RAO Y, et al. Flexure behaviors of abs-based composites containing carbon and kevlar fibers by material extrusion 3D printing[J]. Polymers (Basel), 2019, 11(11):1879.
[104] ULU E, KORKMAZ E, YAY K, et al. Enhancing the structural performance of additively manufactured objects through build orientation optimization[J]. Journal of Mechanical Design, 2015, 137(11):9.
[105] ALAFAGHANI A, QATTAWI A, ALRAWI B, et al. Experimental optimization of fused deposition modelling processing parameters:A design-for-manufacturing approach[C]//Proceedings of the 45th SME North American Manufacturing Research Conference (NAMRC), Univ So Calif, Los Angeles, CA, F Jun 04-08, 2017, Elsevier Science Bv:AMSTERDAM, 2017.
[106] ZHAO H M, HE Y, FU J Z, et al. Inclined layer printing for fused deposition modeling without assisted supporting structure[J]. Robotics and Computer-Integrated Manufacturing, 2018, 51:1-13.
[107] GORSKI F, WICHNIAREK R, KUCZKO W, et al. Selection of fused deposition modeling process parameters using finite element analysis and genetic algorithms[J]. J Mult-Valued Log Soft Comput, 2019, 32(3-4):293-311.
[108] LALEGANI DEZAKI M, MOHD ARIFFIN M K A. The effects of combined infill patterns on mechanical properties in FDM process[J]. Polymers (Basel), 2020, 12(12):2792.
[109] KHADILKAR A, WANG J, RAI R. Deep learning-based stress prediction for bottom-up SLA 3D printing process[J]. The International Journal of Advanced Manufacturing Technology, 2019, 102(5-8):2555-2569.
[110] SOMASHEKARA M A, NAVEENKUMAR M, KUMAR A, et al. Investigations into effect of weld-deposition pattern on residual stress evolution for metallic additive manufacturing[J]. The International Journal of Advanced Manufacturing Technology, 2016, 90(5-8):2009-2025.
[111] ZHOU Y, LU H, WANG G, et al. Voxelization modelling based finite element simulation and process parameter optimization for fused filament fabrication[J]. Materials & Design, 2020, 187:108409.
[112] KIM D J, HONG S G, DUARTE C A. Generalized finite element analysis using the preconditioned conjugate gradient method[J]. Applied Mathematical Modelling, 2015, 39(19):5837-5848.
[113] THOMPSON M K, THOMPSON J M. ANSYS mechanical APDL for finite element analysis[M]. Oxford:Butterworth-Heinemann, 2017.
[114] CHANG J, ZHANG J J, YOU L H. Physically-based deformations:Copy and paste[J]. Visual Comput, 2007, 23(1):71-82.
[115] ZENG K, PAL D, PATIL N, et al. A new dynamic mesh method applied to the simulation of selective laser melting[C]//Proceedings of the 2013 International Solid Freeform Fabrication Symposium, F, 2013. University of Texas at Austin.
[116] LIU J, ANDERSON K L, SRIDHAR N. Direct simulation of polymer fused deposition modeling (FDM)-An implementation of the multi-phase viscoelastic solver in openfoam[J]. International Journal of Computational Methods, 2019, 17(01).
[117] PANDA B N, SHANKHWAR K, GARG A, et al. Performance evaluation of warping characteristic of fused deposition modelling process[J]. The International Journal of Advanced Manufacturing Technology, 2016, 88(5-8):1799-1811.
[118] ALIHEIDARI N, CHRIST J, TRIPURANENI R, et al. Interlayer adhesion and fracture resistance of polymers printed through melt extrusion additive manufacturing process[J]. Materials & Design, 2018, 156:351-361.
[119] SPOERK M, ARBEITER F, CAJNER H, et al. Parametric optimization of intra- and inter-layer strengths in parts produced by extrusion-based additive manufacturing of poly(lactic acid)[J]. Journal of Applied Polymer Science, 2017, 134(41):45401.