Abstract
Fused deposition modeling (FDM) is gaining distinct advantages because of its ability to fabricate the 3D physical prototypes without the restrictions of geometric complexities, while when it comes to accuracy and efficiency, the advantages of FDM is not distinct, and so how to improve them is worthy of study. Focusing on process parameter optimization, such parameters as line width compensation, extrusion velocity, filling velocity, and layer thickness are selected as control factors, input variables, and dimensional error, warp deformation, and built time are selected as output responses, evaluation indexes. Experiment design is assigned according to uniform experiment design, and then the three output responses are converted with fuzzy inference system to a single comprehensive response. The relation between the comprehensive response and the four input variables is derived with second-order response surface methodology, the correctness of which is further validated with artificial neural network. Fitness function is created using penalty function and is solved with genetic algorithm toolbox in Matlab software. With confirmation test, the results are obtained preferring to the results of the experiment 1 with the best comprehensive response among the 17 experiment runs, which confirms that the proposed approach in this study can effectively improve accuracy and efficiency in the FDM process.
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References
Chang DY, Huang BH (2011) Studies on profile error and extruding aperture for the RP parts using the fused deposition modeling process. Int J Adv Manuf Technol 53(9–12):1027–1037
Mastoid SH, Song WQ (2004) Development of new metal/polymer materials for rapid tooling using fused deposition modeling. Mater Des 25(7):587–594
Nikzad M, Masood SH, Sbarski I (2011) Thermo-mechanical properties of a highly filled polymeric composites for fused deposition modeling. Mater Des 32:3448–3456
Zhou JG, Herscovivi D, Chen CC (2000) Parametric process optimization to improve the accuracy of rapid prototyped stereo-lithography parts. Int J Mach Tools Manuf 40:363–379
Cao W, Miyamoto Y (2003) Direct slicing from AutoCAD solid models for rapid prototyping. Int J Adv Manuf Technol 21(10–11):739–742
Li Y, Zhang J (2013) Multi-criteria GA-based Pareto optimization of building direction for rapid prototyping. Int Adv Manuf Technol 2013(69):1819–1831
Noriega A, Blanco D, Alvarez BJ, Garcia A (2013) Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm. Int J Adv Manuf Technol 69:2301–2313
Anitha R, Arunachalam S, Radhkrishnan P (2001) Critical parameters influencing the quality of prototypes in fused deposition modeling. J Mater Process Technol 118:385–388
Lee BH, Abdullah J, Khan ZA (2005) Optimization of rapid prototyping parameters for production of flexible ABS object. J Mater Process Technol 169:54–61
Huang Y, Ming WY, Guo JW, Zhang Z, Liu GD, Li MZ, Zhang GJ (2013) Optimization of cutting conditions of YG15 on rough and finish cutting in WEDM based on statistical analysis. Int Adv Manuf Technol 2013(69):993–1008
Li XY (2009) Multi-object optimal design of rapid prototyping based on uniform experiment. Tsinghua Sci Technol 14:206–211
Sharma N, Khanna R, Gupta RD, Sharma R (2013) Modeling and multi-response optimization on WEDM for HSLA by RSM. Int J Adv Manuf Technol 67:2269–2281
Kamguem R, Djebara A, Songmene V (2013) Investigation on surface finish and metallic particle emission during machining of aluminum alloys using response surface methodology and desirability functions. Int Adv Manuf Technol 69:1283–1298
Sood AK, Ohdar RK, Mahapatra SS (2010) Parametric appraisal of mechanical property of fused deposition modeling processed parts. Mater Des 31:287–295
Sood AK, Ohdar RK, Mahapatra SS (2009) Improving dimensional accuracy of fused deposition modeling processed part using grey Taguchi. Mater Des 30:4243–4252
Pradhan MK (2013) Estimating the effect of process parameters on MRR, TWR and radial overcut of EDMed AISI D2 tool steel by RSM and GRA coupled with PCA. Int J Adv Manuf Technol 68:591–605
Rajyalakshmi G, Venkata Ramaiah P (2013) Multiple process parameter optimization of wire electrical discharge machining on Inconel 825 using Taguchi grey relational analysis. Int J Adv Manuf Technol 69:1249–1262
Zalnezhad E, Ahmed ADS, Hamdi M (2013) A fuzzy logic based model to predict surface hardness of thin film TiN coating on aerospace AL7075-T6 alloy. Int J Adv Manuf Technol 68:415–423
Taeng YF, Chen FC (2007) Multi-objective optimization of high-speed electrical discharge machining process using a Taguchi fuzzy-based approach. Mater Des 28:1159–68
Lin JL, Lin CL (2005) The use of grey-fuzzy logic for the optimization of the manufacturing process. J Mater Process Technol 160:9–14
Chiang KT (2007) The optimal process conditions of an injection-modeled thermoplastic part with a thin shell feature using grey-fuzzy logic: a case study on machining the PC/ABS cell phone shell. Mater Des 28:1851–1860
Shojaeefard MH, Khalkhali A, Akbari M et al (2013) Application of Taguchi optimization technique in determining aluminum to brass friction stir welding parameters. Mater Des 52:587–592
Bas D, Boyaci IH (2007) Modeling and optimization I: usability of response surface methodology. J Food Eng 78:836–845
Sivasakthivel PS, Sudhakaran R (2013) Optimization of machining parameters on temperature rise in end milling of AL 6063 using response surface methodology and genetic algorithm. Int J Adv Manuf Technol 67:2313–2323
Hou TH, Su CH, Liu WL (2007) Parameters optimization of a nano-particle wet milling process using the Taguchi method, response surface method and genetic algorithm. Powder Technol 173:153–162
Krajnik P, Kopac J, Sluga A (2005) Design of grinding factors based on response surface methodology. J Mater Process Technol 162–163: 629–636
Low KL, Tan SH, Zein SHS, Mcphail, Boccaccini DAR (2011) Optimization of the mechanical properties of calcium phosphate/multi-walled carbon nanotubes/bovine serum albumin composites using response surface methodology. Mater Des 32:3312–3319
Bayhan M, Onel K (2010) Optimization of reinforcement content and sliding distance for AlSi7Mg/SiCp composites using response surface methodology. Mater Des 31:3015–3022
Zhang JF, Peng AH (2012) Process parameters optimization of FDM based on robust design. Trans Nanjing Univ Aeronaut Astronaut 29:62–67
Peng AH, Wang ZM (2010) Optimization of process parameters in FDM based on degree of grey incidence. Mech Sci Technol Aerosp Eng 29:625–629
Bezerra MA, Santelli RE, Oliveira EP, Villar LS, Escaleira LA (2008) Response surface methodology (RSM) as a tool for optimization in analytic chemistry. Int J Pur Appl Anal Chem 76:965–977
Wei ZS (2010) Course in probability theory and mathematical statistics. Higher Education Press, Beijing
Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Mach Stud 3:1–13
Zhou GL, Guo DM, Jia ZY, Liu SF (2002) Research on process parameter optimization of fused deposition modeling. J Dalian Univ Technol 42:446–450
Li JF, Liao HL, Ding CX, Coddet C (2005) Optimizing the plasma spray process parameter of yttria stabilized zirconia coatings using a uniform design of experiments. J Mater Process Technol 160:34–42
Zhao XM (2006) Experiment design methods. Science Press, Beijing
Lambiase F (2013) Optimization of shape rolling sequences by integrated artificial intelligent techniques. Int J Adv Manuf Technol 68:443–452
Zhong YG, Xue K, Shi DY (2013) An improved artificial network for laser welding parameter selection and prediction. Adv Manuf Technol 68:755–762
Hornik K (1991) Approximation capabilities of multilayer feed forward networks. IEEE Trans Neural Netw 4:251–257
Tajdari M, Mehraban AG, Khoogar AR (2010) Shear strength prediction of Ni-Ti alloys manufactured by powder metallurgy using fuzzy rule-based model. Mater Des 31:1180–1185
Han LQ (2008) Theory and applications of intelligent control. China Machine Press, Beijing
Rajendra M, Jena PC, Raheman H (2009) Prediction of optimized pretreatment process for bio-diesel production using ANN and GA. Fuel 88:868–875
Wang TM, Xi JT, Jin Y (2006) Prototype warp deformation in the FDM process. Chin J Mech Eng 42:233–238
Gill S, Singh J (2001–2009) Artificial intelligent modeling to predict tensile strength of inertia friction-welded pipe joints. Int Adv Manuf Technol 2013:69
Roshan SB, Jooibari MB, Ehoodi, Teimouri R, Asgharzadeh-Ahmadi G, Falahati-Naghibi M, Sohrabpoor H (2013) Optimization of friction stir welding process of AA7075 aluminum alloy to achieve desirable mechanical properties using ANFIS models and simulated annealing algorithm. Int Adv Manuf Technol 69:1803–1818
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Peng, A., Xiao, X. & Yue, R. Process parameter optimization for fused deposition modeling using response surface methodology combined with fuzzy inference system. Int J Adv Manuf Technol 73, 87–100 (2014). https://doi.org/10.1007/s00170-014-5796-5
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DOI: https://doi.org/10.1007/s00170-014-5796-5