nach oben

The International Journal of Advanced Manufacturing Technology

Erschienen in:

12.06.2021 | ORIGINAL ARTICLE

Genetic algorithm for the reduction printing time and dimensional precision improvement on 3D components printed by Fused Filament Fabrication

verfasst von: Julián I. Aguilar-Duque, Cesar O. Balderrama-Armendáriz, Cesar A. Puente-Montejano, Arturo S. Ontiveros-Zepeda, Jorge L. García-Alcaraz

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 11-12/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Additive manufacturing (AM) has managed to stand out globally, with a financial forecast growth of $26.28 billion by 2027, where $14.54 billion will be generated due to fused filament fabrication (FFF) technology. Despite this significant growth, researchers in the FFF field are working on solving problems associated with productivity and efficiency, where finishing components are one of the most critical aspects. This paper reports the implementation of a genetic algorithm (GA) to optimize the extruder path during the FFF process, comparing the results based on the dimensional finish of a printed component with the traditional method (C_TRAD) with a printed component with the implementation of a GA that modifies the 3D printing path (C_MOD). The methodology includes (1) FFF of the C_TRAD and FFF of the C_MOD and (2) measuring and comparing 118 dimensions associated with each attribute of C_TRAD and C_MOD using a coordinate measuring machine (CMM). Comparisons are made among the computer-aided design (CAD), the C_TRAD, and the C_MOD. Results show that 83.9% of the dimensions of the C_TRAD components are different from the dimensions of the components defined in the CAD, and 81% of the dimensions of the C_MOD components are different from the dimensions of the components defined in the CAD. Finally, 53% of the dimensions in the C_TRAD are different from those in the C_MOD. The implementation of the GA helps reduce the lead time of the subject of study by 11.2%, ensuring that the surface texture of C_TRAD and C_MOD has the same behavior and is greater than those defined in CAD design. Also, it is identified that there is no significant dimensional difference between the C_TRAD and the C_MOD.

Vorheriger Artikel Multi-objective optimization of solvent cast 3D printing process parameters for fabrication of biodegradable composite stents

Nächster Artikel Evaluation of MQL performances using various nanofluids in turning of AISI 304 stainless steel

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Bogdanov D (2019) 3D printing technology as a trigger for the Fourth Industrial Revolution: new challenges to the legal system. Perm U Herald Jurid Sci 44:238. https://doi.org/10.17072/1995-4190-2019-44-238-260CrossRef

Cortes C (2017) La impresión 3D revoluciona la industria automotriz. Retrieved from https://manufactura.mx/columnas/2017/08/17/la-impresion-3d-revoluciona-la-industria-automotriz

Esposito Corcione C, Palumbo E, Masciullo A, Montagna F, Torricelli MC (2018) Fused deposition modeling (FDM): an innovative technique aimed at reusing Lecce stone waste for industrial design and building applications. Constr Build Mater 158:276–284. https://doi.org/10.1016/j.conbuildmat.2017.10.011CrossRef

Ford S, Minshall T (2019) Invited review article: where and how 3D printing is used in teaching and education. Additive Manufacturing 25:131–150. https://doi.org/10.1016/j.addma.2018.10.028CrossRef

DDDrop (2020) What are the advantages of the FDM technology? Retrieved from https://www.dddrop.com/fdm-technology/

Nikitakos N, Dagkinis I, Papachristos D, Georgantis G, Kostidi E (2020) Economics in 3D printing. In 3D Printing: Applications in Medicine and Surgery 85-95 Elsevier Inc

SmartTech (2019) Additive manufacturing opportunities in automotive. 2019. Retrieved from https://www.smartechanalysis.com/reports/automotive-additive-manufacturing/

Wirth M (2020) What the 3D printing community teaches us about innovation. Malaga, Spain: Vernon Press

Beck R, Chaves P, Goyanes A, Vukosavljevic B, Buanz A, Windbergs M et al (2017) 3D printed tablets loaded with polymeric nanocapsules: an innovative approach to produce customized drug delivery systems. Int J Pharm 528(1-2):268–279. https://doi.org/10.1016/j.ijpharm.2017.05.074CrossRef

10.

Goyanes A, Fina F, Martorana A, Sedough D, Gaisford S, Basit AW (2017) Development of modified release 3D printed tablets (printlets) with pharmaceutical excipients using additive manufacturing. Int J Pharm 527(1-2):21–30. https://doi.org/10.1016/j.ijpharm.2017.05.021CrossRef

11.

Pucci JU, Christophe BR, Sisti JA, Connolly ES Jr (2017) Three-dimensional printing: technologies, applications, and limitations in neurosurgery. Biotechnol Adv 35(5):521–529. https://doi.org/10.1016/j.biotechadv.2017.05.007CrossRef

12.

Bourell D, Stucker B, Espalin D, Arcaute K, Rodriguez D, Medina F et al (2010) Fused deposition modeling of patient-specific polymethylmethacrylate implants. Rapid Prototyp J 16:164–173. https://doi.org/10.1108/13552541011034825CrossRef

13.

Choi JW, Kim N (2015) Clinical application of three-dimensional printing technology in craniofacial plastic surgery. Arch Plast Surg 42(3):267–277. https://doi.org/10.5999/aps.2015.42.3.267CrossRef

14.

Tan ET, Ling JM, Dinesh SK (2016) The feasibility of producing patient-specific acrylic cranioplasty implants with a low-cost 3D printer. J Neurosurg 124(5):1531–1537. https://doi.org/10.3171/2015.5.JNS15119CrossRef

15.

Fischer A, Rommel S, Bauernhansl T (2013) New fiber matrix process with 3D fiber printer–a strategic in-process integration of endless fibers using fused deposition modeling (FDM). Paper presented at the IFIP International Conference on Digital Product and Process Development Systems

16.

Tateno T, Nishie S (2018) Vibration characteristics of unit cell structures fabricated by multi-material additive manufacturing. In Mechanics of Additive and Advanced Manufacturing 9 79-81 Springer

17.

Raja V, Zhang S, Garside J, Ryall C, Wimpenny D (2006) Rapid and cost-effective manufacturing of high-integrity aerospace components. Int J Adv Manuf Technol 27(7-8):759–773. https://doi.org/10.1007/s00170-004-2251-zCrossRef

18.

Arivarasi A, Kumar A (2019) Classification of challenges in 3D printing for combined electrochemical and microfluidic applications: a review. Rapid Prototyp J 25(7):1328–1346. https://doi.org/10.1108/RPJ-05-2018-0115CrossRef

19.

Zhang X, Zheng Y, Suresh V, Wang S, Li Q, Li B, Qin H (2020) Correlation approach for quality assurance of additive manufactured parts based on optical metrology. J Manuf Process 53:310–317. https://doi.org/10.1016/j.jmapro.2020.02.037CrossRef

20.

Gaffi C, Gangula B, Illinda P (2017) 3D opportunity in the automotive industry “additive manufacturing hits the road”. Retrieved from https://www2.deloitte.com/content/dam/insights/us/articles/additive-manufacturing-3d-opportunity-in-automotive/DUP_707-3D-Opportunity-Auto-Industry_MASTER.pdf

21.

Weeren RV, Agarwala M, Jamalabad V, Bandyopadhyay A, Vaidyanathan R, Langrana N, . . . Ballard C (1995) Quality of parts processed by fused deposition. Paper presented at the 1995 International Solid Freeform Fabrication Symposium, Austin

22.

Campbell I, Dicknes P (1994) Rapid prototyping: a global view. Paper presented at the International Solid Free Forma Fabrications Symposium, Austin

23.

Mohamed OA, Masood SH, Bhowmik JL (2015) Optimization of fused deposition modeling process parameters: a review of current research and future prospects. Adv Manuf 3(1):42–53. https://doi.org/10.1007/s40436-014-0097-7CrossRef

24.

Dong G, Wijaya G, Tang Y, Zhao YF (2018) Optimizing process parameters of fused deposition modeling by Taguchi method for the fabrication of lattice structures. Additive Manufacturing 19:62–72. https://doi.org/10.1016/j.addma.2017.11.004CrossRef

25.

Sheoran AJ, Kumar H (2020) Fused deposition modeling process parameters optimization and effect on mechanical properties and part quality: review and reflection on present research. Materials Today: Proceedings 21:1659–1672. https://doi.org/10.1016/j.matpr.2019.11.296CrossRef

26.

Sukindar NA, Ariffin MKA, Baharudin BTHT, Jaafar CNA, Ismail MIS (2016) Analyzing the effect of nozzle diameter in fused deposition modeling for extruding polylactic acid using open source 3d printing. Jurnal Teknologi 78(10). https://doi.org/10.11113/jt.v78.6265

27.

Bhate D (2016) Constitutive modeling of 3D printed FDM parts. Retrieved from http://www.ansys-blog.com/constitutive-modeling-of-3d-printed-fdm-parts

28.

Chen C-J, Tseng C-S (1996) The path and location planning of workpieces by genetic algorithms. J Intell Manuf 7(1):69–76. https://doi.org/10.1007/BF00114139CrossRef

29.

Castelino K, D'Souza R, Wright PK (2003) Toolpath optimization for minimizing airtime during machining. J Manuf Syst 22(3):173–180. https://doi.org/10.1016/S0278-6125(03)90018-5CrossRef

30.

Agarwala MK, Jamalabad VR, Langrana NA, Safari A, Whalen PJ, Danforth SC (1996) Structural quality of parts processed by fused deposition. Rapid Prototyp J 2(4):4–19. https://doi.org/10.1108/13552549610732034CrossRef

31.

Cus F, Balic J, Zuperl U (2009) Hybrid ANFIS-ants system based optimisation of turning parameters. Journal of Achievements in Materials and Manufacturing Engineering 36(1):79–86

32.

Oysu C, Bingul Z (2007) Tool path optimization using genetic algorithms. Paper presented at the GEM

33.

Oysu C, Bingul Z (2009) Application of heuristic and hybrid-GASA algorithms to tool-path optimization problem for minimizing airtime during machining. Eng Appl Artif Intell 22(3):389–396. https://doi.org/10.1016/j.engappai.2008.10.005CrossRef

34.

Ülker E, Turanalp ME, Halkaci HS (2009) An artificial immune system approach to CNC tool path generation. J Intell Manuf 20(1):67–77. https://doi.org/10.1007/s10845-008-0104-6CrossRef

35.

Nguyen V, Huynh T, Nguyen T, Tran T (2020) Single and multi-objective optimization of processing parameters for fused deposition modeling in 3D printing technology. International Journal of Automotive and Mechanical Engineering 17(1):7542–7551. https://doi.org/10.15282/ijame.17.1.2020.03.0558CrossRef

36.

Wang W, Shao H, Liu X, Yin B (2020) Printing direction optimization through slice number and support minimization. IEEE Access

37.

Raju M, Gupta MK, Bhanot N, Sharma VS (2019) A hybrid PSO–BFO evolutionary algorithm for optimization of fused deposition modelling process parameters. J Intell Manuf 30(7):2743–2758. https://doi.org/10.1007/s10845-018-1420-0CrossRef

38.

Deswal S, Narang R, Chhabra D (2019) Modeling and parametric optimization of FDM 3D printing process using hybrid techniques for enhancing dimensional preciseness. International Journal on Interactive Design and Manufacturing (IJIDeM) 13(3):1197–1214. https://doi.org/10.1088/1757-899X/114/1/012109CrossRef

39.

Wah PK, Murty KG, Joneja A, Chiu LC (2002) Tool path optimization in layered manufacturing. IIE Trans 34(4):335–347. https://doi.org/10.1080/07408170208928874CrossRef

40.

Agrawal RK, Pratihar D, Choudhury AR (2006) Optimization of CNC isoscallop free form surface machining using a genetic algorithm. Int J Mach Tools Manuf 46(7-8):811–819. https://doi.org/10.1016/j.ijmachtools.2005.07.028CrossRef

41.

Bo ZW, Hua LZ, Yu ZG (2006) Optimization of process route by genetic algorithms. Robot Comput Integr Manuf 22(2):180–188. https://doi.org/10.1016/j.rcim.2005.04.001CrossRef

42.

Onwubolu G, Clerc M (2004) Optimal path for automated drilling operations by a new heuristic approach using particle swarm optimization. Int J Prod Res 42(3):473–491. https://doi.org/10.1080/00207540310001614150CrossRefMATH

43.

Palanisamy P, Rajendran I, Shanmugasundaram S (2007) Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations. Int J Adv Manuf Technol 32(7-8):644–655. https://doi.org/10.1007/s00170-005-0384-3CrossRef

44.

Al-Janan DH, Liu T-K (2016) Path optimization of CNC PCB drilling using hybrid Taguchi genetic algorithm. Kybernetes. 45:107–125CrossRef

45.

Chentsov PA, Petunin AA (2016) Tool routing problem for CNC plate cutting machines. IFAC-PapersOnLine 49(12):645–650. https://doi.org/10.1016/j.ifacol.2016.07.762CrossRef

46.

Dalavi AM, Pawar PJ, Singh TP (2016) Tool path planning of hole-making operations in ejector plate of injection mould using modified shuffled frog leaping algorithm. Journal of Computational Design and Engineering 3(3):266–273. https://doi.org/10.1016/j.jcde.2016.04.001CrossRef

47.

MathWorks (2013) MATLAB (Version R2013a). Standford

48.

Aguilar-Duque JI, García-Alcaraz JL, Hernández-Arellano JL (2020) Geometric considerations for the 3D printing of components using fused filament fabrication. Int J Adv Manuf Technol 109(1):171–186. https://doi.org/10.1007/s00170-020-05523-3CrossRef

Titel: Genetic algorithm for the reduction printing time and dimensional precision improvement on 3D components printed by Fused Filament Fabrication
verfasst von: Julián I. Aguilar-Duque
Cesar O. Balderrama-Armendáriz
Cesar A. Puente-Montejano
Arturo S. Ontiveros-Zepeda
Jorge L. García-Alcaraz
Publikationsdatum: 12.06.2021
Verlag: Springer London
Erschienen in: The International Journal of Advanced Manufacturing Technology / Ausgabe 11-12/2021
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-021-07314-w

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 11-12/2021

Modeling and prediction of cutting temperature in the machining of H13 hard steel of multi-layer coated cutting tools

A simple method for dimensional measurement of ring-shaped objects using image processing technique

Finite element analysis of temperature uniformity in transverse induction heating process in ESP rolling

Calculation and analysis of rolling force during aluminum alloy thick plate snake/gradient temperature rolling with different roll diameters

Manufacturing and properties of composite material from demolition debris, plaster waste, and lime production waste to decrease ecological problems of cities

Effect of heat input on macro morphology and porosity of laser-MIG hybrid welded joint for 5A06 aluminum alloy

Premium Partner

Marktübersichten