3D-FDM'de Charpy Darbe Testinin Yapay Zeka ile Optimizasyonu

Year 2024, Volume: 10 Issue: 1, 12 - 26, 30.04.2024

Mehmet Altuğ

Abstract

Çalışmada FDM yöntemiyle üretilen ABS parçaların darbe enerjisi emilim oranları incelenmiştir. Charpy darbe testi sonuçları, katman dağılımı, yazdırma hızı, destek açısı, yapı yönü, çentik tipi ve dolgu tip kullanılarak belirlendi. Çalışmada Box-behnken deneysel tasarım tasarımı kullanıldı. Çentik darbe numuneleri 3D yazıcıda ABS malzemeden üretildi. Daha sonra darbe test cihazında charpy darbe testleri yapıldı. Veriler Minitab 21 programı kullanılarak değerlendirildi. Daha sonra bu sonuçlara dayanarak DL ve ELM modelleri oluşturuldu. En iyi sonuçlar 0,09 mm katman kalınlığında 0,844 kJ/m2, 60 mm/s baskı hızında ve 30° destek açısında darbe enerjisi emilimi 0,803 kJ/m2 olarak belirlendi. En yüksek darbe enerjisinin edge yönünde 0,841 kj/m2, U çentik tipinde 0,827 kJ/m2, full dolgulu tipinde 0,777 kJ/m2 olarak elde edildi. DL'de adam optimizasyon algoritması, tanh ise aktivasyon fonksiyonudur. DL, MSE değeri 0,000923, r2 ise 0,97427 olarak hesaplandı. ELM'de aktivasyon fonksiyonu girişte sigmoid, çıkışta ise doğrusaldır.

Keywords

FDM, charpy darbe testi, konumlandırma, çentik tipi, derin öğrenme, aşırı öğrenme makineleri

Charpy Impact Test in 3D-FDM and Optimization with Artificial Intelligence

Abstract

In the study, the rates of impact energy absorption of ABS fractures produced by the FDM method were examined. Charpy impact test results were determined using layer distribution, printing speed, support angle, build orientation, notch type, and unfill type. Box-behnken experimental design design in the study. Notch impact samples are produced on an ABS 3D printer. Then, charpy impact tests were performed on the impact test device. Data were evaluated using the Minitab 21 program. Later, DL and ELM file models were created based on this development. The best results were obtained as 0.844 kJ/m2 with a layer thickness of 0.09 mm. At 60 mm/s printing speed and 30° support angle, the impact energy absorption is 0.803 kJ/m2. The extinction edge of the highest impact energy is 0.841 kj/m2. The most effective impact absorption was obtained as 0.827 kJ/m2 in the U notch type. In the full infill type, impact energy absorption is obtained as 0.777 kJ/m2. In DL, man is the programming and tanh is the activation function. DL, MSE value was calculated as 0.000923, r2 was calculated as 0.97427. In ELM, the activation function is sigmoidal at the input and linear at the output.

Keywords

FDM, Charpy impact test, Build orientation, Notch, Deep learning, Extreme learning machines

References

• [1] G. D. Goh, S. Agarwala, G. L. Goh, V. Dikshit, S. L. Sing, and W. Y. Yeong, “Additive manufacturing in unmanned aerial vehicles (UAVs): Challenges and potential,” Aerosp. Sci. Technol., vol. 63, pp. 140–151, 2017, doi: 10.1016/j.ast.2016.12.019.
• [2] M. D. Monzón, Z. Ortega, A. Martínez, and F. Ortega, “Standardization in additive manufacturing: activities carried out by international organizations and projects,” Int. J. Adv. Manuf. Technol., vol. 76, no. 5–8, pp. 1111–1121, 2015, doi: 10.1007/s00170-014-6334-1.
• [3] C. Palanisamy, R. Raman, and P. kumar Dhanraj, “A review on mechanical properties of polyjet and FDM printed parts,” Additive Manufacturing, vol. 79, no. 9. Springer Berlin Heidelberg, 2022. doi: 10.1007/s00289-021-03899-0
• [4] S. Singh, S. Ramakrishna, and R. Singh, “Material issues in additive manufacturing: A review,” J. Manuf. Process., vol. 25, pp. 185–200, Jan. 2017. doi: 10.1016/j.jmapro.2016.11.006
• [5] S. Pattnaik, P. K. Jha, and D. B. Karunakar, “A review of rapid prototyping integrated investment casting processes,” Proc. Inst. Mech. Eng. Part L J. Mater. Des. Appl., vol. 228, no. 4, pp. 249–277, Oct. 2014. doi: 10.1177/1464420713479257
• [6] N. Anwer, B. Schleich, L. Mathieu, and S. Wartzack, “From solid modelling to skin model shapes: Shifting paradigms in computer-aided tolerancing,” CIRP Ann. - Manuf. Technol., vol. 63, no. 1, pp. 137–140, 2014. doi: 10.1016/j.cirp.2014.03.103
• [7] S. H. Chiu, K. T. Chen, S. T. Wicaksono, J. R. Tsai, and S. H. Pong, “Process parameters optimization for area-forming rapid prototyping system,” Rapid Prototyp. J., vol. 21, no. 1, pp. 70–78, Jan. 2015. doi: 10.1108/RPJ-12-2012-0114
• [8] K. L. Alvarez C., R. F. Lagos C., and M. Aizpun, “Investigating the influence of infill percentage on the mechanical properties of fused deposition modelled ABS parts,” Ing. e Investig., vol. 36, no. 3, pp. 110–116, 2016. doi: 10.15446/ing.investig.v36n3.56610
• [9] P. Mantada, R. Mendricky, and J. Safka, “Parameters influencing the precision of various 3D printing technologies,” MM Sci. J., vol. 2017, no. December, pp. 2004–2012, 2017. doi: 10.17973/MMSJ.2017_12_201776
• [10] R. V. Aroca, C. E. H. Ventura, I. De Mello, and T. F. P. A. T. Pazelli, “Sequential additive manufacturing: automatic manipulation of 3D printed parts,” Rapid Prototyp. J., vol. 23, no. 4, pp. 653–659, Jun. 2017. doi:10.1108/RPJ-02-2016-0029
• [11] L. Cheng, P. Zhang, E. Biyikli, J. Bai, J. Robbins, and A. To, “Efficient design optimization of variable-density cellular structures for additive manufacturing: theory and experimental validation,” Rapid Prototyp. J., vol. 23, no. 4, pp. 660–677, Jun. 2017. doi: 10.1108/RPJ-04-2016-0069
• [12] A. Haryńska, H. Janik, M. Sienkiewicz, B. Mikolaszek, and J. Kucińska-Lipka, “PLA-Potato Thermoplastic Starch Filament as a Sustainable Alternative to the Conventional PLA Filament: Processing, Characterization, and FFF 3D Printing,” ACS Sustain. Chem. Eng., vol. 9, no. 20, pp. 6923–6938, 2021. doi: 10.1021/acssuschemeng.0c09413
• [13] B. D. de Castro, F. de C. Magalhães, T. H. Panzera, and J. C. Campos Rubio, “An Assessment of Fully Integrated Polymer Sandwich Structures Designed by Additive Manufacturing,” J. Mater. Eng. Perform., vol. 30, no. 7, pp. 5031–5038, 2021. doi: 10.1007/s11665-021-05604-8
• [14] J. Andrzejewski, K. Grad, W. Wiśniewski, and J. Szulc, “The use of agricultural waste in the modification of poly(Lactic acid)-based composites intended for 3d printing applications. the use of toughened blend systems to improve mechanical properties,” J. Compos. Sci., vol. 5, no. 10, 2021. doi: 10.3390/jcs5100253
• [15] M. Q. Tanveer, A. Haleem, and M. Suhaib, “Effect of variable infill density on mechanical behaviour of 3-D printed PLA specimen: an experimental investigation,” SN Appl. Sci., vol. 1, no. 12, p. 1701, Dec. 2019. doi: 10.1007/s42452-019-1744-1
• [16] M. A. Caminero, J. M. Chacón, I. García-Moreno, and G. P. Rodríguez, “Impact damage resistance of 3D printed continuous fibre reinforced thermoplastic composites using fused deposition modelling,” Compos. Part B Eng., vol. 148, no. April, pp. 93–103, 2018. doi: 10.1016/j.compositesb.2018.04.054
• [17] I. Fekete, F. Ronkay, and L. Lendvai, “Highly toughened blends of poly(lactic acid) (PLA) and natural rubber (NR) for FDM-based 3D printing applications: The effect of composition and infill pattern,” Polym. Test., vol. 99, 2021. doi: 10.1016/j.polymertesting.2021.107205
• [18] S. Korga, M. Barszcz, and L. Zgryza, “The effect of the 3D printout filling parameter on the impact strength of elements made with the FDM method,” IOP Conf. Ser. Mater. Sci. Eng., vol. 710, no. 1, 2019. doi: 10.1088/1757-899X/710/1/012027
• [19] H. Hadidi et al., “Low velocity impact of ABS after shot peening predefined layers during additive manufacturing,” Procedia Manuf., vol. 34, pp. 594–602, 2019. doi: 10.1016/j.promfg.2019.06.169
• [20] A. K. Sood, R. K. Ohdar, and S. S. Mahapatra, “Improving dimensional accuracy of Fused Deposition Modelling processed part using grey Taguchi method,” Mater. Des., vol. 30, no. 10, pp. 4243–4252, Dec. 2009. doi: 10.1016/j.matdes.2009.04.030
• [21] B. Ameri, F. Taheri-Behrooz, and M. R. M. Aliha, “Mixed-mode tensile/shear fracture of the additively manufactured components under dynamic and static loads,” Eng. Fract. Mech., vol. 260, no. July 2021. p. 108185, 2022, doi: 10.1016/j.engfracmech.2021.108185
• [22] D. R. Hetrick, S. H. R. Sanei, O. Ashour, and C. E. Bakis, “Charpy impact energy absorption of 3D printed continuous Kevlar reinforced composites,” J. Compos. Mater., vol. 55, no. 12, pp. 1705–1713, 2021. doi: 10.1177/0021998320985596
• [23] S. Kontárová et al., “Printability, mechanical and thermal properties of poly(3-hydroxybutyrate)-poly(lactic acid)-plasticizer blends for three-dimensional (3D) printing,” Materials (Basel)., vol. 13, no. 21, pp. 1–28, 2020. doi: 10.3390/ma13214736
• [24] R. A. García-León, M. Rodríguez-Castilla, and W. Quintero-Quintero, “Experimental Analysis of Impact Resistance of 3D Polycarbonate and Nylon + Carbon Fiber Specimens,” J. Mater. Eng. Perform., vol. 30, no. 7, pp. 4837–4847, 2021. doi: 10.1007/s11665-020-05422-4
• [25] V. Figueroa-Velarde et al., “Mechanical and physicochemical properties of 3d-printed agave fibers/poly(Lactic) acid biocomposites,” Materials (Basel)., vol. 14, no. 11, 2021. doi: 10.3390/ma14113111
• [26] F. Ning, W. Cong, Y. Hu, and H. Wang, “Additive manufacturing of carbon fiber-reinforced plastic composites using fused deposition modeling: Effects of process parameters on tensile properties,” J. Compos. Mater., vol. 51, no. 4, pp. 451–462, 2017. doi: 10.1177/0021998316646169
• [27] N. Sa’ude, S. H. Masood, M. Nikzad, and M. Ibrahim, “Dynamic Mechanical Properties of Copper-ABS Composites for FDM Feedstock,” Int. J. Eng. Res. Appl., vol. 3, no. 3, pp. 1257–1263, 2013. [Online]. Available: http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Dynamic+Mechanical+Properties+of+Copper-ABS+Composites+for+FDM+Feedstock#0
• [28] M. Samykano, S. K. Selvamani, K. Kadirgama, W. K. Ngui, G. Kanagaraj, and K. Sudhakar, “Mechanical property of FDM printed ABS: influence of printing parameters,” Int. J. Adv. Manuf. Technol., vol. 102, no. 9–12, pp. 2779–2796, 2019. doi: 10.1007/s00170-019-03313-0
• [29] S. Abid, R. Messadi, T. Hassine, H. Ben Daly, J. Soulestin, and M. F. Lacrampe, “Optimization of mechanical properties of printed acrylonitrile butadiene styrene using RSM design,” Int. J. Adv. Manuf. Technol., vol. 100, no. 5–8, pp. 1363–1372, 2019. doi: 10.1007/s00170-018-2710-6
• [30] A. Garg, A. Bhattacharya, and A. Batish, “Chemical vapor treatment of ABS parts built by FDM: Analysis of surface finish and mechanical strength,” Int. J. Adv. Manuf. Technol., vol. 89, no. 5–8, pp. 2175–2191, 2017. doi: 10.1007/s00170-016-9257-1
• [31] S. Khabia and K. K. Jain, “Comparison of mechanical properties of components 3D printed from different brand ABS filament on different FDM printers,” Mater. Today Proc., vol. 26, pp. 2907–2914, 2019. doi: 10.1016/j.matpr.2020.02.600
• [32] S. Khabia and K. K. Jain, “Influence of change in layer thickness on mechanical properties of components 3D printed on Zortrax M 200 FDM printer with Z-ABS filament material & Accucraft i250+ FDM printer with low cost ABS filament material,” Mater. Today Proc., vol. 26, pp. 1315–1322, 2019. doi: 10.1016/j.matpr.2020.02.268
• [33] C. Szegedy et al., “Going deeper with convolutions,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 07-12-June, pp. 1–9, Jun. 2015. doi: 10.1109/CVPR.2015.7298594
• [34] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2016-Decem, pp. 779–788, Jun. 2016. doi: 10.1109/CVPR.2016.91
• [35] H. Tercan and T. Meisen, “Machine learning and deep learning based predictive quality in manufacturing: a systematic review,” J. Intell. Manuf., vol. 33, no. 7, pp. 1879–1905, Oct. 2022. doi: 10.1007/s10845-022-01963-8
• [36] B. Ma, X. Ban, H. Huang, Y. Chen, W. Liu, and Y. Zhi, “Deep Learning-Based Image Segmentation for Al-La Alloy Microscopic Images,” Symmetry (Basel)., vol. 10, no. 4, p. 107, Apr. 2018. doi: 10.3390/sym10040107
• [37] P. Oborski and P. Wysocki, “Intelligent Visual Quality Control System Based on Convolutional Neural Networks for Holonic Shop Floor Control of Industry 4.0 Manufacturing Systems,” Adv. Sci. Technol. Res. J., vol. 16, no. 2, pp. 89–98, Apr. 2022. doi: 10.12913/22998624/145503
• [38] W.-J. Lin, S.-H. Lo, H.-T. Young, and C.-L. Hung, “Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis,” Appl. Sci., vol. 9, no. 7, pp. 1462, Apr. 2019. doi:10.3390/app9071462
• [39] Y. Pan, R. Kang, Z. Dong, W. Du, S. Yin, and Y. Bao, “On-line prediction of ultrasonic elliptical vibration cutting surface roughness of tungsten heavy alloy based on deep learning,” J. Intell. Manuf., vol. 33, no. 3, pp. 675–685, Mar. 2022. doi: 10.1007/s10845-020-01669-9
• [40] Z. Li, Z. Zhang, J. Shi, and D. Wu, “Prediction of surface roughness in extrusion-based additive manufacturing with machine learning,” Robot. Comput. Integr. Manuf., vol. 57, no. October 2018, pp. 488–495, Jun. 2019. doi: 10.1016/j.rcim.2019.01.004
• [41] N. Dimitriou et al., “Fault Diagnosis in Microelectronics Attachment Via Deep Learning Analysis of 3-D Laser Scans,” IEEE Trans. Ind. Electron., vol. 67, no. 7, pp. 5748–5757, Jul. 2020. doi: 10.1109/TIE.2019.2931220
• [42] J. P. Yun, W. C. Shin, G. Koo, M. S. Kim, C. Lee, and S. J. Lee, “Automated defect inspection system for metal surfaces based on deep learning and data augmentation,” J. Manuf. Syst., vol. 55, no. May 2019, pp. 317–324, Apr. 2020. doi: 10.1016/j.jmsy.2020.03.009
• [43] J. Zhang, P. Wang, and R. X. Gao, “Deep learning-based tensile strength prediction in fused deposition modeling,” Comput. Ind., vol. 107, pp. 11–21, May 2019. doi: 10.1016/j.compind.2019.01.011
• [44] A. Essien and C. Giannetti, “A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders,” IEEE Trans. Ind. Informatics, vol. 16, no. 9, pp. 6069–6078, Sep. 2020. doi: 10.1109/TII.2020.2967556
• [45] G. Serin, B. Sener, A. M. Ozbayoglu, and H. O. Unver, “Review of tool condition monitoring in machining and opportunities for deep learning,” Int. J. Adv. Manuf. Technol., vol. 109, no. 3–4, pp. 953–974, Jul. 2020. doi: 10.1007/s00170-020-05449-w
• [46] Q. Wang, W. Jiao, P. Wang, and Y. Zhang, “A tutorial on deep learning-based data analytics in manufacturing through a welding case study,” J. Manuf. Process., vol. 63, pp. 2–13, Mar. 2021, doi: 10.1016/j.jmapro.2020.04.044
• [47] L. Cardoso Silva et al., “Benchmarking Machine Learning Solutions in Production,” in 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 626–633, Dec. 2020. doi: 10.1109/ICMLA51294.2020.00104
• [48] S. Klein, S. Schorr, and D. Bähre, “Quality Prediction of Honed Bores with Machine Learning Based on Machining and Quality Data to Improve the Honing Process Control,” Procedia CIRP, vol. 93, pp. 1322–1327, 2020. doi: 10.1016/j.procir.2020.03.055
• [49] G. Huang, T. Liu, Y. Yang, Z. Lin, S. Song, and C. Wu, “Discriminative clustering via extreme learning machine,” Neural Networks, vol. 70, pp. 1–8, Oct. 2015. doi: 10.1016/j.neunet.2015.06.002
• [50] G. Huang, G. Bin Huang, S. Song, and K. You, “Trends in extreme learning machines: A review,” Neural Networks, vol. 61, pp. 32–48, 2015. doi: 10.1016/j.neunet.2014.10.001
• [51] T. Liu, C. K. Liyanaarachchi Lekamalage, G. Bin Huang, and Z. Lin, “Extreme Learning Machine for Joint Embedding and Clustering,” Neurocomputing, vol. 277, pp. 78–88, 2018. doi: 10.1016/j.neucom.2017.01.115
• [52] Y. Wang, Z. Xie, K. Xu, Y. Dou, and Y. Lei, “An efficient and effective convolutional auto-encoder extreme learning machine network for 3d feature learning,” Neurocomputing, vol. 174, pp. 988–998, 2016. doi: 10.1016/j.neucom.2015.10.035
• [53] Z. Chen, D. Li, and W. Zhou, “Process parameters appraisal of fabricating ceramic parts based on stereolithography using the Taguchi method,” Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., vol. 226, no. 7, pp. 1249–1258, Jul. 2012. doi: 10.1177/0954405412442607
• [54] C. Palanisamy, R. Raman, and P. kumar Dhanraj, “Additive manufacturing: a review on mechanical properties of polyjet and FDM printed parts,” Polym. Bull., vol. 79, no. 9, pp. 7065–7116, Sep. 2022. doi: 10.1007/s00289-021-03899-0
• [55] M. Ramesh and K. Panneerselvam, “Mechanical investigation and optimization of parameter selection for Nylon material processed by FDM,” Mater. Today Proc., vol. 46, pp. 9303–9307, 2019. doi: 10.1016/j.matpr.2020.02.697.
• [56] K. Raney, E. Lani, and D. K. Kalla, “Experimental characterization of the tensile strength of ABS parts manufactured by fused deposition modeling process,” Mater. Today Proc., vol. 4, no. 8, pp. 7956–7961, 2017. doi: 10.1016/j.matpr.2017.07.132.