Abstract
Additive manufacturing (AM) or fused filament fabrication (FFF) are used to fabricate innovative virgin/composite structures using thermoplastic polymers. FFF is one of the most fast-growing manufacturing processes of final products using polymer-based composites. This research uses acrylonitrile butadiene styrene (ABS) thermoplastic polymer as a matrix material to fabricate final-use products with aluminum (Al) metal spray reinforcement. To investigate the effect of Al spray reinforcement, three main input parameters; infill pattern (Triangle, line, and cubic), infill density (60, 80, and 100%), and the number of sprayed layers (2, 3, and 4) have been selected, and fractured strength have been studied using Taguchi L-9 orthogonal array. In addition, single objective, multi-objective, and prediction with machine learning (ML) have been performed on the samples’ flexural properties to select the best-optimized setting. Results of the study were supported with x-ray diffraction (XRD), optical and scanning electron microscope (SEM) fracture analysis.
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S.C. Ligon, R. Liska, J. Stampfl, M. Gurr, and R. Mülhaupt, Polymers for 3D Printing and Customized Additive Manufacturing, Chem. Rev., 2017, 117(15), p 10212–10290.
D.W. Rosen, Computer-aided Design for Additive Manufacturing of Cellular Structures, Comput. Aided Des. Appl., 2007, 4(5), p 585–594.
P. Wu, J. Wang, and X. Wang, A Critical Review of the Use of 3-D Printing in the Construction Industry, Autom. Constr., 2016, 68, p 21–31.
A.N. Dickson, H.M. Abourayana, and D.P. Dowling, 3D Printing of Fibre-Reinforced Thermoplastic Composites Using Fused Filament Fabrication–A Review, Polymers, 2020, 12(10), p 2188.
T.D. Ngo, A. Kashani, G. Imbalzano, K.T.Q. Nguyen, and D. Hui, Additive Manufacturing (3D printing): A Review of Materials, Methods, Applications and Challenges, Compos. B Eng., 2018, 143, p 172–196.
N. Shiode, 3D Urban Models: Recent Developments in the Digital Modelling of Urban Environments in Three-dimensions, Geo J., 2000, 52(3), p 263–269.
S. Pessoa, A.S. Guimarães, S.S. Lucas, and N. Simões, 3D Printing in the Construction Industry - A Systematic Review of the Thermal Performance in Buildings, Renew. Sustain. Energy Rev., 2021, 141, p 110794.
S. Mohammad-Ebrahimi and L. Koh, Manufacturing sustainability: institutional Theory and Life Cycle Thinking, J. Clean. Prod., 2021, 298, p 126787.
A.K. Mohanty, M. Misra, and L.T. Drzal, Sustainable Bio-composites from Renewable Resources: Opportunities and Challenges in the Green Materials World, J. Polym. Environ., 2002, 10(1), p 19–26.
S. Kango, S. Kalia, A. Celli, J. Njuguna, Y. Habibi, and R. Kumar, Surface Modification of Inorganic Nanoparticles for Development Of Organic–organic Nanocomposites–A Review, Prog. Polym. Sci., 2013, 38(8), p 1232–1261.
R. Siakeng, M. Jawaid, H. Ariffin, S.M. Sapuan, M. Asim, and N. Saba, Natural Fiber Reinforced Polylactic acid Composites: A Review, Polym. Compos., 2019, 40(2), p 446–463.
P.B. Malafaya, G.A. Silva, and R.L. Reis, Natural-Origin Polymers as Carriers and Scaffolds for Biomolecules and Cell Delivery in Tissue Engineering Applications, Adv. Drug. Deliv. Rev., 2007, 59(4–5), p 207–233.
R. Dunne, D. Desai, R. Sadiku, and J. Jayaramudu, A Review of Natural Fibres, Their Sustainability and Automotive Applications, J. Reinf. Plast. Compos., 2016, 35(13), p 1041–1050.
K.G. Satyanarayana, G.G.C. Arizaga, and F. Wypych, Biodegradable Composites Based on Lignocellulosic Fibers–An Overview, Prog. Polym. Sci., 2009, 34(9), p 982–1021.
S.V. Raut, A. Bongale, S. Kumar, and A. Bongale, Influence of Metal Powder Reinforced Polymer Composite on the Mechanical Properties of Injection Moulded Parts, AIP Conf. Proc., 2020, 2297(1), p 020024.
J.C. Najmon, S. Raeisi, and A. Tovar, 2 - Review of additive manufacturing technologies and applications in the aerospace industry, Additive Manufacturing for the Aerospace Industryed. F. Froes, R. Boyer Ed., Elsevier, New York, 2019, p 7–31
R. Ashima, A. Haleem, S. Bahl, M. Javaid, S. Kumar-Mahla, and S. Singh, Automation and Manufacturing of Smart Materials in Additive Manufacturing Technologies using Internet of Things Towards the Adoption of Industry 4.0, Mater. Today Proc., 2021, 45, p 5081–5088.
V. Verma and A. Khvan, A short review on Al MMC with reinforcement addition effect on their mechanical and wear behavior, Advances in Composite Materials Developmented. IntechOpen, London, UK, 2019
M. Strano, K. Rane, F. Briatico-Vangosa, and L. Di-Landro, Extrusion of Metal Powder-polymer Mixtures: Melt Rheology and Process Stability, J. Mater. Proc. Technol., 2019, 273, p 116250.
S. Zidi, A. Mihoub, S. Mian Qaisar, M. Krichen, and Q. Abu Al-Haija, Theft Detection Dataset for Benchmarking and Machine Learning Based Classification in a Smart Grid Environment, J. King Saud Univ. Comput. Inform. Sci., 2022 https://doi.org/10.1016/j.jksuci.2022.05.007
V. Govindan and V. Balakrishnan, A Machine Learning Approach in Analysing the Effect of Hyperboles using Negative Sentiment Tweets for Sarcasm Detection, J. King Saud Univ. Comput. Inform. Sci., 2022 https://doi.org/10.1016/j.jksuci.2022.01.008
D. Fernández-Cerero, J.A. Troyano, A. Jakóbik, and A. Fernández-Montes, Machine Learning Regression to Boost Scheduling Performance in Hyper-scale cloud-Computing Data Centres, J. King Saud Univ. Comput. Inform. Sci., 2022 https://doi.org/10.1016/j.jksuci.2022.04.008
G. Shanmugasundar, M. Vanitha, R. Čep, V. Kumar, K. Kalita, and M. Ramachandran, A Comparative Study of Linear Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining, Processes, 2021, 9(11), p 2015.
A.S. Walia, V. Srivastava, P.S. Rana, N. Somani, N.K. Gupta, G. Singh, D.Y. Pimenov, T. Mikolajczyk, and N. Khanna, Prediction of Tool Shape in Electrical Discharge Machining of EN31 Steel Using Machine Learning Techniques, Metals, 2021, 11(11), p 1668.
S. Bhattacharya and S. Chakraborty, Prediction of Responses in a CNC Milling Operation Using Random Forest Regressor, Facta Univ. Ser. Mech. Eng., 2021 https://doi.org/10.22190/FUME210728071B
M. Zhu, Y. Yang, X. Feng, Z. Du, and J. Yang, Robust Modeling Method for Thermal Error of CNC Machine Tools Based on Random Forest Algorithm, J. Intell. Manuf., 2022 https://doi.org/10.1007/s10845-021-01894-w
A. Agrawal, S. Goel, W.B. Rashid, and M. Price, Prediction of Surface Roughness during Hard Turning of AISI 4340 Steel (69 HRC), Appl. Soft Comput., 2015, 30, p 279–286.
J. Jiang, Y. Xiong, Z. Zhang, and D.W. Rosen, Machine Learning Integrated Design for Additive Manufacturing, J. Intell. Manuf., 2022, 33(4), p 1073–1086.
C. Xia, Z. Pan, J. Polden, H. Li, Y. Xu, and S. Chen, Modelling and Prediction of Surface Roughness in Wire arc Additive Manufacturing using Machine Learning, J. Intell. Manuf., 2022, 33(5), p 1467–1482.
X. Li, X. Jia, Q. Yang, and J. Lee, Quality Analysis in Metal Additive Manufacturing with Deep Learning, J. Intell. Manuf., 2020, 31(8), p 2003–2017.
I. Baturynska and K. Martinsen, Prediction of Geometry Deviations in Additive Manufactured Parts: Comparison Of Linear Regression with Machine Learning Algorithms, J. Intell. Manuf., 2021, 32(1), p 179–200.
R. Akhter and S.A. Sofi, Precision Agriculture using IoT Data Analytics and Machine Learning, J. King Saud Univ. Comput. Inform. Sci., 2021 https://doi.org/10.1016/j.jksuci.2021.05.013
H. Wu, Z. Yu, and Y. Wang, Experimental Study of the Process Failure Diagnosis in Additive Manufacturing Based on Acoustic Emission, Meas. J. Int. Meas. Confed., 2019, 136, p 445–453. ((in English))
F. Aggogeri, N. Pellegrini, and F.L. Tagliani, Recent Advances on Machine Learning Applications in Machining Processes, Appl. Sci., 2021, 11(18), p 8764.
R. Kumar, R. Kumar, N. Ranjan, and J.S. Chohan, On Development of Alternating Layer Acrylonitrile Butadiene Styrene-Al Composite Structures Using Additive Manufacturing, J. Mater. Eng. Perform., 2022 https://doi.org/10.1007/s11665-022-06913-2
R. Piyush and R. Kumar, Investigations on Modulus of Elasticity of Aluminium Reinforced 3D Printed Structures, Mater. Today Proc., 2022, 48, p 1055–1058.
R. Kumar, J.S. Chohan, R. Kumar, A. Yadav-Piyush, and N. Singh, Hybrid Fused Filament Fabrication for Manufacturing of Al Microfilm Reinforced PLA Structures, J. Braz. Soc. Mech. Sci. Eng., 2020, 42(9), p 481.
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., 2020, 26, p 1315–1322.
G. Ehrmann and A. Ehrmann, Investigation of the Shape-Memory Properties of 3D Printed PLA Structures with Different Infills, Polymers, 2021, 13, p 164.
A.S. Sidhu, S. Singh, R. Kumar, D.Y. Pimenov, and K. Giasin, Prioritizing Energy-Intensive Machining Operations and Gauging the Influence of Electric Parameters: An Industrial Case Study, Energies, 2021, 14(16), p 4761.
R.S. Sidhu, R. Kumar, R. Kumar, P. Goel, S. Singh, D.Y. Pimenov, K. Giasin, and K. Adamczuk, Joining of Dissimilar Al and Mg Metal Alloys by Friction Stir Welding, Materials, 2022 https://doi.org/10.3390/ma15175901
S. Singh, R. Kumar, R. Kumar, J.S. Chohan, N. Ranjan, and R. Kumar, Aluminum Metal Composites Primed by Fused Deposition Modeling-assisted Investment Casting: Hardness, Surface, Wear, and Dimensional Properties, Proc. Inst. Mech. Eng. Part L J. Des. Appl., 2021, 236(3), p 674–691.
R. Kumar, S. Singh, V. Aggarwal, S. Singh, D.Y. Pimenov, K. Giasin, and K. Nadolny, Hand and Abrasive Flow Polished Tungsten Carbide Die: Optimization of Surface Roughness Polishing Time and Comparative Analysis in Wire Drawing, Materials, 2022, 15(4), p 1287.
R. Kumar, P.S. Bilga, and S. Singh, Multi Objective Optimization using different Methods of Assigning Weights to Energy Consumption Responses, Surface Roughness and Material Removal Rate During Rough Turning Operation, J. Clean. Prod., 2017, 164, p 45–57.
V. Chodha, R. Dubey, R. Kumar, S. Singh, and S. Kaur, Selection of Industrial arc Welding Robot with TOPSIS and Entropy MCDM Techniques, Mater. Today Proc., 2021 https://doi.org/10.1016/j.matpr.2021.04.487
R. Kumar, S. Singh, P.S. Bilga-Jatin, J. Singh, S. Singh, M.-L. Scutaru, and C.I. Pruncu, Revealing the Benefits of Entropy Weights Method for Multi-objective Optimization in Machining Operations: A Critical Review, J. Mater. Res. Technol., 2021, 10, p 1471–1492.
G. Singh, S. Singh, C. Prakash, R. Kumar, R. Kumar, and S.J.P.C. Ramakrishna, Characterization of Three-dimensional Printed Thermal-stimulus polylactic Acid-hydroxyapatite-based Shape Memory Scaffolds, Polym. Compos., 2020, 41(9), p 3871–3891.
C.Y. Hsu and J.C. Chien, Ensemble Convolutional Neural Networks with Weighted Majority for Wafer bin map Pattern Classification, J. Intell. Manuf., 2022, 33(3), p 831–844.
H. Xu, Q. Liu, J. Casillas, M. McAnally, N. Mubtasim, L.S. Gollahon, D. Wu, and C. Xu, Prediction of Cell Viability in Dynamic Optical projection Stereolithography-Based Bioprinting using Machine Learning, J. Intell. Manuf. , 2022, 33(4), p 995–1005.
D.H.C.S.S. Martins, A.A. de Lima, M.F. Pinto, D.O. Hemerly, T.M. Prego, F.L. Silva, L. Tarrataca, U.A. Monteiro, R.H.R. Gutiérrez, and D.B. Haddad, Hybrid Data Augmentation Method for Combined Failure Recognition in Rotating Machines, J. Intell. Manuf., 2022 https://doi.org/10.1007/s10845-021-01873-1
M. Zhu, Y. Yang, X. Feng, Z. Du, and J. Yang, Robust Modeling Method for Thermal Error of CNC Machine Tools Based on Random Forest Algorithm, J. Intell. Manuf., 2022 https://doi.org/10.1007/s10845-021-01894-w
J. Li, L. Cao, J. Xu, S. Wang, and Q. Zhou, In Situ Porosity Intelligent Classification of Selective Laser Melting Based on Coaxial Monitoring and Image Processing, Meas. J. Int. Meas. Confed., 2022, 187, p 110232.
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The authors are also highly thankful for technical assistance to the University Center for Research and Development, Chandigarh University, India, and the Center for Manufacturing Research, Guru Nanak Dev Engineering College, Ludhiana, India.
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Ranjan, N., Kumar, R., Kumar, R. et al. Investigation of Fused Filament Fabrication-Based Manufacturing of ABS-Al Composite Structures: Prediction by Machine Learning and Optimization. J. of Materi Eng and Perform 32, 4555–4574 (2023). https://doi.org/10.1007/s11665-022-07431-x
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DOI: https://doi.org/10.1007/s11665-022-07431-x