A review on industrial optimization approach in polymer matrix composites manufacturing

References

Advani, S.G. and Hsiao, K.T. (2012). Manufacturing techniques for polymer matrix composites (PMCs). Elsevier, Amsterdam, Netherlands.10.1533/9780857096258Search in Google Scholar

Agatonovic-Kustrin, S. and Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J. Pharm. Biomed. Anal. 22: 717–727, https://doi.org/10.1016/s0731-7085(99)00272-1.Search in Google Scholar PubMed

Aguirre, A.M., Méndez, C.A., and De Prada, C. (2012). An optimization-based framework for the scheduling of Automated Manufacturing Systems. IFAC Proc. 45: 780–785, https://doi.org/10.3182/20120710-4-SG-2026.00124.Search in Google Scholar

Akay, M. (2015). An introduction to polymer-matrix composites, 1st ed. Ventilation Publication APs, Irlandia.Search in Google Scholar

Akshay, J.T., Barocio, E., Bilionis, I., and Byron Pipes, R. (2022). Bayesian inference of fiber orientation and polymer properties in short fiber-reinforced polymer composites. Compos. Sci. Technol. 228: 109630, 0266-3538. https://doi.org/10.1016/j.compscitech.2022.109630.Search in Google Scholar

Albani, R.A.S., Albani, V.V.L., and Silva Neto, A.J. (2020). Source characterization of airborne pollutant emissions by hybrid metaheuristic/gradient-based optimization techniques. Environ. Pollut. 267: 115618, https://doi.org/10.1016/J.ENVPOL.2020.115618.Search in Google Scholar

Aliha, M.R.M., Imani, D.M., Salehi, S.M., Shojaee, M., and Abedi, M. (2022). Mixture optimization of epoxy base concrete for achieving highest fracture toughness and fracture energy values using Taguchi method. Compos. Commun. 32: 101150, https://doi.org/10.1016/J.COCO.2022.101150.Search in Google Scholar

Anderson, J.R. (1990). The adaptive character of thought. Psychology Press, London, UK.Search in Google Scholar

Anggoro, P.W., Purharyono, Y., Anthony, A.A., Tauviqirrahman, M., Bayuseno, A.P., and Jamari, J. (2022). Optimisation of cutting parameters of new material orthotic insole using a Taguchi and response surface methodology approach. Alexandria Eng. J. 61: 3613–3632, https://doi.org/10.1016/J.AEJ.2021.08.083.Search in Google Scholar

Antil, P., Singh, S., Manna, A., Katal, N., and Pruncu, C. (2021). An improvement in drilling of SiCp/glass fiber-reinforced polymer matrix composites using response surface methodology and multi-objective particle swarm optimization. Polym. Compos. 42: 5051–5064, https://doi.org/10.1002/PC.26204.Search in Google Scholar

Arif, S., Alam, M.T., Ansari, A.H., Shaikh, M.B.N., and Arif Siddiqui, M. (2018). Analysis of tribological behaviour of zirconia reinforced Al-SiC hybrid composites using statistical and artificial neural network technique. Mater. Res. Express 5: 056506, https://doi.org/10.1088/2053-1591/aabec8.Search in Google Scholar

Armghan, A., Logeshwaran, J., Raja, S., Aliqab, K., Alsharari, M., and Patel, S.K. (2024). Performance optimization of energy-efficient solar absorbers for thermal energy harvesting in modern industrial environments using a solar deep learning model. Heliyon 10: e26371, https://doi.org/10.1016/j.heliyon.2024.e26371.Search in Google Scholar PubMed PubMed Central

Baker, C. and Prieto, D. (2017). Application of carbon nanotubes to Kevlar fabric for Use in body armor. The University of Montana. https://digitalcommons.mtech.edu/cgi/viewcontent.cgi?article=1000&context=urp_aug_2017.Search in Google Scholar

Baraz, D. and Mosheiov, G. (2008). A note on a greedy heuristic for flow-shop makespan minimization with no machine idle-time. Eur. J. Oper. Res. 184: 810–813, https://doi.org/10.1016/J.EJOR.2006.11.025.Search in Google Scholar

Bebis, G., Georgiopoulos, M., and Kasparis, T. (1997). Coupling weight elimination with genetic algorithms to reduce network size and preserve generalization. Neurocomputing 17: 167–194, https://doi.org/10.1016/s0925-2312(97)00050-7.Search in Google Scholar

Bewoor, L.A., Prakash, V.C., and Sapkal, S.U. (2018). Production scheduling optimization in foundry using hybrid Particle Swarm Optimization algorithm. Procedia Manuf. 22: 57–64, https://doi.org/10.1016/J.PROMFG.2018.03.010.Search in Google Scholar

Bezerra, M.A., Santelli, R.E., Oliveira, E.P., Villar, L.S., and Escaleira, L.A. (2008a). Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 76: 965–977, https://doi.org/10.1016/J.TALANTA.2008.05.019.Search in Google Scholar PubMed

Bezerra, M.A., Santelli, R.E., Oliveira, E.P., Villar, L.S., and Escaleira, L.A. (2008b). Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 76: 965–977, https://doi.org/10.1016/j.talanta.2008.05.019.Search in Google Scholar

Bhushi, U., Suthar, J., and Teli, S.N. (2020). Performance analysis of metaheuristics optimization techniques for drilling process on CFRP composites. Mater. Today Proc. 28: 1106–1114, https://doi.org/10.1016/J.MATPR.2020.01.091.Search in Google Scholar

Bishop, C.M. (1995). Neural networks for pattern recognition, 3rd ed. Oxford University Press, Oxford, UK.10.1093/oso/9780198538493.001.0001Search in Google Scholar

Boddu, V.M., Brenner, M.W., Patel, J.S., Kumar, A., Mantena, P.R., Tadepalli, T., and Pramanik, B. (2016). Energy dissipation and high-strain rate dynamic response of E-glass fiber composites with anchored carbon nanotubes. Composites, Part B 88: 44–54, https://doi.org/10.1016/j.compositesb.2015.10.028.Search in Google Scholar

Bonfitto, A., Feraco, S., Tonoli, A., Amati, N., and Monti, F. (2019). Estimation accuracy and computational cost analysis of artificial neural networks for state of charge estimation in lithium batteries. Batteries 5: 47, https://doi.org/10.3390/batteries5020047.Search in Google Scholar

Cadelano, E., Palla, P.L., Giordano, S., and Colombo, L. (2009). Nonlinear elasticity of monolayer graphene. Phys. Rev. Lett. 102: 235502, https://doi.org/10.1103/physrevlett.102.235502.Search in Google Scholar PubMed

Caggiano, A., Bruno, G., and Teti, R. (2015). Integrating optimisation and simulation to solve manufacturing scheduling problems. Procedia CIRP 28: 131–136, https://doi.org/10.1016/J.PROCIR.2015.04.022.Search in Google Scholar

Carley, K.M., Kamneva, N.Y., and Reminga, J. (2004). Response surface methodology. Carnegie Mellon University, School of Computer Science, Institute for Software Research International, Pittsburgh, pp. 1–26.10.21236/ADA459032Search in Google Scholar

Chang, K.-H. (2015). Multiobjective optimization and advanced topics. Des Theory Methods Using CAD/CAE: 325–406, https://doi.org/10.1016/B978-0-12-398512-5.00005-0.Search in Google Scholar

Chater, N. and Oaksford, M. (Eds.) (2008). The probabilistic mind: where next. In: The probabilistic mind: prospects for Bayesian cognitive science. Oxford University Press, Oxford, pp. 501–514.10.1093/acprof:oso/9780199216093.003.0022Search in Google Scholar

Chater, N., Tenenbaum, J.B., and Yuille, A. (2006). Probabilistic models of cognition. Trends Cogn. Sci. 10: 287–291.10.1016/j.tics.2006.05.007Search in Google Scholar PubMed

Cherouat, A. and Billoët, J.L. (2001). Mechanical and numerical modelling of composite manufacturing processes deep-drawing and laying-up of thin pre-impregnated woven fabrics. J. Mater. Process. Technol. 118: 460–471, https://doi.org/10.1016/S0924-0136(01)00987-6.Search in Google Scholar

Chohan, J.S., Boparai, K.S., Singh, R., and Hashmi, M.S.J. (2020). Manufacturing techniques and applications of polymer matrix composites: a brief review. Adv. Mater. Process. Technol. 8: 884–894, https://doi.org/10.1080/2374068X.2020.1835012.Search in Google Scholar

Craig, R.G. and Ostafin, A.E. (2015). Carbon nanotube-reinforced fabric, assembly and related methods of manufacture, WO2014197040 A3. http://google.com/patents/WO2014197040A3 (accessed 23 February 2023).Search in Google Scholar

Damodaran, V., Castellanos, A.G., Milostan, M., and Prabhakar, P. (2018). Improving the Mode-II interlaminar fracture toughness of polymeric matrix composites through additive manufacturing. Mater. Des. 157: 60–73, https://doi.org/10.1016/J.MATDES.2018.07.006.Search in Google Scholar

Danmaliki, G.I., Saleh, T.A., and Shamsuddeen, A.A. (2017). Response surface methodology optimization of adsorptive desulfurization on nickel/activated carbon. Chem. Eng. J. 313: 993–1003, https://doi.org/10.1016/J.CEJ.2016.10.141.Search in Google Scholar

Deepak, D. and Davim, J.P. (2019). Multi-response optimization of process parameters in AWJ machining of hybrid GFRP composite by grey relational method. Procedia Manuf. 35: 1211–1221, https://doi.org/10.1016/J.PROMFG.2019.07.021.Search in Google Scholar

Deng, L. and Yu, D. (2014). Deep learning: methods and applications. Found. Trends Signal Process. 7: 197–387.10.1561/2000000039Search in Google Scholar

Denni, K. (2004). An integrated optimization system for plastic injection molding using Taguchi method, BPNN, GA, and hybrid PSO-GA, PhD dissertation. Chung Hua University.Search in Google Scholar

Dinesh Kumar, D., Gupta, A.K., Chandna, P., and Pal, M. (2015). Optimization of neural network parameters using Grey–Taguchi methodology for manufacturing process applications. J. Mech. Eng. Sci. 229: 2651–2664, https://doi.org/10.1177/0954406214560598.Search in Google Scholar

Ding, H. and Gu, X. (2020). Hybrid of human learning optimization algorithm and particle swarm optimization algorithm with scheduling strategies for the flexible job-shop scheduling problem. Neurocomputing 414: 313–332, https://doi.org/10.1016/J.NEUCOM.2020.07.004.Search in Google Scholar

Domun, N., Hadavinia, H., Zhang, T., Sainsbury, T., Liaghat, G.H., and Vahid, S. (2015). Improving the fracture toughness and the strength of epoxy using nanomaterials – a review of the current status. Nanoscale 7: 10294–10329, https://doi.org/10.1039/c5nr01354b.Search in Google Scholar PubMed

Dong, G., Grace, W., Tang, Y., and Zhao, Y.F. (2018). Optimizing process parameters of fused deposition modeling by Taguchi method for the fabrication of lattice structures. Addit. Manuf. 19: 62–72, https://doi.org/10.1016/j.addma.2017.11.004.Search in Google Scholar

Du, Y., Wang, J.L., and Lei, B. (2019). Multi-objective scheduling of cloud manufacturing resources through the integration of Cat swarm optimization and Firefly algorithm. J. Home Apem. 14: 333–342, https://doi.org/10.14743/apem2019.3.331.Search in Google Scholar

Edwards, W., Lindman, H., and Savage, L.J. (1963). Bayesian statistical inference for psychological research. Psychol. Rev. 70: 193–242, https://doi.org/10.1037/h0044139.Search in Google Scholar

Egala, R., Jagadeesh, G.V., and Setti, S.G. (2021). Experimental investigation and prediction of tribological behavior of unidirectional short castor oil fiber reinforced epoxy composites. Friction 9: 250–272, https://doi.org/10.1007/s40544-019-0332-0.Search in Google Scholar

Fogle, J. (2013). Processing, structure, and properties of nanoparticle reinforced nonwoven sandwich composites. http://trace.tennessee.edu/utk_graddiss/2570.Search in Google Scholar

Ford, S. and Despeisse, M. (2016). Additive manufacturing and sustainability: an exploratory study of the advantages and challenges. J. Clean Prod. 137: 1573–1587, https://doi.org/10.1016/J.JCLEPRO.2016.04.150.Search in Google Scholar

Freitag, M. and Hildebrandt, T. (2016). Automatic design of scheduling rules for complex manufacturing systems by multi-objective simulation-based optimization. CIRP Ann. 65: 433–436, https://doi.org/10.1016/J.CIRP.2016.04.066.Search in Google Scholar

Frketic, J., Dickens, T., and Ramakrishnan, S. (2017). Automated manufacturing and processing of fiber-reinforced polymer (FRP) composites: an additive review of contemporary and modern techniques for advanced materials manufacturing. Addit. Manuf. 14: 69–86, https://doi.org/10.1016/J.ADDMA.2017.01.003.Search in Google Scholar

Garcia, E.J., Wardle, B.L., and John, H.A. (2008). Joining prepreg composite interfaces with aligned carbon nanotubes. Compos. Part Appl. Sci. Manuf. 39: 1065–1070.10.1016/j.compositesa.2008.03.011Search in Google Scholar

Gen, M., Zhang, W., Lin, L., and Yun, Y.S. (2017). Recent advances in hybrid evolutionary algorithms for multiobjective manufacturing scheduling. Comput. Ind. Eng. 112: 616–633, https://doi.org/10.1016/J.CIE.2016.12.045.Search in Google Scholar

Ghasemi, A., Ashoori, A., and Heavey, C. (2021). Evolutionary learning based simulation optimization for stochastic job shop scheduling problems. Appl. Soft Comput. 106: 107309, https://doi.org/10.1016/J.ASOC.2021.107309.Search in Google Scholar

Gibson, A.G. (1992). Modification of Darcy’s law to model mould interface effects in composites processing. Compos. Manuf. 3: 113–118, https://doi.org/10.1016/0956-7143(92)90123-C.Search in Google Scholar

Gowda, B.M.U., Ravindra, H.V., Prakash, G.V.N., Nishanth, P., and Ugrasen, G. (2015). Optimization of process parameters in drilling of epoxy Si3N4 composite material. Mater. Today Proc. 2: 2852–2861, https://doi.org/10.1016/J.MATPR.2015.07.300.Search in Google Scholar

Griffiths, T.L., Kemp, C., and Tenenbaum, J.B. (2008). Bayesian models of cognition. In: Sun, R. (Ed.). The Cambridge handbook of computational psychology. Cambridge University Press, Cambridge, pp. 59–100.10.1017/CBO9780511816772.006Search in Google Scholar

Guo, Z.X., Wong, W.K., Li, Z., and Ren, P. (2013). Modeling and Pareto optimization of multi-objective order scheduling problems in production planning. Comput. Ind. Eng. 64: 972–986, https://doi.org/10.1016/J.CIE.2013.01.006.Search in Google Scholar

Hanif, W.Y.W., Risby, M.S., and Noor, M.M. (2015). Influence of carbon nanotube inclusion on the fracture toughness and ballistic resistance of twaron/epoxy composite panels. Procedia Eng. 114: 118–123, https://doi.org/10.1016/j.proeng.2015.08.049.Search in Google Scholar

Haykin, S. (1994). Neural networks a comprehensive foundation. Prentice Hall PTR, Upper Saddle River, NJ, USA.Search in Google Scholar

Henz, B.J., Mohan, R.V., and Shires, D.R. (2007). A hybrid global–local approach for optimization of injection gate locations in liquid composite molding process simulations. Compos. Part A Appl. Sci. Manuf. 38: 1932–1946, https://doi.org/10.1016/J.COMPOSITESA.2007.03.005.Search in Google Scholar

Heuristics – Overview, How It Works, Types, Examples (2022), https://corporatefinanceinstitute.com/resources/knowledge/other/heuristics/ (accessed 12 September 2022).Search in Google Scholar

Hu, Y., Zhu, F., Zhang, L., Lui, Y., and Wang, Z. (2019). Scheduling of manufacturers based on chaos optimization algorithm in cloud manufacturing. Robot Comput. Integr. Manuf. 58: 13–20, https://doi.org/10.1016/J.RCIM.2019.01.010.Search in Google Scholar

Iliyasu, I., Bello, J.B., Oyedeji, A.N., Salami, K.A., and Oyedeji, E.O. (2022). Response surface methodology for the optimization of the effect of fibre parameters on the physical and mechanical properties of deleb palm fibre reinforced epoxy composites. Sci. African 16: e01269, https://doi.org/10.1016/J.SCIAF.2022.E01269.Search in Google Scholar

Ivanna, B., Semeniuta, O., and Martinsen, K. (2018). Optimization of process parameters for powder bed fusion additive manufacturing by combination of machine learning and finite element method: a conceptual framework. Procedia CIRP 67: 227–232. https://doi.org/10.1016/j.procir.2017.12.204.Search in Google Scholar

Jacquier, E., and Polson, N. (2011). Bayesian methods in finance, in Geweke, J., Koop, G., and Van Dijk, H. (Eds), The Oxford handbook of Bayesian econometrics; online edn, Oxford Academic. (accessed 15 Feb 2023).10.1093/oxfordhb/9780199559084.013.0010Search in Google Scholar

Jaisingh Sheoran, A. and Kumar, H. (2020). Fused Deposition modeling process parameters optimization and effect on mechanical properties and part quality: review and reflection on present research. Mater. Today Proc. 21: 1659–1672, https://doi.org/10.1016/J.MATPR.2019.11.296.Search in Google Scholar

Jensen, K. (1997). A brief introduction to coloured petri nets. Lect. Notes Comput. Sci. 1217: 203–208, https://doi.org/10.1007/BFB0035389/COVER.Search in Google Scholar

Jones, B. and Nachtsheim, C.J. (2009). Split-plot designs: what, why, and how. J. Qual. Technol. 41: 340–361, https://doi.org/10.1080/00224065.2009.11917790.Search in Google Scholar

Jong, W.-R., Huang, Y.-M., Lin, Y.-Z., Chen, S.-C., and Chen, Y.-W. (2020). Integrating Taguchi method and artificial neural network to explore machine learning of computer aided engineering. J. Chinese Insti. Eng.: 1–11, https://doi.org/10.1080/02533839.2019.1708804.Search in Google Scholar

Joshi, S.C. (2012). The pultrusion process for polymer matrix composites. Manuf. Tech. Polym. Matrix Compos.: 381–413, https://doi.org/10.1533/9780857096258.3.381.Search in Google Scholar

Kaihara, T., Kurose, S., and Fujii, N. (2012). A proposal on optimized scheduling methodology and its application to an actual-scale semiconductor manufacturing problem. CIRP Ann. 61: 467–470, https://doi.org/10.1016/J.CIRP.2012.03.077.Search in Google Scholar

Karaş, B., Smith, P.J., Fairclough, J.P.A., and Mumtaz, K. (2022). Additive manufacturing of high-density carbon fibre reinforced polymer composites. Addit. Manuf. 58: 103044, https://doi.org/10.1016/J.ADDMA.2022.103044.Search in Google Scholar

Karlik, B. and Vehbi Olgac, A.V. (2011). Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int. J. Artif. Intell. Expert Syst. 1: 111–122.Search in Google Scholar

Karthick, M., Meikandan, M., Kaliappan, S., Karthick, M., Sekar, S., Patil, P.P., Raja, S., Natrayan, L., and Paramasivam, P. (2022). Experimental investigation on mechanical properties of glass fiber hybridized natural fiber reinforced penta-layered hybrid polymer composite. https://doi.org/10.1155/2022/1864446.Search in Google Scholar

Kenny, J.M. (1994). Application of modeling to the control and optimization of composites processing. Compos. Struct. 27: 129–139, https://doi.org/10.1016/0263-8223(94)90074-4.Search in Google Scholar

Kerni, L., Singh, S., Patnaik, A., and Kumar, N. (2020). A review on natural fiber reinforced composites. Mater. Today Proc. 28: 1616–1621, https://doi.org/10.1016/J.MATPR.2020.04.851.Search in Google Scholar

Khalid, M.Y., Al Rashid, A., Arif, Z.U., Ahmed, W., Arshad, H., and Zaidi, A.A. (2021). Natural fiber reinforced composites: sustainable materials for emerging applications. Results Eng. 11: 100263, https://doi.org/10.1016/J.RINENG.2021.100263.Search in Google Scholar

Khuri, A.I. and Mukhopadhyay, S. (2010). Response surface methodology. Wiley Interdiscip Rev. Comput. Stat. 2: 128–149, https://doi.org/10.1002/WICS.73.Search in Google Scholar

Kim, B.Y., Nam, G.J., and Lee, J.W. (2002). Optimization of filling process in RTM using a genetic algorithm and experimental design method. Polym. Compos. 23: 72–86, https://doi.org/10.1002/PC.10413.Search in Google Scholar

Kruschke, J.K. (2008). Bayesian approaches to associative learning: from passive to active learning. Learn. Behav. 36: 210–226, https://doi.org/10.3758/lb.36.3.210.Search in Google Scholar PubMed

Kruschke, J.K. (2010a). What to believe: Bayesian methods for data analysis. Trends Cogn. Sci. 14: 293–300, https://doi.org/10.1016/j.tics.2010.05.001.Search in Google Scholar PubMed

Kruschke, J.K. (2010b). Bayesian data analysis. Wiley Interdisciplin. Rev. Cogn. Sci. 1: 658–676, https://doi.org/10.1002/wcs.72.Search in Google Scholar PubMed

Kumar, S.A. and Prasad, R.V.S. (2021). Basic principles of additive manufacturing: different additive manufacturing technologies. Addit. Manuf.: 17–35, https://doi.org/10.1016/B978-0-12-822056-6.00012-6.Search in Google Scholar

Kwon, Y.K. and Moon, B.R. (2007). A hybrid neurogenetic approach for stock forecasting. IEEE Trans. Neural Netw. 18: 851–864, https://doi.org/10.1109/tnn.2007.891629.Search in Google Scholar PubMed

Larochelle, H., Jerome, Y.B., and Lamblin, L.P. (2009). Exploring strategies for training deep neural networks. J. Mach. Learn. Res. 1: 1–40.Search in Google Scholar

Lee, M.D. and Wagenmakers, E.J. (2005). Bayesian statistical inference in psychology: comment on Trafimow (2003). Psychol. Rev. 112: 662–668, https://doi.org/10.1037/0033-295x.112.3.662.Search in Google Scholar PubMed

Leasure, B. and Dennis, J.B. (2011). Petri nets. In: Padua, D. (Ed.). Encyclopedia of parallel computing. Springer, Boston, MA, pp. 1525–1530, https://doi.org/10.1007/978-0-387-09766-4_134.Search in Google Scholar

Lee, G.H., Cooper, R.C., An, S.J., Lee, S., van der Zande, A., Petrone, N., Hammerberg, A.G., Lee, C., Crawford, B., Oliver, W., et al.. (2013). High-strength chemical-vapor–deposited graphene and grain boundaries. Science 340: 1073–1076, https://doi.org/10.1126/science.1235126.Search in Google Scholar PubMed

Lee, J.H., Loya, P.E., Lou, J., and Thomas, E.L. (2014). Dynamic mechanical behavior of multilayer graphene via supersonic projectile penetration. Science 346: 1092–1096, https://doi.org/10.1126/science.1258544.Search in Google Scholar PubMed

Leema, N., Radha, P., Vettivel, S.C., and Khanna Nehemiah, H. (2015). Characterization, pore size measurement and wear model of a sintered Cu–W nano composite using radial basis functional neural network. Mater. Des. 68: 195–206, https://doi.org/10.1016/j.matdes.2014.11.035.Search in Google Scholar

Li, M. and Tucker, C.L. (2002). Modeling and simulation of two-dimensional consolidation for thermoset matrix composites. Compos. Part A Appl. Sci. Manuf. 33: 877–892. https://doi.org/10.1016/S1359-835X(02)00017-9.Search in Google Scholar

Li, F., Zhang, L., Liao, T.W., and Liu, Y. (2018). Multi-objective optimisation of multi-task scheduling in cloud manufacturing. Int. J. Prod. Res. 57: 3847–3863. https://doi.org/10.1080/00207543.2018.1538579.Search in Google Scholar

Li, M., Pu, Y., Thomas, V.M., Yoo, C.G., Ozcan, S., Deng, Y., Nelson, K., and Ragauskas, A.J. (2020). Recent advancements of plant-based natural fiber–reinforced composites and their applications. Compos., Part B Eng. 200: 108254, https://doi.org/10.1016/J.COMPOSITESB.2020.108254.Search in Google Scholar

Lin, W., Yu, D.Y., Zhang, C., Liu, X., Zhang, S., Tian, Y., Liu, S., and Xie, Z. (2015a). A multi-objective teaching−learning-based optimization algorithm to scheduling in turning processes for minimizing makespan and carbon footprint. J. Clean Prod. 101: 337–347, https://doi.org/10.1016/J.JCLEPRO.2015.03.099.Search in Google Scholar

Lin, B., Guo, W., Zheng, X., Zhang, H., Rong, C., and Chen, G. (2015b). Optimization scheduling for scientific applications with different priorities across multiple clouds. Proc. Int. Conf. Cloud Comput. Technol. Sci. Cloud Com.: 769–774, https://doi.org/10.1109/CLOUDCOM.2014.15.Search in Google Scholar

Liu, G., Xiong, Y., and Zhou, L. (2021). Additive manufacturing of continuous fiber reinforced polymer composites: design opportunities and novel applications. Compos. Commun. 27: 100907, https://doi.org/10.1016/J.COCO.2021.100907.Search in Google Scholar

Lotfi, A., Li, H., Dao, D.V., and Prusty, G. (2019). Natural fiber–reinforced composites: a review on material, manufacturing, and machinability. J. Thermoplast. Compos. Mater. 34: 238–284, https://doi.org/10.1177/0892705719844546.Search in Google Scholar

Luo, J., Liang, Z., Zhang, C., and Wang, B. (2001). Optimum tooling design for resin transfer molding with virtual manufacturing and artificial intelligence. Compos., Part A Appl. Sci. Manuf. 32: 877–888, https://doi.org/10.1016/S1359-835X(00)00147-0.Search in Google Scholar

Ma, L. and Khorasani, K. (2003). A new strategy for adaptively constructing multi-layer feed-forward neural networks. Neurocomputing 51: 361–385, https://doi.org/10.1016/s0925-2312(02)00597-0.Search in Google Scholar

Maier, H.R. and Dandy, G.C. (1998a). The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study. Environ. Model. Softw. 13: 193–209, https://doi.org/10.1016/s1364-8152(98)00020-6.Search in Google Scholar

Maier, H.R. and Dandy, G.C. (1998b). Understanding the behaviour and optimising the performance of back-propagation neural networks: an empirical study. Environ. Model. Softw. 13: 179–191, https://doi.org/10.1016/s1364-8152(98)00019-x.Search in Google Scholar

Malashin, I., Masich, I., Tynchenko, V., Gantimurov, A., Nelyub, V., Borodulin, A., Martysyuk, D., and Galinovsky, A. (2024). Machine learning in 3D and 4D printing of polymer composites: a review. Polymers 16. 3125.10.3390/polym16223125Search in Google Scholar PubMed PubMed Central

Mallick, P.K. (2017). Processing of polymer matrix composites. Process. Polym. Matrix Compos. Process. Appl.: 1–341, https://doi.org/10.1201/9781315157252/PROCESSING-POLYMER-MATRIX-COMPOSITES-MALLICK.Search in Google Scholar

Mannan, K.T., Sivaprakash, V., Raja, S., Kulandasamy, M., Patil, P.P., and Kaliappan, S. (2022a). Significance of Si3N4/Lime powder addition on the mechanical properties of natural calotropis gigantea composites. Mater. Today: Proc. 69: 1355–1360, https://doi.org/10.1016/j.matpr.2022.09.002.Search in Google Scholar

Mannan, K.T., Sivaprakash, V., Raja, S., Patil, P.P., Kaliappan, S., and Socrates, S. (2022b). Effect of Roselle and biochar reinforced natural fiber composites for construction applications in cryogenic environment. Mater. Today: Proc. 69: 1361–1368, https://doi.org/10.1016/j.matpr.2022.09.003.Search in Google Scholar

Martin, Szarski and Chauhan, Sunita (2021). Composite temperature profile and tooling optimization via Deep Reinforcement Learning. Composites., Part A: Appl. Sci. Manuf. 142: 106235, 1359-835X https://doi.org/10.1016/j.compositesa.2020.106235.Search in Google Scholar

Mathur, R., Advani, S.G., and Fink, B.K. (1999). Use of genetic algorithms to optimize gate and vent locations for the resin transfer molding process. Polym. Compos. 20: 167–178, https://doi.org/10.1002/PC.10344.Search in Google Scholar

Mc Culloch, W.S. and Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biol. 5: 115–133.10.1007/BF02478259Search in Google Scholar

McEntee, S.P. and Ó Brádaigh, C.M. (1998). Large deformation finite element modelling of single-curvature composite sheet forming with tool contact. Compos., Part A Appl. Sci. Manuf. 29: 207–213, https://doi.org/10.1016/S1359-835X(97)00075-4.Search in Google Scholar

Mehrabian, A.R. and Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inform. 1: 355–366, https://doi.org/10.1016/J.ECOINF.2006.07.003.Search in Google Scholar

Mishra, L., Mishra, D., and Ranjan, Mahapatra T. (2022). Optimization of process parameters in Nd:YAG laser micro-drilling of graphite/epoxy based polymer matrix composite using Taguchi based Grey relational analysis. Mater. Today Proc. 62: 7467–7472, https://doi.org/10.1016/J.MATPR.2022.03.501.Search in Google Scholar

Mohammed Ahmed, M., Raja, S., Asadi, A.L., Hani Jamadon, N., Rajeswari, N., and Praveen Kumar, A. (2023). A decision-making carbon reinforced material selection model for composite polymers in pipeline applications. Adv. Polym. Technol. 2023, 6344193, 9 pages https://doi.org/10.1155/2023/6344193.Search in Google Scholar

Mohan, N., Senthil, P., Vinodh, S., and Jayanth, N. (2017). A review on composite materials and process parameters optimisation for the fused deposition modelling process. Virtual Phys. Prototyp. 12: 47–59. https://doi.org/10.1080/17452759.2016.1274490.Search in Google Scholar

Mušič, G. (2012). Schedule optimization based on coloured petri nets and local search. IFAC Proc. 45: 352–357, https://doi.org/10.3182/20120215-3-AT-3016.00062.Search in Google Scholar

Naghizadeh, Z., Faezipour, M., Pol, M.H., Liaghat, G.H., and Abdolkhani, A. (2018). Improvement in impact resistance performance of glass/epoxy composite through carbon nanotubes and silica nanoparticles. Proc. Inst. Mech. Eng. Part J. Mater. Des. Appl. 232: 785–799, https://doi.org/10.1177/1464420716649403.Search in Google Scholar

Nam, T.H., Goto, K., Yamaguchi, Y., Premalal, E., Shimamura, Y., Inoue, Y., Arikawa, S., Yoneyama, S., and Ogihara, S. (2016). Improving mechanical properties of high-volume fraction aligned multi-walled carbon nanotube/epoxy composites by stretching and pressing. Compos. Part B Eng. 85: 15–23, https://doi.org/10.1016/j.compositesb.2015.09.012.Search in Google Scholar

Nawaz, M., Enscore, E.E., and Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega 11: 91–95, https://doi.org/10.1016/0305-0483(83)90088-9.Search in Google Scholar

Nguyen, M., Herszberg, I., and Paton, R. (1999). The shear properties of woven carbon fabric. Compos. Struct. 47: 767–779, https://doi.org/10.1016/S0263-8223(00)00051-9.Search in Google Scholar

Oladele, I.O., Akinwekomi, A.D., Ibrahim, I.O., Adegun, M.H., and Talabi, S.I. (2021). Assessment of impact energy, wear behavior, thermal resistance and water absorption properties of hybrid bagasse fiber/CaCO3 reinforced polypropylene composites. Int. Polym. Process. 36: 205–212, https://doi.org/10.1515/ipp-2020-3984.Search in Google Scholar

Olaiya, N.G., Maraveas, C., Salem, M.A., Raja, S., Rashedi, A., Alzahrani, A.Y., El-Bahy, Z.M., and Olaiya, F.G. (2022). Viscoelastic and properties of amphiphilic chitin in plasticised polylactic acid/starch biocomposite. Polymers 14: 2268, https://doi.org/10.3390/polym14112268.Search in Google Scholar PubMed PubMed Central

Ouarhim, W., Zari, N., Bouhfid, R., and Qaiss, A.E.K. (2019). Mechanical performance of natural fibers–based thermosetting composites. Mech. Phys. Test. Biocompos., Fibre-Reinf. Compos. Hybrid Compos.: 43–60, https://doi.org/10.1016/B978-0-08-102292-4.00003-5.Search in Google Scholar

Palanikumar, K. (2006). Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling. Int. J. Adv. Manuf. Technol. 361:19–27. https://doi.org/10.1007/S00170-006-0811-0.Search in Google Scholar

Pandey, R., Bux, S., Shrivastava, A., Choubey, A., and Singh, S. (2022). A process optimization of additive layer manufacturing processes for the production of polymer composite-based components. Mater. Today Proc. 60: 1565–1569, https://doi.org/10.1016/J.MATPR.2021.12.083.Search in Google Scholar

Pattnaik, P., Sharma, A., Choudhary, M., Singh, V., Agarwal, P., and Kukshal, V. (2020). Role of machine learning in the field of Fiber reinforced polymer composites: a preliminary discussion. Mater. Today: Proc. 44: 4703–4708, https://doi.org/10.1016/j.matpr.2020.11.026.Search in Google Scholar

Penjumras, P., Abdul Rahman, R., Talib, R.A., and Abdan, K. (2015). Response surface methodology for the optimization of preparation of biocomposites based on poly (lactic acid) and durian peel cellulose. Sci. World J. 2015, https://doi.org/10.1155/2015/293609.Search in Google Scholar PubMed PubMed Central

Petrović, M., Vuković, N., Mitić, M., and Miljković, Z. (2016). Integration of process planning and scheduling using chaotic particle swarm optimization algorithm. Expert. Syst. Appl. 64: 569–588, https://doi.org/10.1016/J.ESWA.2016.08.019.Search in Google Scholar

Pouyan, A. and Gardoni, P. (2021). A generalized Bayesian approach for prediction of strength and elastic properties of rock. Eng. Geol. 289: 106187, https://doi.org/10.1016/j.enggeo.2021.106187.Search in Google Scholar

Praveenkumar, V., Raja, S., Jamadon, N.H., and Yishak, S. (2023). Role of laser power and scan speed combination on the surface quality of additive manufactured nickel-based superalloy. Proc. Inst. Mech. Eng., Part L: 14644207231212566.10.1177/14644207231212566Search in Google Scholar

Quan, Z., Wang, Y., and Ji, Z. (2022). Multi-objective optimization scheduling for manufacturing process based on virtual workflow models. Appl. Soft. Comput. 122: 108786, https://doi.org/10.1016/J.ASOC.2022.108786.Search in Google Scholar

Raja, S. and John Rajan, A. (2023). Challenges and opportunities in additive manufacturing polymer technology: a review based on optimization perspective. Adv. Polym. Technol. 2023: 18, 8639185. https://doi.org/10.1155/2023/8639185.Search in Google Scholar

Raja, S. and Rajan, A.J. (2022). A decision-making model for selection of the suitable FDM machine using fuzzy TOPSIS. Math. Probl. Eng. 2022. https://doi.org/10.1155/2022/7653292.Search in Google Scholar

Raja, S. and Rajeswari, N. (2023). Optimization of acrylonitrile butadiene styrene filament 3D printing process parameters based on mechanical test. Int. J. Mech. Ind. Eng. 4, 4, https://doi.org/10.47893/IJMIE.2023.1204.Search in Google Scholar

Raja, S., Agrawal, A.P., Patil, P.P., Thimothy, P., Capangpangan, R.Y., Singhal, P., and Wotango, M.T. (2022a). Optimization of 3D printing process parameters of polylactic acid filament based on the mechanical test. Int. J. Chem. Eng. 2022. https://doi.org/10.1155/2022/5830869.Search in Google Scholar

Raja, S., Logeshwaran, J., Venkatasubramanian, S., Jayalakshmi, M., Rajeswari, N., Olaiya, N.G., and Mammo, W.D. (2022b). OCHSA: designing energy-efficient lifetime-aware leisure degree adaptive routing protocol with optimal cluster head selection for 5G communication network disaster management. Sci. Program. 2022: 5424356, https://doi.org/10.1155/2022/5424356.Search in Google Scholar

Raja, S., Rajan, A.J., Kumar, V.P., Rajeswari, N., Girija, M., Modak, S., Kumar, R.V., and Mammo, W.D. (2022c). Selection of additive manufacturing machine using analytical Hierarchy process. Sci. Program. 2022: 1596590, https://doi.org/10.1155/2022/1596590.Search in Google Scholar

Raja, S., Praveenkumar, V., and Rajeswari, N. (2023). Challenges and opportunities in 4D printing -AnApplication perspective. Int. J. Appl. Res. Mech. Eng. 3, 3.Search in Google Scholar

Raja, S., AhmedMustafa, M., KamilGhadir, G., MusaadAl-Tmimi, H., KhalidAlani, Z., AliRusho, M., and Rajeswari, N. (2024). An analysis of polymer material selection and design optimization to improve Structural Integrity in 3D printed aerospace components. Appl. Chem. Eng. 7: 1875, https://doi.org/10.59429/ace.v7i2.1875.Search in Google Scholar

Rasyid, M.F.A., Salim, M.S., Akil, H.M., and Ishak, Z.A.M. (2016). Optimization of processing conditions via response surface methodology (RSM) of nonwoven Flax fibre reinforced Acrodur biocomposites. Procedia Chem. 19: 469–476, https://doi.org/10.1016/J.PROCHE.2016.03.040.Search in Google Scholar

Rau, A., Jaffrézic, F., Foulley, J.-L., and Doerge, R.W. (2010). An empirical Bayesian method for estimating biological networks from temporal microarray data. Stat. Appl. Genet. Mol. Biol. 9, https://doi.org/10.2202/1544-6115.1513.Search in Google Scholar PubMed

Ravi Sankar, B. and Umamaheswarrao, P. (2018). Multi objective optimization of CFRP composite drilling using Ant colony algorithm. Mater. Today Proc. 5: 4855–4860, https://doi.org/10.1016/J.MATPR.2017.12.061.Search in Google Scholar

Ravikiran, B., Pradhan, D.K., Jeet, S., Bagal, D.K., Barua, A., and Nayak, S. (2022). Parametric optimization of plastic injection moulding for FMCG polymer moulding (PMMA) using hybrid Taguchi-WASPAS-Ant Lion optimization algorithm. Mater. Today Proc. 56: 2411–2420, https://doi.org/10.1016/J.MATPR.2021.08.204.Search in Google Scholar

Robbins, J.R., Ding, J.L., and Gupta, Y.M. (2004). Load spreading and penetration resistance of layered structures – a numerical study. Int. J. Impact Eng. 30: 593–615, https://doi.org/10.1016/j.ijimpeng.2003.08.001.Search in Google Scholar

Rodrigues, L.L.R. and Anjaiah, D. (2012). (PDF) process parameters optimization in GFRP drilling through integration of Taguchi and response surface methodology, https://www.researchgate.net/publication/267424855_Process_Parameters_Optimization_in_GFRP_Drilling_through_Integration_of_Taguchi_and_Response_Surface_Methodology (accessed 14 September 2022).Search in Google Scholar

Rohit, K. and Dixit, S. (2016). A review – future aspect of natural fiber reinforced composite. Polym. Renew. Res. 7: 43–60, https://doi.org/10.1177/204124791600700202.Search in Google Scholar

Samuel, B.O., Sumaila, M., and Dan-Asabe, B. (2022). Manufacturing of a natural fiber/glass fiber hybrid reinforced polymer composite (PxGyEz) for high flexural strength: an optimization approach. Int. J. Adv. Manuf. Technol. 119: 2077–2088, https://doi.org/10.1007/S00170-021-08377-5/TABLES/10.Search in Google Scholar

Sanderson, K. (2015). Structure: artificial armour. Nature 519: S14–S15, https://doi.org/10.1038/519s14a.Search in Google Scholar

Sekhar, K.C., Surakasi, R., Roy, P., Rosy, P.J., Sreeja, T.K., Raja, S., and Chowdary, V.L. (2022). Mechanical behavior of aluminum and graphene nanopowder-based composites. Int. J. Chem. Eng. 2022, https://doi.org/10.1155/2022/2224482.Search in Google Scholar

Severino, P., Santana, M.H.A., and Souto, Eliana B. (2012). Optimizing SLN and NLC by 22 full factorial design: effect of homogenization technique. Mater. Sci. Eng., C 32: 1375–1379, https://doi.org/10.1016/j.msec.2012.04.017.Search in Google Scholar PubMed

Shanmugam, V., Rajendran, D.J.J., Babu, K., Rajendran, S., Veerasimman, A., Marimuthu, U., Singh, S., Das, O., Neisiany, R.E., Hedenqvist, M.S., et al.. (2021). The mechanical testing and performance analysis of polymer-fibre composites prepared through the additive manufacturing. Polym. Test. 93: 106925, https://doi.org/10.1016/J.POLYMERTESTING.2020.106925.Search in Google Scholar

Sharma, A., Mukhopadhyay, T., Rangappa, S.M., Siengchin, S., and Kushvaha, V. (2021). Advances in computational intelligence of polymer composite materials: machine learning assisted modeling, analysis and design. Arch. Comput. Methods Eng. 29: 3341–3385, https://doi.org/10.21203/rs.3.rs-471723/v1.Search in Google Scholar

Shojaei, A., Ghaffarian, S.R., and Karimian, S.M.H. (2004). Three-dimensional process cycle simulation of composite parts manufactured by resin transfer molding. Compos. Struct. 65: 381–390, https://doi.org/10.1016/J.COMPSTRUCT.2003.12.001.Search in Google Scholar

Shunmugesh, K. and Panneerselvam, K. (2016). Machinability study of Carbon Fiber Reinforced Polymer in the longitudinal and transverse direction and optimization of process parameters using PSO–GSA. Eng. Sci. Technol. Int. J. 19: 1552–1563, https://doi.org/10.1016/J.JESTCH.2016.04.012.Search in Google Scholar

Sibalija, T.V. (2019). Particle swarm optimisation in designing parameters of manufacturing processes: a review (2008–2018). Appl. Soft Comput. 84: 105743, https://doi.org/10.1016/J.ASOC.2019.105743.Search in Google Scholar

Šibalija, T.V. and Majstorović, V.D. (2016). Advanced multiresponse process optimisation an intelligent and integrated approach. Springer International Publishing, Cham, pp. 1–20.10.1007/978-3-319-19255-0_1Search in Google Scholar

Siddhartha and Singh, A.K. (2015). Mechanical and dry sliding wear characterization of short glass fiber reinforced polyester-based homogeneous and their functionally graded composite materials. Proc. Inst. Mech. Eng. Part L J. Mat. Des. Appl. 229: 274–298, https://doi.org/10.1177/1464420713511429.Search in Google Scholar

Skordos, A.A., Sutcliffe, M.P.F., Klintworth, J.W., and Adolfsson, P. (2006). Multi-objective optimisation of woven composite draping using genetic algorithms. https://dspace.lib.cranfield.ac.uk/items/06bbcdac-ac38-463d-9583-c1c4a61ce5aa.Search in Google Scholar

Soepangkat, B.O.P., Norcahyo, R., Effendi, M.K., and Pramujati, B. (2020). Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO). Eng. Sci. Technol. Int. J. 23: 700–713, https://doi.org/10.1016/J.JESTCH.2019.10.002.Search in Google Scholar

Sozer, E.M., Simacek, P., and Advani, S.G. (2012). Resin transfer molding (RTM) in polymer matrix composites. Manuf. Tech. Polym. Matrix Compos.: 245–309, https://doi.org/10.1533/9780857096258.3.243.Search in Google Scholar

Stojanović, B., Vencl, A., Bobić, I., Miladinović, S., and Skerlić, J. (2018). Experimental optimisation of the tribological behaviour of Al/SiC/Gr hybrid composites based on Taguchi’s method and artificial neural network. J. Brazil. Soc. Mech. Sci. Eng. 40: 1–14, https://doi.org/10.1007/s40430-018-1237-y.Search in Google Scholar

Struzziero, G., Teuwen, J.J.E., and Skordos, A.A. (2019). Numerical optimisation of thermoset composites manufacturing processes: a review. Compos. Part A Appl. Sci. Manuf. 124: 105499, https://doi.org/10.1016/J.COMPOSITESA.2019.105499.Search in Google Scholar

Subramani, R. (2025). Optimizing process parameters for enhanced mechanical performance in 3D printed impellers using graphene-reinforced polylactic acid (G-PLA) filament. J. Mech. Sci. Technol.: 1–11, https://doi.org/10.1007/s12206-025-0231-4.Search in Google Scholar

Subramani, R. and Yishak, S. (2024). Utilizing additive manufacturing for fabricating energy storage components from graphene-reinforced thermoplastic composites. Adv. Polym. Technol. 2024: 6464049, https://doi.org/10.1155/adv/6464049.Search in Google Scholar

Subramani, R., Kaliappan, S., Arul, Kumar, P.V., Sekar, S., Poures, M.V.D, Patil, P.P., and Esakki Raj, E.S. (2022a). A recent trend on additive manufacturing sustainability with supply chain management concept, Multicriteria decision making techniques. Adv. Mater. Sci. Eng. 2022: 9151839, https://doi.org/10.1155/2022/9151839.Search in Google Scholar

Subramani, R., Kaliappan, S., Sekar, S., Patil, P.P., Usha, R., Manasa, N., and Esakkiraj, E.S. (2022b). Polymer filament process parameter optimization with mechanical test and morphology analysis. Adv. Mater. Sci. Eng. 2022. https://doi.org/10.1155/2022/8259804.Search in Google Scholar

Subramani, R., Kalidass, A.K., Muneeswaran, M.D., and Lakshmipathi, B.G. (2024). Effect of fused deposition modeling process parameter in influence of mechanical property of acrylonitrile butadiene styrene polymer. Appl. Chem. Eng. 7, https://doi.org/10.24294/ace.v7i1.3576.Search in Google Scholar

Suratno, B.R., Ye, L., and Mai, Y.W. (1998). Simulation of temperature and curing profiles in pultruded composite rods. Compos. Sci. Technol. 58: 191–197, https://doi.org/10.1016/S0266-3538(97)00132-2.Search in Google Scholar

Tehrani, M., Boroujeni, A.Y., Hartman, T.B., Haugh, T., Case, S., and Al-Haik, M. (2013). Mechanical characterization and impact damage assessment of a woven carbon fiber reinforced carbon nanotube–epoxy composite. Compos. Sci. Technol. 75: 42–48, https://doi.org/10.1016/j.compscitech.2012.12.005.Search in Google Scholar

Thankachan, T., Soorya Prakash, K., and Kamarthin, M. (2018). Optimizing the tribological behavior of hybrid copper surface composites using statistical and machine learning techniques. J. Tribol. 140: 031610, https://doi.org/10.1115/1.4038688.Search in Google Scholar

Tian, X., Todoroki, A., Liu, T., Wu, L., Hou, Z., Ueda, M., Hirano, Y., Matsuzaki, R., Mizukami, K., Iizuka, K., et al.. (2022). 3D printing of continuous fiber reinforced polymer composites: development, application, and prospective. Chinese J. Mech. Eng. Addit. Manuf. Front. 1: 100016, https://doi.org/10.1016/J.CJMEAM.2022.100016.Search in Google Scholar

Van De Velde, K. and Kiekens, P. (2001). Thermoplastic pultrusion of natural fibre reinforced composites. Compos. Struct. 54: 355–360, https://doi.org/10.1016/S0263-8223(01)00110-6.Search in Google Scholar

Velmurugan, G., Shankar, V.S., Kaliappan, S., Socrates, S., Sekar, S., Patil, P.P., Raja, S., Natrayan, L., and Bobe, K. (2022). Effect of aluminium tetrahydrate nanofiller addition on the mechanical and thermal behaviour of Luffa fibre-based polyester composites under cryogenic environment. J. Nanomater. 2022, 1–10.10.1155/2022/5970534Search in Google Scholar

Venkatasubramanian, S., Raja, S., Sumanth, V., Dwivedi, J.N., Sathiaparkavi, J., Modak, S., and Kejela, M.L. (2022). Fault diagnosis using data fusion with ensemble deep learning technique in IIoT. Math. Probl. Eng. 2022, https://doi.org/10.1155/2022/1682874.Search in Google Scholar

Venkatasubramanian, S., Narain Dwivedi, J., Raja, S., Rajeswari, N., Logeshwaran, J., and Kumar, A.P. (2023). Prediction of Alzheimer’s disease using DHO-based pretrained CNN model. Math. Probl. Eng. 2023, 1110500, 11 pages. https://doi.org/10.1155/2023/1110500.Search in Google Scholar

Venkateswarlu, C. and Jujjavarapu, S.E. (2020). Stochastic and evolutionary optimization algorithms. Stoch Glob Optim Methods Appl to Chem Biochem Pharm Environ Process: 87–123, https://doi.org/10.1016/B978-0-12-817392-3.00004-1.Search in Google Scholar

Vettivel, S.C., Selvakumar, N., and Leema, N. (2013). Experimental and prediction of sintered Cu–W composite by using artificial neural networks. Mater. Des. 45: 323–335, https://doi.org/10.1016/j.matdes.2012.08.056.Search in Google Scholar

Wagenmakers, E.J. (2007). A practical solution to the pervasive problems of p values. Psychon. Bull. Rev. 14: 779–804, https://doi.org/10.3758/bf03194105.Search in Google Scholar PubMed

Wan, Y.L.S., Li, Y., Peng, J., Hu, H., Cheng, Q., and Jiang, L. (2015). Synergistic toughening of graphene oxide– molybdenum disulfide–thermoplastic polyurethane ternary artificial nacre. ACS Nano 9, https://doi.org/10.1021/nn506148w.Search in Google Scholar PubMed

Wang, X. and Duan, H. (2014). A hybrid biogeography-based optimization algorithm for job shop scheduling problem. Comput. Ind. Eng. 73: 96–114, https://doi.org/10.1016/J.CIE.2014.04.006.Search in Google Scholar

Wang, J., Fan, X., Zhang, C., and Wan, S. (2014). A graph-based Ant colony optimization approach for integrated process planning and scheduling. Chinese J. Chem. Eng. 22: 748–753, https://doi.org/10.1016/J.CJCHE.2014.05.011.Search in Google Scholar

Wang, S.H., Wu, X., Zhang, Y.D., Tang, C., and Zhang, X. (2020). Diagnosis of COVID-19 by wavelet renyi entropy and three-segment biogeography-based optimization. Int. J. Comput. Int. Syst. 13: 1332–1344, https://doi.org/10.2991/ijcis.d.200828.001.Search in Google Scholar

Weissman, Steven A. and Anderson, Neal G. (2015). Design of experiments (DoE) and process optimization. A review of recent publications. Org. Process Res. Dev. 19: 1605–1633, https://doi.org/10.1021/op500169m.Search in Google Scholar

Wetzel, E.D., Balu, R., and Beaudet, T.D. (2015). A theoretical consideration of the ballistic response of continuous graphene membranes. J. Mech. Phys. Solid 82: 23–31, https://doi.org/10.1016/j.jmps.2015.05.008.Search in Google Scholar

Wilson, E., Karr, C., and Messimer, S. (2007). Genetic algorithm optimization of a filament winding process modeled in WITNESS 18: 509–521, https://doi.org/10.1081/AMP-120022025.Search in Google Scholar

Yang, X.-S. (2021). Particle swarm optimization. Nat. Insp. Optim Algor.: 111–121, https://doi.org/10.1016/B978-0-12-821986-7.00015-9.Search in Google Scholar

Yin, F., Mao, H.J., and Hua, L. (2011). A hybrid of back propagation neural network and genetic algorithm for optimization of injection molding process parameters. Mater. Des. 32: 3457–3464, https://doi.org/10.1016/j.matdes.2011.01.058.Search in Google Scholar

Yu, Q., Alvarez, N.T., Miller, P., Malik, R., Haase, M., Schulz, M., Shanov, V., and Zhu, X. (2016). Mechanical strength improvements of carbon nanotube threads through epoxy cross-linking. Materials 9: 68, https://doi.org/10.3390/ma9020068.Search in Google Scholar PubMed PubMed Central

Zeng, Y., Ci, L., Carey, B.J., Vajtai, R., and Ajayan, P.M. (2010). Design and reinforcement: vertically aligned carbon nanotube-based sandwich composites. ACS Nano 4: 6798–6804, https://doi.org/10.1021/nn101650p.Search in Google Scholar PubMed

Zhang, Y.D., Sui, Y., Sun, J., Zhao, G., and Qian, P. (2018). Cat Swarm Optimization applied to alcohol use disorder identification. Multimed. Tools Appl. 77: 22875–22896, https://doi.org/10.1007/s11042-018-6003-8.Search in Google Scholar

Zhou, Y., Chen, H., and Zhou, G. (2014). Invasive weed optimization algorithm for optimization no-idle flow shop scheduling problem. Neurocomputing 137: 285–292, https://doi.org/10.1016/J.NEUCOM.2013.05.063.Search in Google Scholar

Zhuo, P., Li, S., Ashcroft, I.A., and Jones, A.I. (2021). Material extrusion additive manufacturing of continuous fibre reinforced polymer matrix composites: a review and outlook. Compos. Part B Eng. 224: 109143, https://doi.org/10.1016/J.COMPOSITESB.2021.109143.Search in Google Scholar