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Chokshi, Sagar, Gohil, Piyush, Parmar, Vijay, Chaudhary, Vijaykumar. (1404). Artificial Neural Network (ANN) Approach to Predict Tensile Properties of Longitudinally Placed Fiber Reinforced Polymeric Composites including Interphase. نشریه هنرهای کاربردی, 12(Special Issue 2: Mechanics of Advanced Fiber-Reinforced Composite Structures), 353-360. doi: 10.22075/macs.2024.33874.1664
Sagar Chokshi; Piyush Gohil; Vijay Parmar; Vijaykumar Chaudhary. "Artificial Neural Network (ANN) Approach to Predict Tensile Properties of Longitudinally Placed Fiber Reinforced Polymeric Composites including Interphase". نشریه هنرهای کاربردی, 12, Special Issue 2: Mechanics of Advanced Fiber-Reinforced Composite Structures, 1404, 353-360. doi: 10.22075/macs.2024.33874.1664
Chokshi, Sagar, Gohil, Piyush, Parmar, Vijay, Chaudhary, Vijaykumar. (1404). 'Artificial Neural Network (ANN) Approach to Predict Tensile Properties of Longitudinally Placed Fiber Reinforced Polymeric Composites including Interphase', نشریه هنرهای کاربردی, 12(Special Issue 2: Mechanics of Advanced Fiber-Reinforced Composite Structures), pp. 353-360. doi: 10.22075/macs.2024.33874.1664
Chokshi, Sagar, Gohil, Piyush, Parmar, Vijay, Chaudhary, Vijaykumar. Artificial Neural Network (ANN) Approach to Predict Tensile Properties of Longitudinally Placed Fiber Reinforced Polymeric Composites including Interphase. نشریه هنرهای کاربردی, 1404; 12(Special Issue 2: Mechanics of Advanced Fiber-Reinforced Composite Structures): 353-360. doi: 10.22075/macs.2024.33874.1664

Artificial Neural Network (ANN) Approach to Predict Tensile Properties of Longitudinally Placed Fiber Reinforced Polymeric Composites including Interphase

Mechanics of Advanced Composite Structures
مقاله 10، دوره 12، Special Issue 2: Mechanics of Advanced Fiber-Reinforced Composite Structures - شماره پیاپی 25، آبان 2025، صفحه 353-360    اصل مقاله (731.35 K)
نوع مقاله: Special Issue: Mechanics of Advanced Fiber Reinforced Composite Structures
شناسه دیجیتال (DOI): 10.22075/macs.2024.33874.1664
نویسندگان
Sagar Chokshi1؛ Piyush Gohil* 2؛ Vijay Parmar2؛ Vijaykumar Chaudhary1
1Department of Mechanical Engineering, Chandubhai S. Patel Institute of Technology, Charotar University of Science and Technology, Changa-388421, Gujarat, India
2Department of Mechanical Engineering, Faculty of Technology and Engineering, The Maharaja Sayajirao University of Baroda, Vadodara-390001, Gujarat, India
تاریخ دریافت: 04 اردیبهشت 1403،  تاریخ بازنگری: 08 مرداد 1403،  تاریخ پذیرش: 05 شهریور 1403 
چکیده
Machine Learning has become prevalent nowadays for predicting data on the mechanical properties of various materials and is widely used in various polymeric applications. In the present study, Artificial Neural Network (ANN), a computational tool is used to predict the elastic modulus of a composite of longitudinally placed fiber-reinforced polymeric composite. The novelty in carried work is that the property prediction is carried out considering interphase and its properties. For this, tensile properties data of Longitudinally Placed Bamboo Fiber Reinforced Polyester Composite (LUDBPC), Longitudinally Placed Flax Fiber Reinforced Polyester Composite (LUDFPC) and Longitudinally Placed Jute Fiber Reinforced Polyester Composite (LUDJPC) has been procured to generate ANN models. The Levenberg-Marquardt training algorithm is used to generate the ANN models as it gives more accurate results compared to other ANN algorithms based on interphase properties data. The validation of ANN models was also carried out based on fresh experimental results of BPC/FPC by doing the fabrication with hand layup technique and testing of composites with a Universal Testing Machine (UTM). The present work signifies that the developed ANN models give accurate results with experimental results for the prediction of elastic modulus of composite (Ecl) and it can be used for the prediction of longitudinally placed fiber-reinforced composite and Ecl of BPC at volume fraction of fiber (vf):22% is 2248.75 MPa and Ecl of FPC at vf:10% is 3210.50 MPa.
کلیدواژه‌ها
Bamboo/Polyester Composite؛ Flax/Polyester Composite؛ Jute/Polyester Composite؛ Interphase volume fraction؛ Elastic modulus of composite
مراجع

[1]    Suyambulingam, I., Rangappa, S.M. and Siengchin, S., 2023. Advanced Materials and Technologies for Engineering Applications. Applied Science and Engineering Progress, 16(3), pp.6760-6760.
[2]    Saxena, M., Morchhale, R.K., Asokan, P. and Prasad, B.K., 2008. Plant fiber—industrial waste reinforced polymer composites as a potential wood substitute material. Journal of composite materials, 42(4), pp.367-384.
[3]    Baria, A.N. and Choksi, S., 2016. Wear and Friction Behaviour of Corn Husk Fiber Reinforced Polyester Composites. International Journal for Scientific Research & Development, 4(05), pp.803-806.
[4]    Sharma, S.C., 2000. Composite materials. Narosa Publishing House
[5]    Kaw, A.K., 2005. Mechanics of composite materials. CRC press.
[6]    Asim, M., Saba, N., Jawaid, M. and Nasir, M., 2018. Potential of natural fiber/biomass filler-reinforced polymer composites in aerospace applications. In Sustainable composites for aerospace applications pp. 253-268. 
[7]    Chairi, M., Jalal E.B., Issam H., Francisco M.C. and Guido D.B., 2023. Composite Materials: A Review of Polymer and Metal Matrix Composites, Their Mechanical Characterization, and Mechanical Properties. In Next Generation Fiber-Reinforced Composites-New Insights.
[8]    Chokshi, S., Gohil, P., Lalakiya, A., Patel, P. and Parmar, A., 2020. Tensile strength prediction of natural fiber and natural fiber yarn: Strain rate variation upshot. Materials Today: Proceedings, 27, pp.1218-1223.
[9]    Chokshi, S. and Gohil, P., 2018. Effect of strain rate on tensile strength of natural fiber reinforced polyester composites. International Journal of Mechanical Engineering and Technology, 9(10).
[10]    Parikh, H.H., Chokshi, S., Chaudhary, V., Khan, A. and Mistry, J., 2023. Flexural response of 3D printed wood dust reinforced polymer composite. Materials Today: Proceedings.
[11]    Desai, Y. and Chokshi, S., 2018. Tribological Characteristization of Chopped Carbon/polyseter Composite Material. Technology, 9(13), pp.942-950.
[12]    Chokshi, Sagar R., and Gohil, Piyush P., 2018. Experimental investigations on flexural properties of longitudinally and transversely placed fiber reinforced polymeric composites. International Journal of Applied Engineering Research, 13(9) pp. 7217-7223.
[13]    Dhandapani, C. and Sivaramakrishnan, R., 2019. Implementation of Machine Learning (ML) in Mechanical Engineering Application using Artificial Intelligence (AI). Science, Technology and Development Journal, 8(10), pp. 93-99.
[14]    Lee, W. M., 2019. Python machine learning. John Wiley & Sons.
[15]    Khayyat, H.A., 2018. ANN based Intelligent Mechanical Engineering Design: A Review. Indian Journal of Science and Technology, pp.1-7.
[16]    Begum, K. and Islam, M., 2013. Natural fiber as a substitute to synthetic fiber in polymer composites: a review. Research Journal of Engineering Sciences, 2278, p.9472.
[17]    Patel, G.A. and Chokshi, S.R., 2018. Tribological and physical characterization of Citrus-limetta peel reinforced polyester composites. International Journal of Applied Engineering Research, 13(9), pp.7210-7216.
[18]    Bajpai, P.K., Singh, I. and Madaan, J., 2014. Development and characterization of PLA-based green composites: A review. Journal of Thermoplastic Composite Materials, 27(1), pp. 52-81.
[19]    Palaniappan, S.K., Singh, M.K., Rangappa, S.M. and Siengchin, S., 2023. Eco-friendly Biocomposites: A Step Towards Achieving Sustainable Development Goals. Composites, 7(12).
[20]    Phiri, R., Rangappa, S.M., Siengchin, S. and Marinkovic, D., 2023. Agro-waste natural fiber sample preparation techniques for bio-composites development: methodological insights. Facta Universitatis, Series: Mechanical Engineering, 21(4), pp.631-656.
[21]    Chokshi, S., Gohil, P. and Patel, D., 2020. Experimental investigations of bamboo, cotton and viscose rayon fiber reinforced Unidirectional composites. Materials Today: Proceedings, 28, pp. 498-503.    
[22]    Hornsby, P.R., Hinrichsen, E. and Tarverdi, K., 1997. Preparation and properties of polypropylene composites reinforced with wheat and flax straw fibres: part I fibre characterization. Journal of materials science, 32, pp.443-449.
[23]    John, M.J. and Thomas, S., 2008. Biofibres and biocomposites. Carbohydrate polymers, 71(3), pp. 343-364.
[24]    Mieck, K.P., Nechwatal, A. and Knobeldorf, C., 1994. Potential applications of natural fibres in composite materials. Melliand Textilberichte, 11, pp. 228-30.
[25]    Mohanty, A.K., Misra, M.A. and Hinrichsen, G.I., 2000. Biofibres, biodegradable polymers and biocomposites: An overview. Macromolecular materials and Engineering, 276(1), pp. 1-24.
[26]    Oksman, K., 2000. Mechanical properties of natural fibre mat reinforced thermoplastic. Applied Composite Materials, 7, pp. 403-414.
[27]    Sanadi, A.R., Calufield, D.F. and Rowell, R.M., 1994. Reinforcing polypropylene with natural fibers. Plastics Engineering (USA), 50(4), pp.27-28.
[28]    Chokshi, S., Parmar, V., Gohil, P. and Chaudhary, V., 2022. Chemical composition and mechanical properties of natural fibers. Journal of Natural Fibers, 19(10), pp. 3942-3953.
[29]    Chokshi, S. and Gohil, P., 2022. Experimental investigation and mathematical modeling of longitudinally placed natural fiber reinforced polymeric composites including interphase volume fraction. Fibers and Polymers, 23(2), pp. 488-501.
[30]    Gohil, P. and Shaikh, A.A., 2010. Transverse elastic modulus in unidirectional fiber reinforced composites: modeling with varying interphase using numerical integration. Nanoscience and technology: An International Journal, 1(3), pp. 223-245.
[31]    Gohil, P., 2010. Experimental and Analytical Investigations of Interphase Influence on the Properties of Fiber Reinforced Composites, Ph.D. Thesis, SVNIT, Surat.
[32]    Moreno, J.J.M., Pol, A.P., Abad, A.S. and Blasco, B.C., 2013. Using the R-MAPE index as a resistant measure of forecast accuracy. Psicothema, 25(4), pp.500-506.
[33]    ASTM D3039/3039M-08, 2008. Standard Test Method for Tensile Properties of Single Textile Fibers, ASTM International, West Conshohocken, PA, 2008.
[34]    Bolcu, D., Stanescu, G. and Ursache, M., 2004. Theoretical and experimental study on determination of the elastic properties of the composite materials. Romanian Reports in Physics, 56(1), pp. 3-12.

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