آمار

تعداد نشریات11
تعداد شماره‌ها237
تعداد مقالات2,398
تعداد مشاهده مقاله3,832,896
تعداد دریافت فایل اصل مقاله2,777,465
Parikh, Hiral H., Gohil, Piyush P.. (1400). Experimental evaluation and estimation of frictional behavior of polymer matrix composites. فناوری آموزش, 10(2), 473-483. doi: 10.22061/jcarme.2019.4329.1527
Hiral H. Parikh; Piyush P. Gohil. "Experimental evaluation and estimation of frictional behavior of polymer matrix composites". فناوری آموزش, 10, 2, 1400, 473-483. doi: 10.22061/jcarme.2019.4329.1527
Parikh, Hiral H., Gohil, Piyush P.. (1400). 'Experimental evaluation and estimation of frictional behavior of polymer matrix composites', فناوری آموزش, 10(2), pp. 473-483. doi: 10.22061/jcarme.2019.4329.1527
Parikh, Hiral H., Gohil, Piyush P.. Experimental evaluation and estimation of frictional behavior of polymer matrix composites. فناوری آموزش, 1400; 10(2): 473-483. doi: 10.22061/jcarme.2019.4329.1527

Experimental evaluation and estimation of frictional behavior of polymer matrix composites

Journal of Computational & Applied Research in Mechanical Engineering (JCARME)
مقاله 15، دوره 10، شماره 2 - شماره پیاپی 20، فروردین 2021، صفحه 473-483    اصل مقاله (1.36 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22061/jcarme.2019.4329.1527
نویسندگان
Hiral H. Parikh* 1؛ Piyush P. Gohil2
1School of Engineering and Technology Navrachana University, Vadodara –391410, Gujarat, India
2Department of Mechanical Engineering, Faculty of Technology & Engineering, The M S University of Baroda, Kalabhavan, Vadodara - 390 001, Gujarat, India
تاریخ دریافت: 22 آبان 1397،  تاریخ بازنگری: 22 اردیبهشت 1398،  تاریخ پذیرش: 12 خرداد 1398 
چکیده
As the fiber-reinforced polymer matrix composites give good strength and can work in rigorous environmental conditions, nowadays, more focus is given to study the behavior of these materials under different operating conditions. Due to the environmental concern, the focus on the natural fiber reinforced polymer matrix composite is enhancing both in research and industrial sectors. Currently, the focus has been given to unifying solid fillers with the polymer matrix composite to improve their mechanical and tribo properties. Aligned to this, the present work discusses the effect of various weight fractions of fillers (Flyash, SiC, and graphite) on the frictional behavior of natural fiber (cotton) polyester matrix composites. The specimen prepared with a hand lay-up process followed by compression molding. A plan of experiments, response surface technique, was used to obtain a response in an organized way by varying load, speed, and sliding distance. The test results reveal that different weight concentration of fillers has a considerable result on the output. The frictional behavior of materials evaluated by general regression and artificial neural network. The validation experiment effects show the estimated friction by using the artificial neural network was closer to experimental values compare to the regression models.
کلیدواژه‌ها
Coefficient of friction؛ Composites؛ Response surface method؛ Artificial neural network؛ Pin on disc
مراجع
[1] Parikh, H. & Gohil P.: Composites as TRIBO Materials in Engineering Systems: Significance and Applications. Processing Techniques and Tribological Behavior of Composite Materials, pp. 168-191, A volume in the Advances in Chemical and Materials Engineering (ACME) Book Series (2015).

[2] Chauhan, S.R; Bharti, G. & Kali das: Effect of Fiber Loading on Mechanical Properties, Friction and Wear Behavior of Vinyl ester Composites under Dry and Water Lubricated Conditions. International Journal of material Science. 1(1), 1-8 (2011).

[3] Hashmi, S.A.R.; Dwivedi, U.K. & Chand, N.: Graphite modified cotton fiber reinforced polyester composites Under sliding wear conditions. Journal of Wear. 262, 1426–1432 (2007).

[4] Yousif, B. F & Tayeb, N.S.M: Wet adhesive wear characteristics of untreated oil palm fiber reinforced polyester and treated oil palm fiber reinforced polyester composites using the pin on disc and block on ring techniques. Journal of Engineering Tribology. 224, 123-131 (2009).

[5] Yousif, B. F; Leong, O.; Low, K. & Wong, K.: The effect of treatment on tribo performance of CFRP composites. Recent Patents on Materials Science. 2, 67-74 (2009).

[6] Nirmal, U.: Prediction of friction coefficient of treated betelnut fiber reinforced polyester (T-BFRP) composite using artificial neural networks. Tribology International. 43, 1417–1429(2010).

[7] Narish, S.; Yousif, B.F. & Dirk, R.: Tribological Characteristics of Sustainable Fiber-Reinforced Thermoplastic Composites under Wet Adhesive Wear. Tribology Transactions. 54, 736-748(2011).

[8] Bajpai, P.; Singh, I. & Madaan, J.: Tribological behavior of natural fiber reinforced PLA composites. Journal of Wear. 297, 829–840 (2013).

[9] Shalwan, A. & Yousif, B.F.: Influence of date palm fiber and graphite filler on mechanical and wear characteristics of epoxy composites. Materials and Design. 59, 264–273 (2014).

[10] Ibrahem, R. A.: Friction and Wear Behaviour of Fibre / Particles Reinforced Polyester Composites. International Journal of Advanced Materials Research. 2(2), 22-26 (2016).

[11] Tharazi, I.; Sulong, A.B; Muhamad, N.; Haron, H.C.; Tholibon, D.; Ismail, N.F; Radzi,M.K.F.M; Razak, Z.: Optimization of Hot Press Parameters on Tensile Strength for Unidirectional Long Kenaf Fiber Reinforced Polylactic-Acid Composite. Procedia Engineering. 184, 478-485 (2017).

[12] Mahesh, T. S.; Nandeeshaiah, Krishna, M.: Composites, Statistical Optimization of Process Parameters on Mechanical Properties of Abs/Glass. Materials Today: Proceedings. 4, 9542–9546 (2017).

[13] Vamsi Krishna, M.; Bala Narasimha, G. & Rajesh, N., Anthony, M.: Optimization of Influential Parameters on Mechanical behaviour of AlMg1 SiCu Hybrid Metal Matrix Composites using Taguchi integrated Fuzzy Approach. Materials Today: Proceedings. 2, 1464 – 1468 (2015).

[14] Koronis, G.; Silva, A. & Foong, S.: Predicting the Flexural Performance of Woven Flax Reinforced Epoxy Composites Using Design of Experiments. Materials Today: Communications. 1-30(2017).

[15] Balak, Z.; Mohammad, Z.; Mohammadreza, R. & Esmael, S.: Taguchi design and hardness optimization of ZrB2-based composites reinforced with chopped carbon fiber and different additives and prepared by SPS. Journal of Alloys and Compounds. 639, 617–625 (2015).

[16] Aymerich, F & Serra, M.: Prediction of fatigue strength of composite laminates by means of neural networks. Key Engineering Materials. 144, 231–240(1998).

[17] Haque, M. & Sudhakar, K V.: Prediction of corrosion-fatigue behavior of DP steel through artificial neural network. International Journal of Fatigue. 23, 1–4 (2001).

[18] Veeresh Kumar, G.B. & Pramod, R.: Artificial Neural Networks for Predicting the Tribological Behaviour of Al7075-SiC Metal Matrix Composites. International Journal of Material Science & Engineering– IJMSE. 1(3), 6-11 (2014).

[19] Partheepan, G.; Sehgal, D.K. & Pandey, R.K.: Fracture toughness evaluation using miniature specimen test and neural network. Computational Materials Science. 44, 523–530 (2008).

[20] Abeesh, C. Basheer; Uday, A. & Suhas, S.: V.V. Bhanuprasad and V.M. Gadre. Modeling of surface roughness in precision machining of metal matrix composites using ANN. Journal of Materials Processing Technology. 197 (1-3), 439-444 (2008).

[21] Ganesan, G.; Raghukandan, K.; Karthikeyan, R. & Pai, B.C.: Development of processing map for 6061 Al/15% SiCp through neural networks. Journal of Materials Processing Technology. 166 (3), 423-429 (2005).

[22] Kamath, M. G.; Bhat, G. S.; Parikh, D. V. & Mueller, D.: Cotton Fiber Nonwovens For Automotive Composites. International Nonwovens Journals. 14(1), 34-40 (2005).

[23] Kamath, M. G. Processing and Evaluation of Cotton-based Composites for Automotive and Other Applications. Master of Science Thesis. The University of Tennessee, Knoxville (2004).

آمار
تعداد مشاهده مقاله: 924
تعداد دریافت فایل اصل مقاله: 763


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب