Powiadomienia systemowe
- Sesja wygasła!
Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
This study deals with the application of soft computing techniques viz., response surface methodology, artificial neural network, radial basis function network and support vector regression in analyzing and predicting the ram tensile and shear strengths of aluminium 5052–stainless steel 316 explosive clads, having different interlayer. 60 explosive cladding experiments were conducted, based on central composite design of experiments, by varying the process parameters viz., loading ratio (mass of the explosive/mass of the flyer plate: 0.6–1.0), distance of separation (6–10 mm), preset angle (6°–8°) and interlayer (aluminium 1100/pure copper/stainless steel 304). The responses viz., ram tensile and shear strengths obtained from 90% of the experiments and trial experiments are used for training artificial neural network, radial basis function network and support vector regression in a Matlab environment, altering training algorithms and number of neurons in the hidden layer. The remaining 10% of the experimental outcome is used for testing the developed models. Likewise in RSM, regression equations are generated for the responses, based on analysis of variance. All the four models are capable of predicting the ram tensile and shear strength effectively, as the average percentage deviation with the experimental outcome are less than 10%. Of the three models, artificial neural network model predicts the ram tensile strength and shear strength in a better manner.
Czasopismo
Rocznik
Tom
Strony
art. no. e42, 2022
Opis fizyczny
Bibliogr. 46 poz., rys., tab., wykr.
Twórcy
autor
- Department of Mechanical Engineering, Annamalai University, Annamalai Nagar, Tamil Nadu, India
autor
- Sree Raghavendra Arts and Science College, Keezhamoongiladi, Chidambaram, Tamil Nadu, India
Bibliografia
- 1. Saravanan S, Raghukandan K. Effect of silicon carbide and wiremesh reinforcements in dissimilar grade aluminium explosive clad composites. Defence Technol. 2020;16(6):1160–6.
- 2. Carvalho HSFL, Galvão I, Mendes RM, Leal R, Loureiro A. Aluminum-to-steel cladding by explosive welding. Metals. 2020;10(8):1062.
- 3. Murugan SP, Cheepu M, Nam DG, Park YD. Weldability and fracture behaviour of low carbon steel/aluminium/stainless steel clad sheet with resistance spot welding. Trans Indian Inst Met. 2017;70(3):759–68.
- 4. Wang C, Jiang Y, Xie J, Zhou D, Zhang X. Interface formation and bonding mechanism of embedded aluminum-steel composite sheet during cold roll bonding. Mater Sci Eng A. 2017;708:50–9.
- 5. Haghshenas M, Abdel-Gwad A, Omran AM, Gökçe B, Sahraeinejad S, Gerlich AP. Friction stir weld assisted diffusion bonding of 5754 aluminum alloy to coated high strength steels. Mater Des. 2014;55:442–9.
- 6. Becker M, Balle F. Multi-spot ultrasonic welding of aluminum to steel sheets: process and fracture analysis. Metals. 2021;11(5):779.
- 7. Wang H, Qin G, Geng P, Ma X. Interfacial microstructures and mechanical properties of friction welded Al/steel dissimilar joints. J Manuf Process. 2020;49:18–25.
- 8. Sherpa BB, Kumar PD, Upadhyay A, Kumar S, Agarwal A, Tyagi S. Effect of explosive welding parameters on Al/LCS interface cladded by low velocity of detonation explosive welding (LVEW) process. Int J Adv Manuf Technol. 2021;113(11):3303–17.
- 9. Saravanan S, Inokawa H, Tomoshige R, Raghukandan K. Effect of silicon carbide particles in microstructure and mechanical properties of dissimilar aluminium explosive cladding. J Manuf Process. 2019;47:32–40.
- 10. Senthil SM, Parameshwaran R, Nathan SR, Kumar MB, Deepandurai K. A multi-objective optimization of the friction stir welding process using RSM-based-desirability function approach for joining aluminum alloy 6063–T6 pipes. Struct Multidiscip Optim. 2020;62(3):1117–33.
- 11. Saravanan S, Raghukandan K. Microstructure and mechanical strength predictive modeling in Al 5052-trapezoidal grooved SS 304 explosive cladding. J Wuhan Univ Technol Mater Sci Ed. 2020;35(5):958–66.
- 12. Cao X, Li Z, Zhou X, Luo Z. Modeling and optimization of resistance spot welded aluminum to Al-Si coated boron steel using response surface methodology and genetic algorithm. Measurement. 2021;171:108766.
- 13. Naik AB, Reddy AC. Optimization of tensile strength in TIG welding using the Taguchi method and analysis of variance (ANOVA). Thermal Sci Eng Progress. 2018;8:327–39.
- 14. Sivagurumanikandan N, Saravanan S, Kumar GS, Raju S, Raghukandan K. Prediction and optimization of process parameters to enhance the tensile strength of Nd: YAG laser welded super duplex stainless steel. Optik. 2018;157:833–40.
- 15. Bagherzadeh SA, Shamsipour M, Kholoud MJ, Dehkordi MHR. ANN modeling and multiobjective genetic algorithm optimization of pulsed laser welding of Ti6Al4V alloy sheets with various thicknesses. J Laser Appl. 2021;33(1):012056.
- 16. De Filippis L, Serio L, Facchini F, Mummolo G, Ludovico A. Prediction of Vickers microhardness and ultimate tensile strength of AA5754H1 friction stir welding butt joints using artificial neural network. Materials. 2016;9(11):915.
- 17. do Valle-Tomaz I, Colaço FHG, Sarfraz S, Pimenov DY, Gupta MK, Pintaude G. Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm. Int J Adv Manuf Technol. 2021;113(11):3569–83.
- 18. Sarkar A, Dey P, Rai RN, Saha SC. A comparative study of multiple regression analysis and back propagation neural network approaches on plain carbon steel in submerged-arc welding. Sadhana. 2016;41(5):549–59.
- 19. Liang R, Yu R, Luo Y, Zhang Y. Machine learning of weld joint penetration from weld pool surface using support vector regression. J Manuf Process. 2019;41:23–8.
- 20. Petković D. Prediction of laser welding quality by computational intelligence approaches. Optik. 2017;140:597–600.
- 21. Do Koo Y, Yoo KH, Na MG. Estimation of residual stress in welding of dissimilar metals at nuclear power plants using cascaded support vector regression. Nucl Eng Technol. 2017;49(4):817–24.
- 22. Kumar GS, Raghukandan K, Saravanan S, Sivagurumanikandan N. Optimization of parameters to attain higher tensile strength in pulsed Nd: YAG laser welded Hastelloy C-276–Monel 400 sheets. Infrared Phys Technol. 2019;100:1–10.
- 23. Arkes J. Regression analysis: a practical introduction. London: Routledge; 2019.
- 24. Gurney K. An introduction to neural networks. Hoboken: CRC Press; 2018.
- 25. Amiri N, Farrahi GH, Kashyzadeh KR, Chizari M. Applications of ultrasonic testing and machine learning methods to predict the static & fatigue behavior of spot-welded joints. J Manuf Process. 2020;52:26–34.
- 26. Lourakis MI. A brief description of the Levenberg-Marquardt algorithm implemented by levmar. Found Res Technol. 2015;4(1):1–6.
- 27. Mollah AA, Pratihar DK. Modeling of TIG welding and abrasive flow machining processes using radial basis function networks. Int J Adv Manuf Technol. 2008;37(9–10):937–52.
- 28. Mahato S, Paul S. Detection of major depressive disorder using linear and non-linear features from EEG signals. Microsyst Technol. 2019;25(3):1065–76.
- 29. Yang H, Rad HN, Hasanipanah M, Amnieh HB, Nekouie A. Prediction of vibration velocity generated in mine blasting using support vector regression improved by optimization algorithms. Nat Resour Res. 2020;29(2):807–30.
- 30. Gupta AK. Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression. Int J Prod Res. 2010;48(3):763–78.
- 31. Verma S, Misra JP, Singh J, Batra U, Kumar Y. Prediction of tensile behavior of FS welded AA7039 using machine learning. Mater Today Commun. 2021;26:101933.
- 32. Manikandan P, Hokamoto K, Fujita M, Raghukandan K, Tomoshige R. Control of energetic conditions by employing interlayer of different thickness for explosive welding of titanium/304 stainless steel. J Mater Process Technol. 2008;195(1–3):232–40.
- 33. Saravanan S, Raghukandan K, Kumar P. Effect of wire mesh interlayer in explosive cladding of dissimilar grade aluminium plates. J Central South Univ. 2019;26(3):604–11.
- 34. Carvalho GH, Galvão I, Mendes R, Leal RM, Loureiro A. Explosive welding of aluminium to stainless steel using carbon steel and niobium interlayers. J Mater Process Technol. 2020;283:116707.
- 35. Zhao D, Ivanov M, Wang Y, Liang D, Du W. Multi-objective optimization of the resistance spot welding process using a hybrid approach. J Intell Manuf. 2020;31:1–6.
- 36. Myers RH, Montgomery DC, Anderson-Cook CM. Response surface methodology: process and product optimization using designed experiments. Hoboken: Wiley; 2016.
- 37. Elango E, Saravanan S, Raghukandan K. Experimental and numerical studies on aluminum-stainless steel explosive cladding. J Central South Univ. 2020;27(6):1742–53.
- 38. Paul H, Skuza W, Chulist R, Miszczyk M, Gałka A, Prażmowski M, Pstruś J. The effect of interface morphology on the electro-mechanical properties of Ti/Cu clad composites produced by explosive welding. Metall Mater Trans A. 2020;51(2):750–66.
- 39. Robin LG, Raghukandan K, Saravanan S. Studies on wire-mesh and silicon carbide particle reinforcements in explosive cladding of Al 1100-Al 5052 sheets. J Manuf Process. 2020;56:887–97.
- 40. Sultana N, Hossain SZ, Alam MS, Islam MS, Al Abtah MA. Soft computing approaches for comparative prediction of the mechanical properties of jute fiber reinforced concrete. Adv Eng Softw. 2020;149:102887.
- 41. Adil M, Ullah R, Noor S, Gohar N. Effect of number of neurons and layers in an artificial neural network for generalized concrete mix design. Neural Comput Appl. 2020;2020:1–9.
- 42. Huang Z, Shan L, Lai J, Wu J. Sliding model synchronous control based on RBFN of multiple exciters in vibration system. J Mech Sci Technol. 2021;35(10):4301–11.
- 43. Gajalakshmi K, Palanivel S, Nalini NJ, Saravanan S, Raghukandan K. Grain size measurement in optical microstructure using support vector regression. Optik. 2017;138:320–7.
- 44. Rao KV, Murthy PB. Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM. J Intell Manuf. 2018;29(7):1533–43.
- 45. Zhao D, Wang Y, Liang D, Ivanov M. Performances of regression model and artificial neural network in monitoring welding quality based on power signal. J Market Res. 2020;9(2):1231–40.
- 46. Huang J, Sun Y, Zhang J. Reduction of computational error by optimizing SVR kernel coefficients to simulate concrete compressive strength through the use of a human learning optimization algorithm. Eng Comput. 2021;24:1–8.
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-140155bd-a709-4654-bef7-e0173acd71b4