BALLI, Serkan ;SEN, Faruk . Failure Prediction of Cross-Ply Laminated Double-Serial Mechanically Fastened Composites using Fuzzy Expert System. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 61, n.2, p. 123-130, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/failure-prediction-of-cross-ply-laminated-double-serial-mechanically-fastened-composites-using-fuzzy-expert-system/>. Date accessed: 04 oct. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2014.1909.
Balli, S., & Sen, F. (2015). Failure Prediction of Cross-Ply Laminated Double-Serial Mechanically Fastened Composites using Fuzzy Expert System. Strojniški vestnik - Journal of Mechanical Engineering, 61(2), 123-130. doi:http://dx.doi.org/10.5545/sv-jme.2014.1909
@article{sv-jmesv-jme.2014.1909, author = {Serkan Balli and Faruk Sen}, title = {Failure Prediction of Cross-Ply Laminated Double-Serial Mechanically Fastened Composites using Fuzzy Expert System}, journal = {Strojniški vestnik - Journal of Mechanical Engineering}, volume = {61}, number = {2}, year = {2015}, keywords = {Fuzzy Expert System, Bolted Joint, Pinned Joint, Fastened Joint, Laminated Composites, Fuzzy Logic}, abstract = {The scope of this study is to create a model that predicts failure loads for mechanically fastened composite plates using a fuzzy expert system. The composite material used in the study was manufactured in both a fibre reinforced manner and with glass fibres. The results of a previous experimental study for cross-ply laminated composite plates that were mechanically fastened with two serial pins or bolts were used to model and predict of failure loads. Furthermore, experimental data of a preceding study were obtained with different geometrical parameters for various applied preload moments (pinned/bolted) as 2, 3, 4 and 5 Nm. In this study, a fuzzy expert system and regression analysis methods were applied by using these geometrical parameters and pinned/bolted joint configurations. Therefore, 5 geometrical parameters and 300 test data were used. According to obtained results, it was determined that the fuzzy expert system was more appropriate than the regression analysis method for modelling and prediction. Performances of the fuzzy expert system and regression analysis method were discussed in terms of error ratios and mean absolute deviations.}, issn = {0039-2480}, pages = {123-130}, doi = {10.5545/sv-jme.2014.1909}, url = {https://www.sv-jme.eu/sl/article/failure-prediction-of-cross-ply-laminated-double-serial-mechanically-fastened-composites-using-fuzzy-expert-system/} }
Balli, S.,Sen, F. 2015 June 61. Failure Prediction of Cross-Ply Laminated Double-Serial Mechanically Fastened Composites using Fuzzy Expert System. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 61:2
%A Balli, Serkan %A Sen, Faruk %D 2015 %T Failure Prediction of Cross-Ply Laminated Double-Serial Mechanically Fastened Composites using Fuzzy Expert System %B 2015 %9 Fuzzy Expert System, Bolted Joint, Pinned Joint, Fastened Joint, Laminated Composites, Fuzzy Logic %! Failure Prediction of Cross-Ply Laminated Double-Serial Mechanically Fastened Composites using Fuzzy Expert System %K Fuzzy Expert System, Bolted Joint, Pinned Joint, Fastened Joint, Laminated Composites, Fuzzy Logic %X The scope of this study is to create a model that predicts failure loads for mechanically fastened composite plates using a fuzzy expert system. The composite material used in the study was manufactured in both a fibre reinforced manner and with glass fibres. The results of a previous experimental study for cross-ply laminated composite plates that were mechanically fastened with two serial pins or bolts were used to model and predict of failure loads. Furthermore, experimental data of a preceding study were obtained with different geometrical parameters for various applied preload moments (pinned/bolted) as 2, 3, 4 and 5 Nm. In this study, a fuzzy expert system and regression analysis methods were applied by using these geometrical parameters and pinned/bolted joint configurations. Therefore, 5 geometrical parameters and 300 test data were used. According to obtained results, it was determined that the fuzzy expert system was more appropriate than the regression analysis method for modelling and prediction. Performances of the fuzzy expert system and regression analysis method were discussed in terms of error ratios and mean absolute deviations. %U https://www.sv-jme.eu/sl/article/failure-prediction-of-cross-ply-laminated-double-serial-mechanically-fastened-composites-using-fuzzy-expert-system/ %0 Journal Article %R 10.5545/sv-jme.2014.1909 %& 123 %P 8 %J Strojniški vestnik - Journal of Mechanical Engineering %V 61 %N 2 %@ 0039-2480 %8 2018-06-27 %7 2018-06-27
Balli, Serkan, & Faruk Sen. "Failure Prediction of Cross-Ply Laminated Double-Serial Mechanically Fastened Composites using Fuzzy Expert System." Strojniški vestnik - Journal of Mechanical Engineering [Online], 61.2 (2015): 123-130. Web. 04 Oct. 2024
TY - JOUR AU - Balli, Serkan AU - Sen, Faruk PY - 2015 TI - Failure Prediction of Cross-Ply Laminated Double-Serial Mechanically Fastened Composites using Fuzzy Expert System JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2014.1909 KW - Fuzzy Expert System, Bolted Joint, Pinned Joint, Fastened Joint, Laminated Composites, Fuzzy Logic N2 - The scope of this study is to create a model that predicts failure loads for mechanically fastened composite plates using a fuzzy expert system. The composite material used in the study was manufactured in both a fibre reinforced manner and with glass fibres. The results of a previous experimental study for cross-ply laminated composite plates that were mechanically fastened with two serial pins or bolts were used to model and predict of failure loads. Furthermore, experimental data of a preceding study were obtained with different geometrical parameters for various applied preload moments (pinned/bolted) as 2, 3, 4 and 5 Nm. In this study, a fuzzy expert system and regression analysis methods were applied by using these geometrical parameters and pinned/bolted joint configurations. Therefore, 5 geometrical parameters and 300 test data were used. According to obtained results, it was determined that the fuzzy expert system was more appropriate than the regression analysis method for modelling and prediction. Performances of the fuzzy expert system and regression analysis method were discussed in terms of error ratios and mean absolute deviations. UR - https://www.sv-jme.eu/sl/article/failure-prediction-of-cross-ply-laminated-double-serial-mechanically-fastened-composites-using-fuzzy-expert-system/
@article{{sv-jme}{sv-jme.2014.1909}, author = {Balli, S., Sen, F.}, title = {Failure Prediction of Cross-Ply Laminated Double-Serial Mechanically Fastened Composites using Fuzzy Expert System}, journal = {Strojniški vestnik - Journal of Mechanical Engineering}, volume = {61}, number = {2}, year = {2015}, doi = {10.5545/sv-jme.2014.1909}, url = {https://www.sv-jme.eu/sl/article/failure-prediction-of-cross-ply-laminated-double-serial-mechanically-fastened-composites-using-fuzzy-expert-system/} }
TY - JOUR AU - Balli, Serkan AU - Sen, Faruk PY - 2018/06/27 TI - Failure Prediction of Cross-Ply Laminated Double-Serial Mechanically Fastened Composites using Fuzzy Expert System JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 61, No 2 (2015): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2014.1909 KW - Fuzzy Expert System, Bolted Joint, Pinned Joint, Fastened Joint, Laminated Composites, Fuzzy Logic N2 - The scope of this study is to create a model that predicts failure loads for mechanically fastened composite plates using a fuzzy expert system. The composite material used in the study was manufactured in both a fibre reinforced manner and with glass fibres. The results of a previous experimental study for cross-ply laminated composite plates that were mechanically fastened with two serial pins or bolts were used to model and predict of failure loads. Furthermore, experimental data of a preceding study were obtained with different geometrical parameters for various applied preload moments (pinned/bolted) as 2, 3, 4 and 5 Nm. In this study, a fuzzy expert system and regression analysis methods were applied by using these geometrical parameters and pinned/bolted joint configurations. Therefore, 5 geometrical parameters and 300 test data were used. According to obtained results, it was determined that the fuzzy expert system was more appropriate than the regression analysis method for modelling and prediction. Performances of the fuzzy expert system and regression analysis method were discussed in terms of error ratios and mean absolute deviations. UR - https://www.sv-jme.eu/sl/article/failure-prediction-of-cross-ply-laminated-double-serial-mechanically-fastened-composites-using-fuzzy-expert-system/
Balli, Serkan, AND Sen, Faruk. "Failure Prediction of Cross-Ply Laminated Double-Serial Mechanically Fastened Composites using Fuzzy Expert System" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 61 Number 2 (27 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 61(2015)2, 123-130
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
The scope of this study is to create a model that predicts failure loads for mechanically fastened composite plates using a fuzzy expert system. The composite material used in the study was manufactured in both a fibre reinforced manner and with glass fibres. The results of a previous experimental study for cross-ply laminated composite plates that were mechanically fastened with two serial pins or bolts were used to model and predict of failure loads. Furthermore, experimental data of a preceding study were obtained with different geometrical parameters for various applied preload moments (pinned/bolted) as 2, 3, 4 and 5 Nm. In this study, a fuzzy expert system and regression analysis methods were applied by using these geometrical parameters and pinned/bolted joint configurations. Therefore, 5 geometrical parameters and 300 test data were used. According to obtained results, it was determined that the fuzzy expert system was more appropriate than the regression analysis method for modelling and prediction. Performances of the fuzzy expert system and regression analysis method were discussed in terms of error ratios and mean absolute deviations.