Mechanical strength and setting times estimation of hydroxyapatite cement by using neural network
In this study, the mechanical strength, the initial and the final setting times in hydroxyapatite (HA) bone cement are estimated by designing a back-propagation neural network (BPNN) which has 2 inputs and 3 outputs. Firstly, some experimental samples have been prepared to train the BPNN to get it t...
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Published in | Materials in engineering Vol. 31; no. 5; pp. 2585 - 2591 |
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Abstract | In this study, the mechanical strength, the initial and the final setting times in hydroxyapatite (HA) bone cement are estimated by designing a back-propagation neural network (BPNN) which has 2 inputs and 3 outputs. Firstly, some experimental samples have been prepared to train the BPNN to get it to estimate the output parameters. Then BPNN is tested using some experimental samples that have not been used in the training stage. To prepare the training and testing data sets, some experiments were performed. In these experiments, the β-tricalcium phosphate (β-TCP), the calcium carbonate and the dicalcium phosphate are used to prepare the powder part of the HA bone cement. Also the liquid part of the cement consists of the NaH
2PO
4⋅2H
2O solution with different concentrations. The effects of liquid phase concentration and the liquid/powder ratio of the cement, as input parameters, have been investigated on the setting times and the mechanical strength of the cement, as output parameters. The comparison of the predicted values and the experimental data indicates that the developed model has an acceptable performance to estimation of the setting times and the mechanical strength in HA bone cement. Also three neural networks with 2-inputs and 1-output was developed, similar to above method, and were compared with 3-outputs model. It is found that the prediction accuracy of 3-outputs model is better than those of other 1-output models. |
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AbstractList | In this study, the mechanical strength, the initial and the final setting times in hydroxyapatite (HA) bone cement are estimated by designing a back-propagation neural network (BPNN) which has 2 inputs and 3 outputs. Firstly, some experimental samples have been prepared to train the BPNN to get it to estimate the output parameters. Then BPNN is tested using some experimental samples that have not been used in the training stage. To prepare the training and testing data sets, some experiments were performed. In these experiments, the β-tricalcium phosphate (β-TCP), the calcium carbonate and the dicalcium phosphate are used to prepare the powder part of the HA bone cement. Also the liquid part of the cement consists of the NaH
2PO
4⋅2H
2O solution with different concentrations. The effects of liquid phase concentration and the liquid/powder ratio of the cement, as input parameters, have been investigated on the setting times and the mechanical strength of the cement, as output parameters. The comparison of the predicted values and the experimental data indicates that the developed model has an acceptable performance to estimation of the setting times and the mechanical strength in HA bone cement. Also three neural networks with 2-inputs and 1-output was developed, similar to above method, and were compared with 3-outputs model. It is found that the prediction accuracy of 3-outputs model is better than those of other 1-output models. The mechanical strength, the initial and the final setting times in hydroxyapatite (HA) bone cement were estimated by designing a back-propagation neural network (BPNN) which has two inputs and three outputs. Experimental samples were prepared to train the BPNN to get it to estimate the output parameters. The BPNN was then tested using experimental samples that had not been used in the training stage. Beta-tricalcium phosphate (beta-TCP), calcium carbonate and dicalcium phosphate were used to prepare the powder part of the HA bone cement. The liquid part of the cement consisted of NaH2PO4.2H2O solution with different concentrations. The effects of liquid phase concentration and the liquid/powder ratio of the cement, as input parameters, on the setting times and the mechanical strength of the cement, as output parameters, were investigated. The comparison of the predicted values and the experimental data indicated that the developed model has acceptable performance to estimate the setting times and the mechanical strength in HA bone cement. Three neural networks with two-inputs and one-output were developed, and were compared with the three-output model. The prediction accuracy of the three-output model was better than that of other one-output models. |
Author | Moztarzadeh, F. Baseri, H. Solati-Hashjin, M. Rabiee, S.M. |
Author_xml | – sequence: 1 givenname: H. surname: Baseri fullname: Baseri, H. email: h.baseri@nit.ac.ir organization: Department of Mechanical Engineering, Babol Noshirvani University of Technology, Babol, Mazandaran, Iran – sequence: 2 givenname: S.M. surname: Rabiee fullname: Rabiee, S.M. organization: Department of Mechanical Engineering, Babol Noshirvani University of Technology, Babol, Mazandaran, Iran – sequence: 3 givenname: F. surname: Moztarzadeh fullname: Moztarzadeh, F. organization: Department of Medical Engineering, Amirkabir University of Technology, Tehran, Iran – sequence: 4 givenname: M. surname: Solati-Hashjin fullname: Solati-Hashjin, M. organization: Department of Medical Engineering, Amirkabir University of Technology, Tehran, Iran |
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Cites_doi | 10.1080/10255840500156971 10.1109/NEMS.2008.4484381 10.1016/j.matdes.2004.06.012 10.1002/1097-4636(2001)58:3<329::AID-JBM1025>3.0.CO;2-9 10.1016/0142-9612(93)90190-D 10.1016/j.biomaterials.2004.04.043 10.1016/j.biomaterials.2006.05.022 10.1166/jnn.2008.AN26 10.14219/jada.archive.2006.0353 10.1179/174367607X227972 10.1557/JMR.1998.0015 10.1016/j.matdes.2005.01.005 10.1016/S0142-9612(97)00121-X |
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Predictive modelling of the mechanical properties and failure processes in hydroxyapatite–polyethylene (HapexTM) composite publication-title: J Mater Sci: Mater Med contributor: fullname: Guild – volume: 19 start-page: 1587 year: 1998 ident: 10.1016/j.matdes.2009.11.028_bib9 article-title: Mechanical strength of calcium phosphate cement in vivo and in vitro publication-title: J Biomater doi: 10.1016/S0142-9612(97)00121-X contributor: fullname: Yamamoto |
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Snippet | In this study, the mechanical strength, the initial and the final setting times in hydroxyapatite (HA) bone cement are estimated by designing a... The mechanical strength, the initial and the final setting times in hydroxyapatite (HA) bone cement were estimated by designing a back-propagation neural... |
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Title | Mechanical strength and setting times estimation of hydroxyapatite cement by using neural network |
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