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 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 NaH2PO4⋅2H2O 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.