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Zulkifli Zulkifli
Institut Teknologi Sepuluh Nopember, Surabaya
Computer Science & IT

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Prediction of Ceramic’s Mechanical Properties Based on Sintering Temperature using Neural Network Zulkifli Zulkifli; Detak Yan Pratama; Dyah Sawitri; Purwadi Agus Darwito
IPTEK Journal of Proceedings Series No 1 (2015): 1st International Seminar on Science and Technology (ISST) 2015
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (204.994 KB) | DOI: 10.12962/j23546026.y2015i1.1156

Abstract

Ceramics is one of material which apply in many area.  Thus, study of its properties is very important to fulfilled the properties requirement. The mechanical properties of ceramic such as flexural strength and hardness mainly depend on the sintering temperature and additive material. The experiments must be done to determine the best mechanical properties based on proportional sintering temperature and additive materials. Simulation for predicting mechanical properties of ceramics had been developed by using Artificial Neural Network. According to neural network simulation, the graphic of simulation result had same pattern to experimental data as the target. For predicting hardness, the Normalized Root Mean Square Error of network is 0 at training and 0.077 at validation part. This value is in line to its Coefficient Correlation which have value closed to 1. Meanwhile, the network can be used to predict flexural strength of ceramics excellently.
Co-Authors Detak Yan Pratama Dyah Sawitri Purwadi Agus Darwito