This paper aims to investigate the effect of fly ash reinforcement ratio (Rr) and sintering temperature (T) on the transverse rupture strength (TRS), hardness and density of fly ash reinforced bronze-based composite materials by using multi-objective Taguchi technique, analysis of variance (ANOVA) and regression analysis.
The bronze-based composite materials containing 5, 10 and 15 Wt.% fly ashes were prepared by using spark plasma sintering carried out under a pressure of 35 MPa, at 750, 800 and 850 °C for 3 min. Sintering temperature and fly ash reinforcement ratio were considered as input parameters; the TRS, hardness and density were considered as output parameters. Experiments were designed according to Taguchi L9 orthogonal array. Multi signal-to-noise ratio (MSNR) was computed to define the optimal process parameters. ANOVA was conducted to detect the importance of the input parameters for the process performance. Moreover, the linear model was developed for predicting the performance parameters by using regression analysis.
Fly ash can be a good alternative as reinforcement to reduce the cost for composite materials. Optimal process parameters had obtained 850°C sintering temperature and 5 per cent reinforcement ratio by using multi-objective Taguchi technique. The per cent contributions of the control factors on the performance parameters had obtained sintering temperature (95.78 per cent) and fly ash reinforcement ratio (3.00 per cent) with ANOVA. The obtained results indicate that the sintering temperature was found to be the dominant factor among controllable factors. However, the reinforcement ratio showed an insignificant effect.
It has been indicated that multi-objective Taguchi technique and regression analysis are effective and powerful tools in modeling and simultaneous optimization of quality characteristics for composite materials.
Kus, H., Basar, G. and Kahraman, F. (2018), "Modeling and optimization for fly ash reinforced bronze-based composite materials using multi objective Taguchi technique and regression analysis", Industrial Lubrication and Tribology, Vol. 70 No. 7, pp. 1187-1192. https://doi.org/10.1108/ILT-02-2018-0059
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