While optimizing machining parameters through GRA, in the final step GRG of initial, predicted and experimental parameters were compared to find the effect of GRA. In this regard on what basis the initial parameter combination selection has been made. In articles there is no explanation about this.
Hi Gopal,
It depends on your response. In Grey Relational Analysis (GRA), there are three different types of grey relational generation:
For example, if you want to find out the optimal parameter combinations to minimize your responses then the function 'Smaller the better' must be used.
On what basis the initial parameter combination selection has been made. The answer is the parameter combination must be made based on the level of each parameter which can maximize GRA. Simply, generate the mean plot of GRG and then look at the plot and select the highest point in the graph for each parameter to maximize GRG. The maximum point in each plot for each parameter is the optimal setting for your response.
First of all by trial run you have to choose your parameters. For confirmation test initial setting parameters you should use for comparing with the experimental results
in several paper about grey relational analysis combined with taguchi methode , I found initial parameter design to compare predictive value in confirmation test . For example : initial factor A1B2C2D1 compare with optimal result A2B2C2D2. Anyone can explain how determine the initial parameter design or initial optimal faktor? I really confuse about this.... Thank you
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