The Applications Of Factorial Design In Energetic Material

As a kind of special energy which has unique physicochemical properties,energetic materials has extensive applications in military,civil and other fields.The researchers who study energetic materials at home and abroad have made continuous progress.They discovered and researched a lot kinds of new energetic materials,promoting the development of modern science and technology.According to different research directions,various research methods across diverse disciplines are applied to studies.Recently,statistical methods have been adopted by more and more scientific research projects,having become important research methods.In the studies of energetic materials,the quantities of researches which explore the different effects of various factors and select the main factors is small.Consequently,it's very meaningful to explore the factors which influence energetic materials with the knowledge of statistics.From the perspective of the branch of statistics – design of experiments,the article studied the factors which have statistically significant effect on energetic materials.Firstly,the possible influence factors are found through the investigation.Then,the experimental scheme is worked out on the basis of the knowledge about factorial design and orthogonal design.To ensure the accuracy of factorial effect,the criterions of purity and maximum discernibility degree are considered.Secondly,the data simulation is made by computer.And then making visual analysis and plotting the effects plots of the factors.Thirdly,the ANOVA model is built.The best factor combination is picked out by filtrating the main effect and the second-order interaction which has significant influence to the experiment.Lastly,the regression analysis model which includes qualitative and quantitative factors is built.We can fit the regression equation and get the exact effect of the factors by the method of stepwise regression.Therefore,the article provides the complete solution from the experiment design to the data analysis.It has important practical significance.