摘要
在人工智能时代到来之际,计算机似乎已无所不能,那么我们每天进行的材料研究是不是也应该发生巨大的变化?我们一直采用的研究方法是不是可以借助强大的计算机有所变革等问题都非常值得思考。本文探讨了现在多因素研究方法的问题,提出了多因素均匀实验设计的基础上进行重要性追加实验设计方法,提示采用MATLAB强大数学工具能够便捷地获得多元非线性数学模型,为提高多因素材料研究效率和水平提供一种思路。
In the age of artificial intelligence,when computers seem to be able to do everything,should the material research conducted every day also change dramatically?Should the research methods we've been using be changed with the help of powerful computers?These problems are worthy of our consideration.Material research involves many influencing factors,such as material system itself and material preparation process,which is a typical multi-factor system problem.Unfortunately,it is difficult for us to study the non-linear multi-factor complex objects immediately.The univariization method that each factor is studied one by one has been used.Such research results have great limitations.For example,the interaction between factors cannot be revealed.Orthogonal experiments and uniform experimental design are important methods for conducting efficient multi-factor studies.However,the limitations of later data processing of this method cannot meet the requirements of high-level material research.This paper discusses the problem of multi-factor research methods.On the basis of multi-factor uniform experimental design,the importance design methods of additional experimental are proposed.The steps and effects of this method is illustrate with a tow-dimensional sample.First,a uniform experimental design was used to perform the first round studies with the multi-factors adjusted simultaneously.Multivariate cubic spline method was used to process the obtained multivariate data for obtaining the multivariate function.Then,the multivariate function is used as the weight function to conduct the importance experiment design for the second round of additional experiment design.Importance sampling can achieve more efficient experimental design based on the experimental data of the first round uniform sampling.Finally,multivariate nonlinear mathematical models can be obtained by using artificial neural network for all multidimensional experimental data.With the obtained model,factor optimization and properties prediction can be performed
作者
张跃
ZHANG Yue(School of Materials Science and Engineering,Beihang University,Beijing 100191,China)
出处
《现代技术陶瓷》
CAS
2024年第1期1-11,共11页
Advanced Ceramics
关键词
实验设计
多因素研究
重要性追加实验
数据处理
experimental design
multi-factor study
importance additional experiment design
data processing
