THE METHOD OF MULTIVARIATE STATISTICAL ANALYSIS OF TIME MULTIVARIATE CRITICAL ATTRIBUTES OF MANUFACTURING QUALITY WITH DATA FACTORIZATION
AbstractThe object of the study is the process of product quality assurance at the stage of the initial design of the manufacturing process. The subject matter is informational technologies for assessing the factor influence of critical parameters of the process of manufacturing (CPPs) on the critical quality attributes of a product (CQAs). The goal of the study is to use the method of multivariate statistical analysis for assessing the character and features of the influence of time multivariate critical process parameters on time multivariate critical quality attributes at the stage of designing the manufacturing process. The task of the study is to determine the structure and hierarchy of time multivariate data of CPPs and CQAs and to determine qualitatively and quantitatively the relationship among the formed objects of the specified parameters. The following methods were consistently used – statistical procedures of the exploratory analysis of multivariate data; transforming the homogeneous observed values matrices of CPPs and product CQAs into the data table with factorized data; deriving the regression trees of multivariate CPPs with multivariate responses (CQAs). The methods implement the software packages of the R language. The following results were obtained – the method to solve the problem of product quality assurance at the stage of designing the initial manufacturing process in accordance with the process-analytical technology for designing modern certified manufacturing standards such as QbD (Quality-by-Design) is suggested. The method uses the information technologies of multivariate statistical analysis (MSA) to evaluate the influence of time multivariate critical process parameters (CPPs) on the time product critical quality attributes (CQAs). Preparatory transformation of clusters of critical process (manufacture process) parameters into factors of product critical quality attributes was carried out. Factorized time multivariate CPPs enable using the methods of multivariate statistical analysis for assessing the impact of CPP factors on the time multivariate CQAs. Conclusions. This method of statistical analysis along with statistical multivariate canonical analysis present the up-to-date information technology for the detailed assessment of the influence of time multivariate CPP objects and some CPP components on CQAs. The method is oriented to practical application to assure the quality of products at the stage of designing (improving) manufacturing processes.
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