A statistical model of routine process monitoring variates and conformance assessment for radiation processing

被引:0
作者
Lundahl, Bradley [1 ]
机构
[1] Johnson & Johnson, Microbial Qual & Steril Assurance, Raritan, NJ 08869 USA
关键词
Routine radiation process monitoring; Statistical process control; Bivariate normal distribution; Conformance assessment; Risk assessment;
D O I
10.1016/j.radphyschem.2022.110640
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Modeling a routine radiation process monitoring variate consists of identifying a statistical model that accurately forecasts process variate solution space and provides an inference structure by which processed product conformance to dose range specifications can be assessed with a high degree of surety. This paper shall identify and describe the structural characteristics of the process variates associated with the various forms of routine radiation process monitoring and demonstrate the Bivariate Normal Joint Probability distribution model applicability to routine radiation processing end to end. The routine radiation process end to end consists of: a. Performance qualification dose mapping (PQ) b. Capability/reliability assessment c. Process target optimization (PTO) d. Statistical process control (SPC) of the routine process e. Product conformance assessment of processed products to dose range specifications. The significance of this model is it is a probabilistic risk/confidence assessment of the process output variate. This treats the sampled variate output as expressing the aggregate variance of all expressing predictor variables (common cause), accurately treats output variates as random variates (solution space) and avoids the errors of the current routine radiation process monitoring industry model.
引用
收藏
页数:14
相关论文
共 9 条
[1]  
[Anonymous], 2021, E3239 ASTM INT, DOI [10.1520/E3239-21, DOI 10.1520/E3239-21]
[2]  
ASTM MNL7, 2010, MNL7 ASTM INT, Veighth
[3]  
Balakrishnan Narayanaswamy, 2009, Continuous Bivariate Distributions, DOI [10.1007/b101765, DOI 10.1007/B101765]
[4]  
Bertsekas D.P., 2008, INTRO PROBABILITY, V13
[5]  
Fisher R.A., 1921, Metron, V1, P3
[6]   Determining Distribution for the Quotients of Dependent and Independent Random Variables by Using Copulas [J].
Ly, Sel ;
Pho, Kim-Hung ;
Ly, Sal ;
Wong, Wing-Keung .
JOURNAL OF RISK AND FINANCIAL MANAGEMENT, 2019, 12 (01)
[7]   Density of the ratio of two normal random variables and applications [J].
Pham-Gia, T. ;
Turkkan, N. ;
Marchand, E. .
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2006, 35 (09) :1569-1591
[8]  
RStudio Team, 2016, RSTUDIO INTGR DEV R
[9]  
Swaroop R., 1980, NASA TECHNICAL MEMOR