scholarly journals Monitoring and Diagnosis of Multistage Manufacturing Processes Using Hierarchical Bayesian Networks

2021 ◽  
Vol 53 ◽  
pp. 32-43
Author(s):  
Partha Protim Mondal ◽  
Placid Matthew Ferreira ◽  
Shiv Gopal Kapoor ◽  
Patrick N Bless
2000 ◽  
Author(s):  
Yu Ding ◽  
Jionghua Jin ◽  
Dariusz Ceglarek ◽  
Jianjun Shi

Abstract In multistage manufacturing systems, quality of final products is strongly affected not only by product design characteristics but also by key process design characteristics. However, historically, tolerance research has primarily focused on allocating tolerances based on product design characteristics for each component. Currently, there is no analytical approach for multistage manufacturing processes to optimally allocate tolerances to integrate product and process characteristics at minimum cost. One of the major obstacles is that the relationship between tolerances of process and product characteristics is not well understood and modeled. Under this motivation, this paper aims at presenting a framework addressing the process-oriented (rather than product-oriented) tolerancing technique for multistage manufacturing processes. Based on a developed state space model, tolerances of process design characteristics at each fabrication stage are related to the quality of final product. All key elements in the framework are described and then derived for a multistage assembly process. An industrial case study is used to illustrate the proposed approach.


Author(s):  
N. Thompson Hobbs ◽  
Mevin B. Hooten

This chapter seeks to explain hierarchical models and how they differ from simple Bayesian models and to illustrate building hierarchical models using mathematically correct expressions. It begins with the definition of hierarchical models. Next, the chapter introduces four general classes of hierarchical models that have broad application in ecology. These classes can be used individually or in combination to attack virtually any research problem. Examples are used to show how to draw Bayesian networks that portray stochastic relationships between observed and unobserved quantities. The chapter furthermore shows how to use network drawings as a guide for writing posterior and joint distributions.


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