Finite Mixture Models for Nonidentical Multiple Tooled Manufacturing

2006 ◽  
Vol 128 (4) ◽  
pp. 996-1005
Author(s):  
Allen T. Bracken

This paper presents a novel method to assess nonidentical multiple tooled (NIMT) manufacturing processes (like multiple cavity injection molding) using finite mixture distribution (FMD) models. A stepwise methodology is presented, including supporting mathematics and statistics. The methodology is illustrated and supported by its application to two sets of real multicavity injection molding data. The method is commercially relevant and is significant in that it allows enhanced examination of the fraction of the parts nonconforming or better setting of the specification level. Included are discussions of FMD models versus normal models and novel tail probability comparison methods (ratio of tail probabilities and log PDF comparisons). The methodology is recommended for NIMT processes, and is thought to better address the adequacy evaluation of processes where there are multiple nonidentical distributions mixing in production.

Risks ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 115
Author(s):  
Despoina Makariou ◽  
Pauline Barrieu ◽  
George Tzougas

The key purpose of this paper is to present an alternative viewpoint for combining expert opinions based on finite mixture models. Moreover, we consider that the components of the mixture are not necessarily assumed to be from the same parametric family. This approach can enable the agent to make informed decisions about the uncertain quantity of interest in a flexible manner that accounts for multiple sources of heterogeneity involved in the opinions expressed by the experts in terms of the parametric family, the parameters of each component density, and also the mixing weights. Finally, the proposed models are employed for numerically computing quantile-based risk measures in a collective decision-making context.


2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Javier Juan-Albarracín ◽  
Elies Fuster-Garcia ◽  
Alfons Juan ◽  
Juan M. García-Gómez

Author(s):  
Tracey Ward ◽  
Raphael Bernier ◽  
Cora Mukerji ◽  
Danielle Perszyk ◽  
James C. McPartland ◽  
...  

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