Extending Statistical Models for Batch-End Quality Prediction to Batch Control

2013 ◽  
pp. 40-54
Author(s):  
Geert Gins ◽  
Jef Vanlaer ◽  
Pieter Van den Kerkhof ◽  
Jan F. M. Van Impe
Keyword(s):  
Statistical Models ◽  
Quality Prediction ◽  
Batch Control

scholarly journals Weld-Quality Prediction Algorithm Based on Multiple Models Using Process Signals in Resistance Spot Welding

Metals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1459
Author(s):  
Sehyeon Kim ◽  
Insung Hwang ◽  
Dong-Yoon Kim ◽  
Young-Min Kim ◽  
Munjin Kang ◽  
...  
Keyword(s):  
Statistical Models ◽  
Spot Welding ◽  
Indentation Depth ◽  
Welded Joint ◽  
Prediction Algorithm ◽  
Quality Prediction ◽  
Weld Quality ◽  
Tensile Shear ◽  
Resistance Spot ◽  
Spot Welded

An efficient nondestructive testing method of resistance spot weld quality is essential in evaluating the weld quality of all welded joints in the automotive components of a car body production line. This study proposes a quality prediction algorithm for resistance spot welding that can predict the geometrical and physical properties of a spot-welded joint and evaluate weld quality based on quality acceptance criteria. To this end, four statistical models that predict the main geometrical and physical properties of a spot-welded joint, including tensile shear strength, indentation depth, expulsion occurrence, and failure mode, were estimated based on material information, dynamic resistance, and electrode displacement signals. The significance of the estimated models was then verified through an analysis of variance. The prediction accuracies of the models were 94.3%, 93.4%, 97.5%, and 85.0% for the tensile shear strength, indentation depth, expulsion occurrence, and failure modes, respectively. A weld quality evaluation methodology that can predict the properties of a spot-welded joint and evaluate the overall quality requirements based on authorized welding standards was proposed using the four statistical models.

Identifying Deep Sweetspots: Seismically Constrained Reservoir Quality Prediction Offshore Brunei

2016 ◽  
Author(s):  
Stephan Gelinsky ◽  
Sze-Fong Kho ◽  
Irene Espejo ◽  
Matthias Keym ◽  
Jochen Näth ◽  
...  
Keyword(s):  
Reservoir Quality ◽  
Quality Prediction

Signature Analysis: Statistical Models and Their Application to FA

1996 ◽  
Author(s):  
C.K. Lakshminarayan ◽  
S. Pabbisetty ◽  
O. Adams ◽  
F. Pires ◽  
M. Thomas ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Failure Analysis ◽  
Statistical Models ◽  
Essential Elements ◽  
Assessment Model ◽  
Signature Analysis ◽  
Risk Assessment Model ◽  
Sample Sizes ◽  
Reliability Engineering ◽  
Semiconductor Chips

Abstract This paper deals with the basic concepts of Signature Analysis and the application of statistical models for its implementation. It develops a scheme for computing sample sizes when the failures are random. It also introduces statistical models that comprehend correlations among failures that fail due to the same failure mechanism. The idea of correlation is important because semiconductor chips are processed in batches. Also any risk assessment model should comprehend correlations over time. The statistical models developed will provide the required sample sizes for the Failure Analysis lab to state "We are A% confident that B% of future parts will fail due to the same signature." The paper provides tables and graphs for the evaluation of such a risk assessment. The implementation of Signature Analysis will achieve the dual objective of improved customer satisfaction and reduced cycle time. This paper will also highlight it's applicability as well as the essential elements that need to be in place for it to be effective. Different examples have been illustrated of how the concept is being used by Failure Analysis Operations (FA) and Customer Quality and Reliability Engineering groups.

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