2010
DOI: 10.1179/030192309x12506804200780
|View full text |Cite
|
Sign up to set email alerts
|

Modelling of Charpy V test rejection probability

Abstract: The purpose of this study was to develop a product design model for estimating the impact toughness of low alloy steel plates. The rejection probability in a Charpy V test is predicted with process variables and chemical composition. Joint modelling of the mean and deviation was used in order to improve the results. The proposed method is suitable for the whole production line, including all grades of steel in production and it is not restricted to a few test temperatures. Using the proposed model the product … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2013
2013
2019
2019

Publication Types

Select...
4

Relationship

1
3

Authors

Journals

citations
Cited by 4 publications
(3 citation statements)
references
References 16 publications
0
3
0
Order By: Relevance
“…7 , 8 These models are suitable especially if the response variable has Gamma distribution. After log-transformation, the model can be fitted using Least Squares, as in Tamminen et al 9…”
Section: Methodsmentioning
confidence: 99%
“…7 , 8 These models are suitable especially if the response variable has Gamma distribution. After log-transformation, the model can be fitted using Least Squares, as in Tamminen et al 9…”
Section: Methodsmentioning
confidence: 99%
“…Variance modelling has been used, for example, in quality improvement experiments designed to map the process settings that minimize variance under given conditions. Variance modelling is also naturally suited for tolerance design, because it helps to find the sources of variance in the process (Engel, 1992;Tamminen et al, 2010). The purpose of variance function estimation is to model the structure of the variances as a function of predictors (Carroll and Ruppert, 1988).…”
Section: Models For Quality and Rejection Probability Predictionmentioning
confidence: 99%
“…Industrial processes data is often heteroscedastic, which means that the noise process of the model is input-dependent as well. The advantage of the deviance modelling approach has been presented in [8] and [15].…”
Section: The Modelling Stepsmentioning
confidence: 99%