2013
DOI: 10.1016/j.procir.2013.06.042
|View full text |Cite
|
Sign up to set email alerts
|

Casting Defect Analysis using Design of Experiments (DoE) and Computer Aided Casting Simulation Technique

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
45
0

Year Published

2014
2014
2019
2019

Publication Types

Select...
5
5

Relationship

0
10

Authors

Journals

citations
Cited by 103 publications
(46 citation statements)
references
References 1 publication
1
45
0
Order By: Relevance
“…In the Analyze phase, causes of problems are studied and prioritized. Then, the Design of Experiment (DOE) is introduced to screen for significant factors [7,8]. In the Improve phase, the response surface methodology (RSM) is employed to find the optimal setting of each significant factor [9,10].…”
Section: Methodsmentioning
confidence: 99%
“…In the Analyze phase, causes of problems are studied and prioritized. Then, the Design of Experiment (DOE) is introduced to screen for significant factors [7,8]. In the Improve phase, the response surface methodology (RSM) is employed to find the optimal setting of each significant factor [9,10].…”
Section: Methodsmentioning
confidence: 99%
“…Dabade and Bhedasgaonkar [18] proposed method of casting defect analysis by combination of design of experiment method and computer aided casting simulation technique. They aimed to find optimal setting of moulding sand and mould related parameters of green sand casting process of wheel hub casting.…”
Section: Defect Minimization In Casting Through Process Improvement-amentioning
confidence: 99%
“…Surrogate models are used in many fields and a large amount of works was found related to this method. Applications for structural mechanics [45], Computed Fluid dynamics [46,47], electromagnetics [48,49], discrete event simulation for manufacturing processes [50] or forming process [51,52] can be mentioned. Furthermore, surrogate models are used to fulfil Finite Element models objectives faster, as model approximation, design space exploration, sensitivity analysis [53] and optimisation [30].…”
Section: Metamodelsmentioning
confidence: 99%