NDLI: An intelligent system for squeeze casting process

Content Provider	Springer Nature Link
Author	G. C., Manjunath Patel Krishna, Prasad Parappagoudar, Mahesh B.
Copyright Year	2016
Abstract	The present work deals with the forward and reverse modelling of squeeze casting process by utilizing the neural network-based approaches. The important quality characteristics in squeeze casting, namely surface roughness and tensile strength, are significantly influenced by its process variables like pressure duration, squeeze pressure, and pouring and die temperatures. The process variables are considered as input and output to neural network in forward and reverse mapping, respectively. Forward and reverse mappings are carried out utilizing back propagation neural network and genetic algorithm neural network. For both supervised learning networks, batch training is employed using huge training data (input-output data). The input-output data required for training is generated artificially at random by varying process variables between their respective levels. Further, the developed model prediction performances are compared for 15 random test cases. Results have shown that both models are capable to make better predictions, and the models can be used by any novice user without knowing much about the mechanics of materials and the process. However, the genetic algorithm tuned neural network (GA-NN) model prediction performance is found marginally better in forward mapping, whereas BPNN produced better results in reverse mapping.
Starting Page	3051
Ending Page	3065
Page Count	15
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	86
Issue Number	9-12
e-ISSN	14333015
Language	English
Publisher	Springer London
Publisher Date	2016-02-10
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Squeeze casting process Forward mapping Reverse mapping BPNN and GA-NN Industrial and Production Engineering Production Mechanical Engineering Computer-Aided Engineering (CAD, CAE) and Design
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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