UTHM Institutional Repository

Defective casting diagnosing via an enhanced knowledge hyper-surface method

Mohd Nawi, Nazri and Harun, Zawati and Wahid, Noorhaniza Defective casting diagnosing via an enhanced knowledge hyper-surface method. Applied Mechanics and Materials Vols 229-231.


Download (451kB)


The research on the analysis of cause and effect relationships in castings has always been a center of attention in the manufacturing industry. An intelligent diagnosis system should be able to diagnose effectively the causal representation and also justify its diagnosis. Recently, a method, known as the Knowledge Hyper-surface method which used Lagrange Interpolation polynomials has gained more popularity in learning cause and effect analysis in casting processes. The current method show that the belief value of the occurrence of cause with respect to the change in the belief value in the occurrence of effect can be modeled by linear, quadratic or cubic relationships and the method retained the advantages of neural networks and overcomes their limitations in learning the input-output mapping function in the presence of noisy, limited and sparse data. However, the methodology was unable to model exponential increase/decrease in belief values in cause and effect relationships. This paper proposed an enhancement to the current Knowledge Hyper-surface method by introducing midpoints in the existing shape formulation which further constrains the shape of the Knowledge hyper-surfaces to model an exponential rise in belief values but without exposing the data set to the limitations of 'over fitting'. The ability of the proposed method to capture the exponential change in the belief variation of the cause when the belief in the effect is at its minimum is compared to the current method on real casting data.

Item Type: Article
Uncontrolled Keywords: lagrange interpolation polynomials;knowledge hyper-surface; belief variation;exponential rise
Subjects: T Technology > TS Manufactures > TS200-770 Metal manufactures. Metalworking
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: M.Iqbal Zainal A
Date Deposited: 06 Mar 2013 04:45
Last Modified: 06 Mar 2013 04:45
URI: http://eprints.uthm.edu.my/id/eprint/3423
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item


Downloads per month over past year