Low Cost Knowledge Base System for Design of Deep Drawing Die Set

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ISSN: 2278-0149 (Online)
Short Title: Int. J. Mech. Eng. Robot. Res.
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Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.

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Home > Articles > All Issues > 2013 > Volume 2, No. 1, January 2013 >

B V S Rao1 , G Chandra Mohan Reddy1, G Krishna Mohan Rao2 P Ranadheer 1, and P V R Ravindra Reddy1

1.Department of Mechanical Engineering, CBIT, Gandipet, Hyderabad 75, Andhra Pradesh, India.
2.Department of Mechanical Engineering, JNTU College of Engineering, Hyderabad, Andhra Pradesh, India.

Abstract—An expert system is a computer system used to distribute the expertise of a human or group of humans throughout a group of users. In this paper a low cost Knowledge Base System (KBS) is proposed for design of deep drawing die. The task of building the system is structured into different modules for major activities of design of deep drawing die. A manufacturability assessment module of the proposed framework is developed to check the manufacturability of deep drawn parts. The technological knowledge is represented by using various logical operators especially IF Else loops and it is coded in ADOBE FLEX, FLASH CS5 language. The future module will be designed to be loaded into the prompt area of AutoCAD using AUTOLISP for automated design. The cost of implementation of proposed system makes it affordable for small and medium scale sheet metal industries.

Index Terms—Knowledge Base System (KBS), Deep drawing die, Manufacturability, Sheet metal

Cite: B V S Rao, G Chandra Mohan Reddy, G Krishna Mohan Rao, P Ranadheer, and P V R Ravindra Reddy, "Low Cost Knowledge Base System for Design of Deep Drawing Die Set," International Journal of Mechanical Engineering and Robotics Research, Vol.2, No. 1, pp. 246-253, January 2013.