An intelligent process planning system for prismatic parts using STEP features

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Date

2007

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Journal ISSN

Volume Title

Publisher

Springer London Ltd

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Organizational Unit
Manufacturing Engineering
(2003)
Opened in 2003 with the aim to graduate experts in the field of machine-production, our Department is among the firsts in our country to offer education in English. The Manufacturing Engineering program focuses on the manufacturing technologies that shape materials from raw materials to final products by means of analytical, experimental and numerical modeling methods. First Manufacturing Engineering Program to be engineered by Müdek, our department aims to graduate creative and innovative Manufacturing Engineers that are knowledgeable in the current technology, and are able to use production resources in an effective and sustainable way that never disregards environmental facts. As the first Department to implement the Cooperative Education Program at Atılım University in coordination with institutions from the industry, the Manufacturing Engineering offers a practice-oriented approach in education with its laboratory infrastructure and research opportunities. The curriculum at our department is supported by current engineering software, and catered to creating engineers equipped to meet the needs of the production industry.

Journal Issue

Abstract

This paper presents an intelligent process planning system using STEP features (ST-FeatCAPP) for prismatic parts. The system maps a STEP AP224 XML data file, without using a complex feature recognition process, and produces the corresponding machining operations to generate the process plan and corresponding STEP-NC in XML format. It carries out several stages of process planning such as operations selection, tool selection, machining parameters determination, machine tools selection and setup planning. A hybrid approach of most recent techniques ( neural networks, fuzzy logic and rule-based) of artificial intelligence is used as the inference engine of the developed system. An object-oriented approach is used in the definition and implementation of the system. An example part is tested and the corresponding process plan is presented to demonstrate and verify the proposed CAPP system. The paper thus suggests a new feature-based intelligent CAPP system for avoiding complex feature recognition and knowledge acquisition problems.

Description

Amaitik, Saleh/0000-0001-7055-4461

Keywords

process planning, CAPP, STEP, neural networks, fuzzy logic

Turkish CoHE Thesis Center URL

Citation

76

WoS Q

Q2

Scopus Q

Source

Volume

31

Issue

9-10

Start Page

978

End Page

993

URI

https://doi.org/10.1007/s00170-005-0269-5
 https://hdl.handle.net/20.500.14411/934

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