2017•2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC)Requires access

An effective data mining approach of existing CAM models for NC machining process reuse

Rui Huang, Yun Chen

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Abstract

To effectively reuse existing NC machining process of similar part and feature, an effective data mining approach of existing CAM models in machining process data is proposed. First, a machining feature based multilevel structured CAM model is proposed to reveal the relations between machining features and machining operations. Then, the structured machining know-how database is automatically generated through data mining of existing CAM models. Finally, a prototype system based on CATIA has been developed to verify the effectiveness of the proposed approach.

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What this paper is about

To effectively reuse existing NC machining process of similar part and feature, an effective data mining approach of existing CAM models in machining process data is proposed. First, a machining feature based multilevel structured CAM model is proposed to reveal the relations between machining features and machining operations. Then, the structured machining know-how database is automatically generated through data mining of existing CAM models. Finally, a prototype system based on CATIA has been developed to verify the effectiveness of the proposed approach.

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Available abstract

To effectively reuse existing NC machining process of similar part and feature, an effective data mining approach of existing CAM models in machining process data is proposed. First, a machining feature based multilevel structured CAM model is proposed to reveal the relations between machining features and machining operations. Then, the structured machining know-how database is automatically generated through data mining of existing CAM models. Finally, a prototype system based on CATIA has been developed to verify the effectiveness of the proposed approach.

Key concepts: Machining, Reuse, Process (computing), Feature (linguistics), Computer science, Data model (GIS), Data mining, Engineering drawing

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