A process planning system with feature based neural network search strategy for aluminum extrusion die manufacturing
S. Butdee (KMRC), Chaiwat Noomtong (LGS), Serge Tichkiewitch (LGS)

TL;DR
This paper introduces a neural network-based process planning system for aluminum extrusion die manufacturing that leverages feature-based case retrieval to improve efficiency, consistency, and reduce planning time.
Contribution
It presents a novel neural network approach for feature-based case retrieval in die manufacturing, enhancing process planning efficiency and consistency.
Findings
System reduces planning time significantly.
Achieves high consistency in die design and process plans.
Successfully tested with positive results.
Abstract
Aluminum extrusion die manufacturing is a critical task for productive improvement and increasing potential of competition in aluminum extrusion industry. It causes to meet the efficiency not only consistent quality but also time and production cost reduction. Die manufacturing consists first of die design and process planning in order to make a die for extruding the customer's requirement products. The efficiency of die design and process planning are based on the knowledge and experience of die design and die manufacturer experts. This knowledge has been formulated into a computer system called the knowledge-based system. It can be reused to support a new die design and process planning. Such knowledge can be extracted directly from die geometry which is composed of die features. These features are stored in die feature library to be prepared for producing a new die manufacturing. Die…
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Taxonomy
TopicsMetallurgy and Material Forming · Manufacturing Process and Optimization · Additive Manufacturing and 3D Printing Technologies
