A process planning system with feature based neural network search strategy for aluminum extrusion die manufacturing

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 geometry is defined according to the characteristics of the profile so we can reuse die features from the previous similar profile design cases. This paper presents the CaseXpert Process Planning System for die manufacturing based on feature based neural network technique. Die manufacturing cases in the case library would be retrieved with searching and learning method by neural network for reusing or revising it to build a die design and process planning when a new case is similar with the previous die manufacturing cases. The results of the system are dies design and machining process. The system has been successfully tested, it has been proved that the system can reduce planning time and respond high consistent plans

Surface Roughness Control Based on Digital Copy Milling Concept to Achieve Autonomous Milling Operation

AbstractIn order to develop an autonomous and intelligent machine tool, a system named Digital Copy Milling (DCM) was developed in our previous studies. The DCM generates tool paths in real time based on the principle of copy milling. In the DCM, the cutting tool is controlled dynamically to follow the surface of CAD model corresponding to the machined shape without any NC program. In this study, surface roughness control of finished surface is performed as an enhanced function of DCM. From rough-cut to semi-finish-cut and finish-cut operations, the DCM selects cutting conditions and generates tool paths dynamically to satisfy instructed surface roughness Ra. The experimental verification was performed successfully