Abstract
As the key component of intelligent CNC machine tool, CNC controller is expected to provide various intelligent functions, among which, autonomous process planning is one of the most important functions. However, current machining data models are not very suitable to support this function. G&M code gives movement and auxiliary instructions directly, making the CNC controller just an executor. A new standard STEP-NC, providing semantically rich information, aims to overcome the drawbacks of G&M code. However, information in the data model specified in STEP-NC is not very suitable for intelligent CNC controller autonomous process planning because of the lack of important machining requirements. Also, in STEP-NC model, machining strategies, which are supposed to be decided by CNC controller, have already been included. Namely, decision right is not totally handed over to the controller. To solve these problems, a STEP-based machining data model is proposed in this paper. The machining data model is an ARM, and it is designed as the machining task input of intelligent CNC controller. It can provide necessary workpiece information for intelligent process planning functions based on knowledge base. With the consideration of interoperation with design stage, STEP data format is used in the machining data model. Detailed structure and implementing method is introduced. In order to verify the feasibility of this data model, generation method of machining task complied with this data model and process plan generation based on the machining task have been put forward through a case. Results show the machining task complied with this data model can provide sufficient information for the autonomous process planning function of CNC controller.
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The work is supported by the National Natural Science Foundation of China (Grant No. 51405270).
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Zhu, W., Hu, T., Luo, W. et al. A STEP-based machining data model for autonomous process generation of intelligent CNC controller. Int J Adv Manuf Technol 96, 271–285 (2018). https://doi.org/10.1007/s00170-017-1554-9
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DOI: https://doi.org/10.1007/s00170-017-1554-9