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引用次数: 0
摘要
近年来,先进信息技术与先进制造技术的深度融合逐渐成为实现智能制造的主要途径之一。计算机数控(CNC)系统是加工制造的基础设备,系统的加工质量和效率是支撑和保障智能制造的基础。然而,数控加工中使用的 G 代码通常是根据静态模型用计算机辅助制造(CAM)生成的,其刀具路径相对粗糙,相邻路径不均匀,路径中的坏点会造成加工缺陷。为解决这些问题,提出了一种将智能数控系统的基本要素与人-网络-物理系统(HCPS)模型相结合的建模方法,并进一步提出了刀具路径优化的数字化解决方案,将 CAM 刀具路径的再设计过程集成到网络应用中。此外,还对刀具路径优化过程进行了分步处理,并建立了流水线处理流程,以加速优化过程。最后,以五角星凸筋模型的工艺文件优化为例,演示了所提方法的有效性。
A digital solution for CPS-based machining path optimization for CNC systems
In recent years, the deep integration of advanced information technology and advanced manufacturing technology has gradually become one of the main ways to achieve smart manufacturing. The computer numerical control (CNC) system is the basic equipment for machining and manufacturing, and the quality and efficiency of the system’s machining are the basis for supporting and ensuring smart manufacturing. However, the G-code used in CNC machining is usually generated with computer-aided manufacturing (CAM) according to a static model, and its tool path is relatively rough, with uneven adjacent paths and bad points in the path causing machining defects. To solve these problems, a modeling approach combining the basic elements of the intelligent CNC system with the human-cyber-physical system (HCPS) model is proposed, and a digital solution for tool path optimization is further proposed, integrating the redesign process of CAM tool path into cyber application. In addition, the process of tool path optimization is processed in steps, and a pipelined processing flow is established to accelerate the optimization process. Finally, the effectiveness of the proposed method is demonstrated using an example of process file optimization for a pentagram convex rib model.
期刊介绍:
The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.