摘要
五轴数控机床动态性能是影响工件加工质量的重要因素,S型试件作为校验机床动态性能的检验试件,在实际应用中取得了一定成果。为进一步确定机床的加工状态,提高工件加工质量,提出了一种基于S型试件切削的五轴数控机床动态因素辨识方法。综合采用了多体运动学、模糊理论、BP神经网络理论对机床动态因素进行辨识。该方法可以用于评估数控机床的动态性能,通过误差溯源给出的机床动态因素,可用于指导机床的维修和调整,保障关键工件的数控加工质量。
The dynamic parameters of five-axis CNC have an important impact on machining quality,＂S＂ specimen as a calibrator test for the dynamic performance of machine tool achieved certain results in practical applications.An identification method based on the ＂S＂ specimen is put forward for determining the processing status of the machine and processing quality.The multi-body kinematics,fuzzy theory and BP neural network theory are used to identify the machine dynamic factors.This method can evaluate the dynamic performance of CNC machine tool,guide the maintenance and adjustment of the machine and protect the processing quality of the important parts.
出处
《制造技术与机床》
CSCD
北大核心
2012年第12期152-156,共5页
Manufacturing Technology & Machine Tool
基金
国家科技重大专项:国产高档数控机床在典型飞机结构件加工中的示范应用(课题编号:2010ZX04015-011)
关键词
数控机床
动态性能S型试件
误差辨识
BP神经网络
CNC Machine Tool
Dynamic Performance
＂S＂ Specimen
Error Identification
BP Neural Network
