Research and development of automatic monitoring technique for carriage preload degradation and adaptive spindle speed estimating techniques of machine tools

• Main Authors: CHENG,YIN-CHUN, 鄭尹俊
• Other Authors: CHENG,CHIH-CHUN
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/22994314019734070302

碩士 === 國立中正大學 === 機械工程系研究所 === 104 === Two important techniques of machine tool based on operational modal analysis (OMA) are proposed and realized. The first is an automatic monitoring technique for preload degradation of linear guide ways, and the second is an adaptive spindle cut-ting speed technique. The linear guideway type (LGT) recirculating linear ball bearing abrasion caused by long time operation, eventually leads to preload loss, which however often occurs much earlier than the guide ways fatigue. Therefore, detecting the preload loss be-comes an important issue especially in a machine tool designed for high speed and high accuracy. In this study, a novel methodology of monitoring degradation of linear guideway type recirculating linear ball bearing of an X-Y table is proposed. By simply attaching accelerometers on the worktable of the feed drive system and then exciting the worktable with a pulse from servo motor, the worktable natural frequencies and the corresponding mode shapes are identified based on the method of OMA. Thereaf-ter, tracking the change of yawing mode frequency of worktable using modal assur-ance criteria (MAC), the linear bearing preload degradation can be monitored auto-matically without exciting the worktable manually. The material removal rate (MRR) reflects the machining efficiency, so how to increase the MRR without inducing instability, i.e. chatter, is always an important is-sue in machining. In this study, the natural frequencies, mode shapes and correspond-ing damping ratio of spindle tool system are identified firstly by using OMA. With these information, the stability lobe diagram (SLD) which depicts the machining sta-bility in terms of spindle speed and stiffness ratio can then be created. With this SLD, the optimal spindle speed can be determined. During the machining, an adaptive op-timal spindle speed machining (AOSS) technique is proposed. That is, during the ma-chining process the dynamic characteristic of the spindle tool system and then the SLD are continuously identified and updated using OMA and then the spindle speed changes accordingly. Results show that using AOSS not only increases the MRR but also maintains a good workpiece surface roughness. Moreover, the associated spindle vibration and noise are also effectively reduced.