【作者】 逄广思；

【导师】 申桂香；

【作者基本信息】 吉林大学 ， 机械电子工程， 2009， 硕士

【摘要】 现代数控机床的研制是一个庞大的系统工程,必须走多企业联合研制的道路,以充分发挥各设计单位和制造单位的优势,共享产品开发的资源和经验。异地设计、制造、管理与协同工作是现在乃至未来数控机床工业发展的必然趋势。配套件作为数控机床的重要组成之一,其可靠性水平的好坏直接影响到整机的质量和产品的声誉。因此,提高配套件的可靠性是关系到主机行业和配套件行业生存与发展的大问题,积极开展协同环境下的数控机床配套件可靠性分析技术的研究,对整机生产企业合理选择配套件,提高整机的可靠性水平具有十分重大的现实意义。可靠性分析是可靠性设计、可靠性预计、故障诊断等可靠性研究的重要基础。本文采用随机过程、概率论与数理统计、可靠性工程等多学科相结合的方法,以某系列加工中心故障数据为例,通过统计分析,从整机上掌握因配套件原因引起的此系列加工中心的故障发生情况,并对故障频繁的部件或子系统进行深入分析,探寻可靠性改进设计的方向。同时,从“可修复”和“不可修复”的角度出发,以计算机为工具,进行加工中心配套件可靠性分析。更多还原

【Abstract】 The reliability of CNC machine tools relate to many different departments of host-plant production, the supplier and cooperation manufacturers of corollary parts and purchased parts, operators, maintenance personnel and equipment management department. It is system engineering in the collaborative environment which needs the cooperation of various departments and enterprises. Corollary parts, as an important component of CNC machine tools, its reliability has a direct impact on the quality of whole machine as well as the reputation of its products. Therefore, enhancing corollary parts’reliability is an important issue related to the survival and development of host and corollary parts industry. Meanwhile, research on reliability analysis technique of corollary parts of CNC machine tools based on collaborative environment has a great practical significance on manufacturers in choosing reasonable corollary parts and to improve reliability of CNC machine tools. The reliability analysis technique of corollary parts has laid the foundation for achieving great breakthrough of national project“863”“Dependability modeling and analysis techniques of heavy CNC equipment facing life cycle”.1. FMECA on corollary parts of machining center in collaborative environmentThe paper had researched on a series of machining center; used time truncated testing by conducting on field test to 20 machining centers of this series in order to collect information of reliability test. Then I had elected 48 machining center failure data caused by corollary parts from 106 collected and accumulated machining center failure data. Afterwards, the paper conducted on failure mode and effect and criticality analysis to the 48 failure data. And researched on fault position, fault mode and ratio of fault causes due to corollary parts, then mastered fault occurrence condition of this series machining center on account of corollary parts from the whole machine. Furthermore, this paper made fault mode an effect analysis to the components or subsystems which had caused frequent failures to the whole machine, then explored the direction of reliability improvement design. After analysis, I got the conclusion that tool magazine was the main position to machining center failure and the main failure mode was tool imbalance, the main effect was parts damaged. Besides, the criticality of tool magazine was the highest. Therefore, tool magazine was the key corollary part affecting the reliability of machining center. 2. Reliability analysis on unrepairable corollary parts of machining centerAnalysis of reliability data of unrepairable corollary parts of machining center, establish reliability data model with non-replacement random censoring according to the characteristic of unrepairable corollary parts’failure data. Based on the FMECA mentioned before, the majority failure of unrepairable corollary parts of machining center were abandoned after damaged and the parts being replaced. We assumed that the statistical data obey Weibull distribution as most of the mechanical products were subject to Weibull distribution. Then carrying out least squares parameter estimation, correlation coefficient test and d test. The results showed that the data were subject to two-parameter Weibull distribution. As scatter diagram of empirical distribution function was a convex function, besides, the shape parameter of Weibull close to 1, the failure data may also be subject to exponential distribution, then carrying out hypothesis testing to exponential distribution using the same method. Then plot the scatter diagram of empirical distribution function both of exponential distribution and Weibull distribution on the same figure. It was found that all these empirical distribution function’points were subject to Weibull model when the time was relatively small. On the country when the time of failure-free operation was large, the empirical distribution function closed to exponential distribution. So it was difficult to determine which distribution model was better only from these curves. Therefore, the paper used compared the distribution function curve method“correlation index”to optimize distribution types. Finally, is was proved that the lifetime data of unrepairable corollary parts of machining center obey two-parameter Weibull distribution through goodness-of-fit test. Moreover, I had figured out probability density function f (t ), distribution function , failure rate function F (t )λ( t) as well as reliability function R (t ).Then calculated the observed value and point estimate value of mean time to failure (MTTF).3. Reliability analysis on repairable corollary parts of machining centerThis paper carried out stochastic process theory to analysis failure process of repairable corollary parts of machining center. Then adopt the total time method for data pre-processing according to the characteristics of random censoring in reliability test. It was concluded that the failure process was in line with renewal process by trend test and correlation test. However, time between failures may be subject to exponential distribution, Weibull distribution, normal or lognormal distribution and so on. Therefore, the paper assumed the failure data obey the four distribution mentioned above which were commonly in engineering. And all these four distribution were allowed from correlation test and d test. So this paper adopt correlation index for goodness-of-fit test, then it was concluded that fault data obey two-parameter Weibull distribution. Finally, I had figured out observed value of mean time between failure (MTBF), mean time to repair (MTTR) and inherent availability Ai.更多还原