基于Minitab的质量控制技术在制造过程中的应用

通过对某摩托车厂加工质量的研究,运用质量控制技术分析其生产的90变速鼓的加工质量,使用Minitab软件绘制X-R控制图、直方图、因果图、排列图和工序能力分析图,对生产90变速鼓的质量进行基本分析,并从人机两方面提出对制造过程中质量控制的设想.     

SPC技术在数控加工工艺优化中的应用

采用基于统计过程控制(SPC)控制图和过程能力分析的方法,对数控加工的数据进行实时监控,实现了车间质量数据的分析处理和对产品质量的有效控制,达到了提高生产过程能力、降低生产成本,以及有效防止不良品流出的目的。     

基于89S52单片机的坦克部件试验台集成系统开发

坦克维修后的试验是保证维修质量的重要手段,目前的试验自动化程度低,各种试验台彼此独立,分布散乱.为此,我们研制开发了基于单片微型计算机的试验集成系统.该系统主要由基于89S52单片机的数据采集系统和PC机软件系统组成.由PC机控制89S52单片机完成数据的采集,采集后的数据传送给PC机进行数据的处理和显示,使用Access数据库管理数据,并可打印出试验报告.单片机和PC机的通讯采用了无线传输方式,使用无线通信模块RTR2000完成.该系统可将车间现有的多种试验台集成起来,使用一台PC机进行管理.     

数控螺旋锥齿轮磨齿机加工性能的实验研究

应用统计过程控制技术,对国产七轴五联动数控螺旋锥齿轮磨齿机YK2050的加工性能进行实验研究。根据实际磨齿加工采集的数据,运用平均值-全距控制图分析了机床的稳定性,并计算了机床的工序能力指数,研究结果表明,机床具有良好的加工稳定性以及充足的工序能力。最后对造成机床加工性能异常的原因进行了分析并给出了解决方法。     

基于PLC和PC的定量控制系统

介绍了基于PLC和PC的定量控制系统,分别从下位机PLC的实时定量控制和上位机PC的监控管理功能描述该系统,并给出了部分原理功能图,实践表明用本系统进行定量控制取得很好的效果.     

便携式测量机在曲面件在机测量中的应用

为解决便携式测量机在机检测某模型件唇部曲面时因基准定位误差而导致测量结果偏差过大的问题,基于在同一加工坐标系下加工的简单结构基准测量件和坐标匹配算法,设计通过对测量数据与理论值的偏差计算,并对坐标系进行旋转和平移匹配,使测量数据尽可能地包容曲面CAD模型；实践证明,该方法有效地匹配了测量坐标系和加工坐标系,实现了机加过程的质量控制.     

利用-s图控制汽车零件制造质量

阐述了应用计量控制图中的均值 -标准差控制图 (x -s图 )对汽车零件制造质量进行控制的计算机系统。该系统对机械零件加工质量进行实时控制 ,对加工过程的有关数据进行收集 ,按统计质量控制原理进行分类、统计、绘制控制图。对所得到的图进行分析 ,判断生产过程是否处于控制状态 ;还得到零件单项质量不合格率等统计结果     

数控螺旋锥齿轮磨齿机加工性能的实验研究

应用统计过程控制技术,对国产七轴五联动数控螺旋锥齿轮磨齿机YK2050的加工性能进行实验研究。根据实际磨齿加工采集的数据,运用平均值－全距控制图分析了机床的稳定性,并计算了机床的工序能力指数,研究结果表明,机床具有良好的加工稳定性以及充足的工序能力。最后对造成机床加工性能异常的原因进行了分析并给出了解决方法。

     

多机通信在复合钢板生产过程中的应用研究

为提高减振复合钢板的生产质量、精度。采用PC机与单片机主从式多机通信方法。设计了以单片机分别对复合钢板生产过程中所需树脂材料温度、钢板卷曲速度及钢板厚度信息进行采集并传送至PC机。通过PC机进行信息处理并向单片机发送控制命令的系统。从而使复合钢板生产的过程中树脂材料温度控制在所需范围,复合钢板厚度达到精度要求。通过理论分析。验证了该方法的有效性。     

浅析发动机制造业机加线SPC质量控制系统

主要阐述了发动机机加线实行SPC质量控制图的流程,从制定产品的管控特性、控制图的选择、抽样原则的确定、控制线计算以及后续持续改进过程等几个方面进行了简单介绍。SPC质量控制图的实施可以及时发现并消除异常点,提前预防产品超差,降低生产不合格件加工风险,提高产品下线合格率。     

工序质量控制系统的研究

对工序质量控制系统的实现进行了研究.系统可生成质量控制图表对工序质量进行控制.     

基于中小批量制造的SPC管理系统分析与设计

简要阐述了SPC统计分析方法及其控制图的概念,针对小批量生产的相关特征设计了一个SPC管理系统。系统主要依照控制图的思想,对中小制造企业的质量管理进行合理的控制。系统包含SPC分析模块、质量诊断模块和工序能力计算模块,通过多模块组合完成对中小批量制造的动态监控与管理。通过对该系统的分析与设计,促进了质量管理在质量检测中的应用,保证了企业对质量的控制。     

基于标准化变换的质量控制图研究

基于标准化变换的质量控制图一般应用在小批量环境下,对生产过程进行控制。分析标准化变换的准则,指出工序分析起到关键作用及能否对工序进行控制,不在于统计,而在于管理;通过对不同数据变换方式有效性的研究表明,各种标准化变换控制图的复杂性及灵敏性是不同的,因此要依工序分析的结果灵活采用各种标准化控制图。     

基于中小批量制造的SPC管理系统分析与设计

简要阐述了SPC统计分析方法及其控制图的概念,针对小批量生产的相关特征设计了一个SPC管理系统。系统主要依照控制图的思想,对中小制造企业的质量管理进行合理的控制。系统包含SPC分析模块、质量诊断模块和工序能力计算模块,通过多模块组合完成对中小批量制造的动态监控与管理。通过对该系统的分析与设计,促进了质量管理在质量检测中的应用,保证了企业对质量的控制。     

电机振动的质量检测与控制系统

设计了电机振动速度的在线自动检测与质量控制系统。通过电涡流传感器对工件的振动速度进行采集,经过以计算机为核心的控制系统的计算,利用休哈特控制图和过程能力指数等统计过程控制工具,通过人机界面实时显现,并根据闽值报警,实现对电机振动速度的质量控制。     

Monitoring multivariate–attribute processes based on transformation techniques

Control charts are widely used in monitoring the quality of a product or a process. In most of the cases, quality of a product or a process can be characterized by two or more correlated quality characteristics. Many control charts have been proposed for monitoring multivariate or multi-attribute quality characteristics, separately, but sometimes the correlated variables and attribute quality characteristics represents the quality of a process. In this paper, the use of four transformation methods is proposed to monitor the multivariate–attribute processes. In the first one, the distribution of correlated variables and attribute quality characteristics are transformed to approximate multivariate normal distribution, and then the transformed data are monitored by multivariate control charts including T ² and MEWMA. Based on the second transformation method, the correlated variables and attribute quality characteristics are transformed, such that the correlation between the quality characteristics approaches to zero, then univariate control charts are used in monitoring the transformed data. In the third and fourth proposed methods, a combination of two transformation methods is used to make the quality characteristics independent and to transform them to normal distribution. The difference between the third and fourth method is the order of using the transformation techniques. The performance of the proposed methods is evaluated by using simulation studies in terms of average run length criterion. Finally, the proposed approach is applied to a real dataset.     

Improving bonding strength of tape-automated bonding technology by implementing statistical process control: a case study

The purpose of this paper is to improve the bonding strength of tape-automated bonding (TAB) technology in supertwisted nematic liquid crystal display (STN LCD) module manufacturing by double-process control. First, the quality characteristic – the output of the process – is monitored by control charts and the variation of the process is reduced. Later, it is found that the quality characteristic is affected by critical input process parameters. These critical input process parameters and the quality characteristic are therefore monitored simultaneously. By this double-process control, the deviation of the process is further reduced. The results show that process capability and yield rate are increased.     

轴承锻造中的Minitab统计学分析

为了能够快速有效地对产品进行质量稳定性和过程能力分析,以锻造车间现场的轴承锻件锥度为例,采用Minitab软件对其数据进行统计,绘制控制图和过程能力图,通过因果图分析判定质量的稳定性和过程能力的有效性,并对存在的过程能力不足的问题提出了改进措施,使产品质量显著提高。     

Online intelligent monitoring and diagnosis of aircraft horizontal stabilizer assemble processes

The ability to reduce variation for quality improvement in the aircraft horizontal stabilizer assembly processes plays an essential role in the success of an aircraft manufacturing enterprise in today’s globally competitive marketplace. Monitoring and identifying variation source(s) of out-of-control signals are important issues for variation reduction in horizontal stabilizer assembly process. Traditional quality control focuses on statistical process control using control charts. However, control charts cannot identify variation source(s) of out-of-control signals. One novel integrated system is developed for monitoring and diagnosis of horizontal stabilizer assembly processes. $ \left| \Sigma \right| $ control charts are firstly designed to be used as the detector of abnormal signals, and then, an improved particle swarm optimization with simulated annealing (PSOSA)-based selective neural network (NN) ensemble approach is explored for identifying the variation source(s) of out-of-control signals. Utilization of selective NN ensemble algorithm is able to improve the generalization performance of neural systems in comparison with using single NN recognizers, and PSOSA algorithm aims to improve the ability to escape from a local optimum. The data from the real-world aircraft horizontal stabilizer assembly processes are collected to validate the developed system. The results indicate that the developed system can perform effectively for monitoring and identifying out-of-control signals of variance increases in terms of correct classification percentage and average run length.     

Monitoring process variability using exponentially weighted moving sample variance control charts

Exponentially weighted moving average (EWMA) control charts are regarded as one of the most convenient tools in detecting small process shifts. Although EWMA control charts have been extensively used to monitor the mean of quality characteristics, there are few studies concentrating on the monitoring of process variability by using weighted moving control charts. In this paper, we propose an exponentially weighted moving sample variance (EWMSV) control chart for monitoring process variability when the sample size is equal to 1. The results are compared numerically with other similar methods using the average run length (ARL). Through an example, the practical considerations are presented to implement EWMSV control charts.