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INSTRUMENTATION TECHNOLOGIES FOR IMPROVING MANUFACTURING QUALITY

机译:改善制造质量的仪器技术

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摘要

For the past decade or more American manufacturing has been committed to the principle of improving quality. Deming and others have defined philosophies and techniques that lead to improvement. To successfully implement such improvement, critical processes must be carefully monitored through ongoing analysis of process performance using Statistical Quality Control methods. The measurements required to successfully implement Statistical Quality Control (SQC) require more sophisticated instrumentation than simple gauging blocks and other goo-go testing techniques. Instrumentation needs to give actual measurements, not just limit checks, and the measurements need to be supplied to some statistical calculating engine for SQC. Instrumentation technologies have continued to progress. Data Acquisition Systems can help to integrate process control with management information systems. Smart sensors, fieldbusses, embedded processors, artificial intelligence and other emerging technologies are becoming available for industrial control applications. There are now great opportunities for manufacturers to have much better understanding and control of their processes. Quality and productivity are dependent upon good instrumentation. The accuracy and timeliness of the instrumentation determine the information used for SQC. This information can be provided to operators in real-time and in a form appropriate for quality control. Significant cost savings are achievable.
机译:在过去的十年或更长时间里,美国制造业一直致力于提高质量的原则。戴明和其他人已经定义了导致改进的哲学和技术。为了成功实施这种改进,必须使用统计质量控制方法通过对过程性能进行持续分析来仔细监视关键过程。与简单的量块和其他通过/不通过测试技术相比,成功实施统计质量控制(SQC)所需的测量需要更复杂的仪器。仪器需要提供实际的测量值,而不仅仅是极限检查,并且需要将测量值提供给SQC的某些统计计算引擎。仪器技术一直在进步。数据采集​​系统可以帮助将过程控制与管理信息系统集成在一起。智能传感器,现场总线,嵌入式处理器,人工智能和其他新兴技术正变得可用于工业控制应用。现在,制造商有很多机会可以更好地了解和控制其过程。质量和生产率取决于良好的仪器。仪器的准确性和及时性决定了用于SQC的信息。该信息可以以适合质量控制的形式实时提供给操作员。可以节省大量成本。

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