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引用次数: 0

摘要

在过去的十多年里,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。为了成功地实施这种改进,必须通过对过程质量控制方法的持续分析来仔细地监视关键过程。成功实施统计质量控制(SQC)所需的测量需要比简单的量块和其他可用/不可用的测试技术更复杂的仪器。检测需要提供实际的测量,而不仅仅是限制检查,并且需要将测量提供给SQC的一些统计计算引擎。仪器仪表技术不断进步。数据采集系统有助于将过程控制与管理信息系统相结合。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在用于工业控制应用。现在,制造商有很大的机会更好地理解和控制他们的过程。质量和生产力取决于良好的仪器。仪器的准确性和及时性决定了SQC所使用的信息。这些信息可以以适合质量控制的形式实时提供给作业者。可以实现显著的成本节约。
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Instrumentation technologies for improving manufacturing quality
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 quality control methods. The measurements required to successfully implement statistical quality control (SQC) require more sophisticated instrumentation than simple gauging blocks and other go/no-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, fieldbuses, 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.
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