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Statistical Issues in Geometric Tolerance Verification UsingCoordinate Measuring Machines

Statistical Issues in Geometric Tolerance Verification UsingCoordinate Measuring Machines
使用三坐标测量机验证几何公差的统计问题
批准号:
9203054
负责人:
Mary Dowling
金额:
$14.73万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
1992
资助国家:
美国
项目状态:
已结题
起止时间:
1992-06-15 至 1995-05-31

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中文摘要
翻译
目前在公差方面几乎没有指导, 评价零件特征几何变化的标准 坐标测量机(CMM)。 从业人员面临着 许多重要但尚未回答的问题,包括 应在零件特征上进行的测量,以及 这些测量的位置。 由于标准要求 知识的整个功能,而CMM可以采取只有一个小的 在一个合理的时间段内,没有简单的答案, 这些问题。 本研究的目的是开发一种 严格的标准几何验证程序 公差规格,在精度和 成本 研究将涉及以下任务: 研究部件,以便识别常见的轮廓变化 特定过程的模式; 测量几何变化的估计技术; 以及研究这些数字与 测量和由此产生的采样风险。 该研究的影响将在几个领域,包括 制定一个程序,以确定适当的数目, 和测量的位置,以满足特定的风险水平。 将利用有关功能的先前信息 减少样本量,同时保持相同 估计精度 教育影响也将包括在 课堂教学的形式,研究学生,研讨会, 两会
英文摘要
There is currently little or no guidance in the tolerancing standards for evaluating geometric variation of part feature using coordinate measuring machines (CMMs). Practitioners are faced with many important but unanswered questions, including the number of measurements that should be made on a part feature, and the location of such measurements. Since the standard requires knowledge of the entire feature while a CMM can take only a small sample in a reasonable time period, there are no simple answers to these questions. The objective of this research is to develop a rigorous procedure for the verification of standard geometric tolerance specifications, developing tradeoffs between accuracy and cost. The research will involve the following tasks: a detailed study of parts to allow identification of common profile variation patterns for specific processes; the development and evaluation of estimation techniques for the measurement of geometric variation; and the study of the relationship between the number of measurements and the resulting sampling risks. The impact of the research will be in several areas, including the development of a procedure to determine an appropriate number and location of measurements to satisfy specified risk levels. Prior information will be exploited regarding feature characteristics to reduce sample sizes while maintaining the same estimation accuracy. Educational impact will also be included in the form of classroom instruction, research students, seminars, and conferences.
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