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所在平台: Udemy |
课程主页: https://www.udemy.com/course/tabtrainer-minitab-msa-gage-rr-study-crossed/
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Coursera课程总结：Minitab中的连续数据Gage R&R研究 本课程由TÜV认证的六西格玛专家Prof. Dr. Murat Mola教授，针对质量工程师、分析师和从业者，教授如何使用Minitab进行符合AIAG标准的连续数据交叉Gage R&R研究。 **核心内容：** * **研究设置与数据采集：** 课程以Smartboard公司的工业案例为基础，使用10个滚珠轴承、3个操作员（Czubak, Gruska, Stahley）以及每个零件和操作员进行2次测量的数据集。数据结构清晰，包含运行顺序、滚珠轴承、操作员和直径（mm）四列，确保数据齐全和一致性。 * **平均值与极差法 (ARM) 分析：** 运用业界标准的ARM方法，评估测量系统的可重复性（Repeatability）和可再现性（Reproducibility）。重点讲解GR & R百分比、分辨力（ndc）的计算，并根据AIAG指南对测量系统进行分类（GR & R < 10% 可接受，10-30% 边缘可接受，> 30% 不可接受）。本案例的GR & R值为8.36%（可接受），ndc值为16（高分辨率）。 * **方差分析 (ANOVA) 方法分析：** 通过双因素方差分析（ANOVA）对同一数据集进行更深入的统计分析，量化零件间变异、操作员变异、操作员与零件交互作用以及可重复性等所有变异来源。利用F分布检验各变异来源的统计显著性，并以0.05为显著性水平解释p值。分析结果显示，零件间变异显著（p < 0.05），而操作员变异（p = 0.241）和交互作用（p = 0.227）均不显著。最终，ANOVA方法也得出了8.36%的GR & R值和16的ndc值。 **课程收益：** 本课程旨在使学员能够从结构化数据收集到深入的统计洞察，自信地判断测量系统是否有效、稳定且适用于生产优化。通过实践操作，学员将掌握评估测量系统能力的实用技能。
Welcome to this expert-level course from the Tabtrainer® Series - your professional platform for certified, data-driven quality training.In this training unit, you will master how to perform a Gage R & R study using continuous data and a crossed design in Minitab, guided by AIAG-compliant methods. Based on a real industrial case from the Smartboard Company, you'll work with a full measurement dataset to evaluate repeatability, reproducibility, and overall system capability.You'll apply both the Average & Range Method (ARM) and the ANOVA method to interpret GRR%, ndc, and operator effects with precision. From the basics of structured data collection to deep statistical insights, this course enables you to confidently judge whether your measurement system is valid, stable, and suitable for use in production optimization.Taught by Prof. Dr. Murat Mola, TÜV-certified Six Sigma expert, awarded Professor of the Year 2023 in Germany, and founder of Tabtrainer®, this course ensures a unique blend of academic excellence and industrial relevance - tailored for quality engineers, analysts, and practitioners seeking real-world competence.Part 1 - Introduction and Data CollectionFocus: Study setup and measurement planThe study uses 10 ball bearings, 3 operators (Czubak, Gruska, Stahley), and 2 measurement repetitions per part and operator.The measurement design is "crossed," meaning each operator measures each part.Data structure: 60 total values, arranged in four columns: Run Order, Ball Bearing, Operator, Diameter (mm).Objective: Ensure completeness and consistency of the data set before beginning the analysis.Key Insight: A well-prepared and structured data set is essential for valid analysis.Part 2 - Gage R & R Using the Average & Range Method (ARM)Focus: Practical evaluation based on industry-standard tablesThe ARM approach calculates repeatability and reproducibility from the averages and ranges of measurement data.Tolerance limits and process variation are taken into account.Calculation of the number of distinct categories (ndc) helps determine measurement resolution.Classification of the system is based on AIAG guidelines:GR & R < 10%: AcceptableGR & R 10-30%: Marginally acceptableGR & R > 30%: UnacceptableResult:GR & R value: 8.36% (acceptable)ndc value: 16 (high resolution)Part 3 - Gage R & R Using the ANOVA MethodFocus: Statistical depth and interaction analysisThe same data set is now analyzed using a two-way analysis of variance (ANOVA).All variation components are quantified: part-to-part, operator, operator × part interaction, and repeatability.The F-distribution is used to test statistical significance of each variation source.p-values are interpreted in relation to a 0.05 significance level.Results:Part-to-part variation: Statistically significant (p < 0.05)Operator variation: Not significant (p = 0.241)Interaction effect: Not significant (p = 0.227)Final GR & R value: 8.36%ndc value: 16
