Round Robin Noise Measurement System Analysis Using Light Duty Diesel Engine

2015 ◽  
Author(s):  
Dhanesh Purekar
Keyword(s):  
Diesel Engine ◽  
Measurement System ◽  
System Analysis ◽  
Round Robin ◽  
Noise Measurement ◽  
Light Duty ◽  
Measurement System Analysis

Measurement system analysis on the external standard using an azeotropic mixture of ethylene glycol and detailed uncertainties

2021 ◽  
Author(s):  
Ahmad Syafiq Ahmad Hazmi ◽  
Zulina Abd Maurad ◽  
Mohd Azmil Mohd Noor ◽  
Nik Siti Mariam Nek Mat Din ◽  
Zainab Idris
Keyword(s):  
Ethylene Glycol ◽  
Measurement System ◽  
System Analysis ◽  
Azeotropic Mixture ◽  
Measurement System Analysis ◽  
External Standard

Effects of Within Part Variation on Measurement System Analysis

2012 ◽  
Author(s):  
Chittaranjan Sahay ◽  
Suhash Ghosh ◽  
Syed Mohammed Haja Mohideen
Keyword(s):  
Detailed Analysis ◽  
Measurement System ◽  
System Analysis ◽  
Primary Reason ◽  
Improved Method ◽  
System Error ◽  
Measurement System Analysis ◽  
Sources Of Variation ◽  
System Part ◽  
Part Variation

Inherent variation of the measurement system, part-to-part variation and variation arising due to the operator are considered to be the most common sources of variation in a measurement system analysis (MSA). Often errors due to within part variation are overlooked, or even worse, are assumed to be from the inherent variation of the measurement system. Understanding the sources of variation in a measurement system is important for all measurement applications. It becomes even more critical when the part used to evaluate a gage has a significant within part variation. This is an important source of measurement system error that the current procedures followed for MSA studies do not clearly or adequately address. The primary reason for this is a lack of awareness, and there are no clear guidelines on conducting a MSA study under these circumstances. A detailed analysis of the effects of within part variation on MSA is described in this paper. An improved method for conducting the MSA under these circumstances is also presented. This improved and more effective MSA takes all sources of variations into consideration.

scholarly journals Measurement System Analysis for Binary Inspection: Continuous Versus Dichotomous Measurands

2011 ◽  
Vol 43 (2) ◽  
pp. 99-112 ◽  
Author(s):  
Jeroen De Mast ◽  
Tashi P. Erdmann ◽  
Wessel N. Van Wieringen
Keyword(s):  
Measurement System ◽  
System Analysis ◽  
Measurement System Analysis

Approaches for Measurement System Analysis Considering Randomness and Fuzziness

2020 ◽  
Vol 9 (2) ◽  
pp. 98-131
Author(s):  
Liang-Hsuan Chen ◽  
Chia-Jung Chang
Keyword(s):  
Measurement System ◽  
System Analysis ◽  
Sum Of Squares ◽  
Measurement System Analysis ◽  
Repeatability And Reproducibility ◽  
Packaging Industry ◽  
Relationship Of ◽  
Semiconductor Packaging ◽  
The Relationship ◽  
Fuzzy Random

For some quality inspection practices, subjective judgements based on the inspectors' experience and knowledge, such as visual inspection, may be required for some particular quality characteristics. This kind of measurement system, including its associated randomness and fuzziness, should be assessed by Measurement system analysis (MSA) before its application. For such purpose, this article represents observations with randomness and fuzziness from MSAs as fuzzy random variables, and then two pairs of descriptive parameters, i.e., expected value and variance, are derived. Then, the relationship of the total sum of squares of factors is proven to hold, so that fuzzy analysis of variance (FANOVA) in terms of gauge repeatability and reproducibility can be developed. The proposed approach has the advantage that FANOVA is developed based on the relationship of the total sum of squares of factors, considering randomness and fuzziness. A real case in the semiconductor packaging industry is used to demonstrate the applicability of the proposed approaches to MSA.

scholarly journals Leveraging Measurement System Analysis (MSA) to Improve Library Assessment: The Attribute Gage R&R

2009 ◽  
Vol 70 (6) ◽  
pp. 568-577 ◽  
Author(s):  
Sarah Anne Murphy ◽  
Sherry Engle Moeller ◽  
Jessica R. Page ◽  
Judith Cerqua ◽  
Mark Boarman
Keyword(s):  
Measurement System ◽  
System Analysis ◽  
Ohio State University ◽  
Assessment System ◽  
State University ◽  
Measurement Systems ◽  
Library Assessment ◽  
Measurement System Analysis ◽  
Ideal Tool ◽  
The Ohio State University

Measurement System Analysis (MSA) provides decision makers with a useful suite of tools for understanding whether variation should be attributed to an assessment system itself or the actual item or program being assessed. This paper introduces the Attribute Gage R&R, using a study of The Ohio State University Libraries’ mechanism for measuring quality in e-mail reference transactions as an example. An ideal tool for examining assessment programs that require subjective interpretation, the Attribute Gage R&R can assist library organizations in understanding their processes and validating the utility of data collected through their measurement systems.

scholarly journals Measurement System Analyses – Gauge Repeatability and Reproducibility Methods

2018 ◽  
Vol 18 (1) ◽  
pp. 20-27 ◽  
Author(s):  
Lenka Cepova ◽  
Andrea Kovacikova ◽  
Robert Cep ◽  
Pavel Klaput ◽  
Ondrej Mizera
Keyword(s):  
Measurement System ◽  
Automotive Industry ◽  
Manufacturing Industry ◽  
System Analysis ◽  
Advantages And Disadvantages ◽  
Measurement System Analysis ◽  
System Analysis Approach ◽  
Repeatability And Reproducibility ◽  
Plastic Parts ◽  
Part Variation

Abstract The submitted article focuses on a detailed explanation of the average and range method (Automotive Industry Action Group, Measurement System Analysis approach) and of the honest Gauge Repeatability and Reproducibility method (Evaluating the Measurement Process approach). The measured data (thickness of plastic parts) were evaluated by both methods and their results were compared on the basis of numerical evaluation. Both methods were additionally compared and their advantages and disadvantages were discussed. One difference between both methods is the calculation of variation components. The AIAG method calculates the variation components based on standard deviation (then a sum of variation components does not give 100 %) and the honest GRR study calculates the variation components based on variance, where the sum of all variation components (part to part variation, EV & AV) gives the total variation of 100 %. Acceptance of both methods among the professional society, future use, and acceptance by manufacturing industry were also discussed. Nowadays, the AIAG is the leading method in the industry.

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