Measurement system analysis using designed experiments with minimum α–β Risks and n

Senol, Sansli

doi:10.1016/j.measurement.2004.05.001

Cited by 26 publications

(14 citation statements)

References 14 publications

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“…Measurements are collected from a crossed design: each of a certain number of parts measured several times by a number of operators. GR&R study provides a technique for estimating two independent components of measurement variance: GR&R. Senol 3 defines repeatability ( 2 repeatability ) as the variance of the measurements obtained while measuring the characteristics of a measurement device. However, reproducibility ( 2 reproducibility ) is the variance of the medium of the measurements obtained by different operators in measuring the same characteristics of the same piece with the same measuring device.…”

Section: Introductionmentioning

confidence: 99%

Improving wooden parts' quality by adopting DMAIC procedure

Li¹,

Al–Refaie²

2008

Quality & Reliability Eng

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This paper utilizes the six-sigma define-measure-analyze-improve-control (DMAIC) procedure to improve quality through enhancing the measurement system capability (MSC) of a wood industry. In the first stage, the major reported defects at the customer site are identified, and then the performance of the measurement system is evaluated by conducting a gage repeatability and reproducibility (GR&R) experiment on a drilling process, i.e. the outputs of a drilling machine. The obtained results show that the measurement system is inadequate and results in wrong classification of the produced wooden parts. Thus, to improve MSC, corrective actions, including operators' training, proper selection of measuring instruments, and improved measuring methods, were implemented. To determine the achieved improvement in MSC, a second GR&R study was carried out under the same conditions. In conclusion, adopting the DMAIC procedure including GR&R study turns out to be an effective method in improving the quality system involving measurements.

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Section: Introductionmentioning

confidence: 99%

Improving wooden parts' quality by adopting DMAIC procedure

Li¹,

Al–Refaie²

2008

Quality & Reliability Eng

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“…The research implies that the average value during the process, the accuracy in relation to the specification and the probability distribution of the measuring system error can affect the risks of error acceptance or rejection. Senol [25] considered the risk involved for the manufacturers and the consumers and, while minimizing the number of samples required, applied experimental designs to find the optimal configuration for influencing factors for measurement system analysis, in order to improve measurement accuracy. Aguilar et al [26] believed that the most important statistical property for 3D measurement devices was the fact that small bias and variation can produce measurement results that are close to traceable standard values.…”

Section: Position Variability In Measurement Systemsmentioning

confidence: 99%

Analysis on Accuracy of Bias, Linearity and Stability of Measurement System in Ball screw Processes by Simulation

2015

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Abstract:To consistently produce high quality products, a quality management system, such as the ISO9001, 2000 or TS 16949 must be practically implemented. One core instrument of the TS16949 MSA (Measurement System Analysis) is to rank the capability of a measurement system and ensure the quality characteristics of the product would likely be transformed through the whole manufacturing process. It is important to reduce the risk of Type I errors (acceptable goods are misjudged as defective parts) and Type II errors (defective parts are misjudged as good parts). An ideal measuring system would have the statistical characteristic of zero error, but such a system could hardly exist. Hence, to maintain better control of the variance that might occur in the manufacturing process, MSA is necessary for better quality control. Ball screws, which are a key component in precision machines, have significant attributes with respect to positioning and transmitting. Failures of lead accuracy and axial-gap of a ball screw can cause negative and expensive effects in machine positioning accuracy. Consequently, a functional measurement system can incur great savings by detecting Type I and Type II errors. If the measurement system fails with respect to specification of the product, it will likely misjudge Type I and Type II errors. Inspectors normally follow the MSA regulations for accuracy measurement, but the choice of measuring system does not merely depend on some simple indices. In this paper, we examine the stability of a measuring system by using a Monte Carlo simulation to establish bias, linearity variance of the normal distribution, and the probability density function. Further, we forecast the possible area distribution in the real case. After the simulation, the OPEN ACCESS Sustainability 2015, 7 15465 measurement capability will be improved, which helps the user classify the measurement system and establish measurement regulations for better performance and monitoring of the precision of the ball screw.

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“…Also presented in Burdick et al [9] are the measures that will be presented later in the present paper. Senol [10] minimized the false failures, missed faults and sample size with a statistically evaluated MSA method using designed experiments. The current GR&R technique has succeeded in defining the root causes of measurement inconsistencies and in improving the productivity of a manufacturing process in different industries [11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%