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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Uncertainty in Measurement: Accuracy and Precision03:37

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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Uncertainty Evaluation in Multistage Assembly Process Based on Enhanced OOPN.

Yubing Huang1, Wei Dai1, Weiping Mou2

  • 1School of Reliability and System Engineering, Beihang University, Beijing 100000, China.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an uncertainty evaluation model for multistage assembly processes, identifying vulnerable spots using defect streams and entropy. The developed model helps control fluctuations and improve overall product assembly quality.

Keywords:
Shannon entropyassembly processdefect streamobject-oriented petri netuncertainty evaluation

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Area of Science:

  • Manufacturing Engineering
  • Industrial Engineering
  • Systems Engineering

Background:

  • Multistage assembly processes are prone to defects, impacting product quality.
  • Quantifying uncertainty in assembly is crucial for process improvement.
  • Existing methods may not fully capture the dynamic nature of defect streams.

Purpose of the Study:

  • To develop an uncertainty evaluation model for multistage assembly processes.
  • To identify and control vulnerable spots within the assembly line.
  • To enhance product assembly quality through a novel uncertainty assessment approach.

Main Methods:

  • Object-Oriented Petri Nets (OOPN) were adapted to model the assembly process.
  • A fitted defect changing function replaced the standard transition function in the OOPN.
  • Entropy from physics was applied to quantify process uncertainty, using semi-Markov chains.
  • Steady-state probability analysis was combined with uncertainty evaluation.

Main Results:

  • An OOPN-based uncertainty evaluation model was successfully developed.
  • Vulnerable spots in the assembly process were identified through defect stream analysis.
  • A scanning test program was proposed to mitigate identified vulnerabilities.
  • The model demonstrated effectiveness in analyzing assembly structure and process variables.

Conclusions:

  • The developed uncertainty evaluation model provides a robust method for assessing assembly processes.
  • Identifying vulnerable spots and controlling fluctuations leads to improved product assembly quality.
  • The application of entropy and OOPN offers a novel perspective on manufacturing process analysis.