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00:59Propagation of Uncertainty from Random Error
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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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00:59Uncertainty: Overview
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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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03:37Uncertainty in Measurement: Accuracy and Precision
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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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01:10Propagation of Uncertainty from Systematic Error
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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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01:30Typical Model Studies
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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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01:05Estimation of the Physical Quantities
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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A Practical Approach for Uncertainty Management in Rubber Manufacturing Processes Using Physics-Informed Real-Time
Ismael Viejo1, Salvador Izquierdo1, Ignacio Conde1
1Instituto Tecnológico de Aragón (ITAINNOVA), Calle María de Luna 7, 50018 Zaragoza, Spain.
Polymers
|May 28, 2022
Summary
This study introduces a new nonintrusive method for managing uncertainty in industrial manufacturing. It uses physics-informed models to accurately predict output uncertainties, improving process management.
Area of Science:
- Industrial Engineering
- Manufacturing Systems
- Uncertainty Quantification
Background:
- Effective industrial manufacturing management requires accurate process modeling.
- Incorporating uncertainty propagation is crucial for realistic manufacturing simulations.
- Existing methods may lack efficiency in handling complex uncertainties.
Purpose of the Study:
- To propose a novel nonintrusive methodology for uncertainty management in manufacturing.
- To develop a real-time, physics-informed model for a posteriori uncertainty computation.
- To enable unified uncertainty management across material transformation processes.
Main Methods:
- Utilized tensor factorization as a Model Order Reduction technique.
- Built a deterministic, physics-informed model incorporating material properties, process operations, and uncertainties.
- Applied the method to an automotive door seal co-extrusion process.
Main Results:
- Successfully computed output uncertainties post-hoc using the developed model.
- Identified key sensitivities within the manufacturing process.
- Demonstrated the model's applicability to a complex co-extrusion scenario.
Conclusions:
- The proposed nonintrusive method effectively manages uncertainty in industrial manufacturing.
- Physics-informed models with tensor factorization offer a robust approach to uncertainty quantification.
- This methodology enhances the reliability and predictability of manufacturing processes.