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where T is the average values of painting defects at different levels.
The predicted range of optimum painting defects is 0.09 < 1.11 < 2.13.
The percentage of painting defects and washing defects obtained by Taguchi method are 0.83 and 1, respectively.
In Table 7, it is clear that the parameters A, C, and D significantly affect both mean and variation in the painting defects.
The percent contribution of each parameter to the variation of painting defects and optimum parameter (under economic condition) is shown in Table 8.
The optimum levels of various parameters for minimum painting defects of shock absorber obtained by Taguchi method and genetic algorithm are shown in Table 9.
Similar(51)
Corrosion propagation around the painting defect was better held by amorphous Zn Fe P due to an improved adhesion between painting and alloy layers.
Since we consider randomly occurred paint defects, the problem is modelled by stochastic programming.
We consider the problem in an automotive manufacturing setting in which the cars are inspected for paint defects after paint operations.
However, it is not possible to know the sequence of vehicles that leave paint shop in advance since randomly occurred paint defects mixes the sequence unintentionally.
The model considers unintentional sequence alteration due to paint defects and the need of instant decision-making of the assembly line.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com