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The objective functions are formulated for minimization of power consumption in the rolling process, variances between ideal dimensions and design dimensions, as well as variances between the lengths of contact arcs.
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One cause changes the process mean and the other changes the process variance.
Of particular interest is the estimation of the mean response and process variance.
The Shewhart R control chart (R chart) has been widely used to monitor process variance.
Based on the analysis, recommendations for minimizing process variance in the resistance spot welding process are provided.
Under this assumption, the sample mean and variance are often used to estimate the process mean and the process variance at each design point.
Incorrect adjustment of a process may result in shifts in process mean, process variance, or both, ultimately affecting the quality of products.
The distribution of reactor states was studied as a function of input process variance and it was found that the distribution can become bimodal even when the associated deterministic steady state is unique.
By using the direct Liapunov method almost sure asymptotic stability and uniform stochastic stability conditions are obtained as the function of stochastic process variance, damping coefficient, and geometric and physical parameters of the beam.
Although these charts are reasonably able to detect most shifts in the process mean, they may be unable to accurately detect other process faults, such as shifts in the process variance.
For example, when nonconformities of wafers have clustering phenomenon in semiconductor production process, the process control based on Poisson distribution always underestimates the true average nonconformities and process variance.
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