12 research outputs found
Profile control chart based on maximum entropy
Monitoring a process over time is so important in manufacturing processes to
reduce the wastage of money and time. The purpose of this article is to monitor
profile coefficients instead of a process mean. In this paper, two methods are
proposed for monitoring the intercept and slope of the simple linear profile,
simultaneously. The first one is linear regression, and another one is the
maximum entropy principle. A simulation study is applied to compare the two
methods in terms of the second type of error and average run length. Finally,
two real examples are presented to demonstrate the ability of the proposed
chart
New statistical control limits using maximum copula entropy
Statistical quality control methods are noteworthy to produced standard
production in manufacturing processes. In this regard, there are many classical
manners to control the process. Many of them have a global assumption around
distributions of the process data. They are supposed to be normal, which is
clear that it is not always valid for all processes. Such control charts made
some false decisions that waste funds. So, the main question while working with
multivariate data set is how to find the multivariate distribution of the data
set, which saves the original dependency between variables. Up to our
knowledge, a copula function guarantees the dependence on the result function.
But it is not enough when there is no other functional information about the
statistical society, and we have just a data set. Therefore, we apply the
maximum entropy concept to deal with this situation. In this paper, first of
all, we find out the joint distribution of a data set, which is from a
manufacturing process that needs to be control while running the production
process. Then, we get an elliptical control limit via the maximum copula
entropy. In the final step, we represent a practical example using the stated
method. Average run lengths are calculated for some means and shifts to show
the ability of the maximum copula entropy. In the end, two real data examples
are presented
Inertial capability index based on fuzzy data
Process performance can be analyzed by using process capability indices (PCIs), which are summary statistics to depict the process location and dispersion successfully. In some cases, quality characteristic and target are not precise numbers and they are expressed in fuzzy terms, so that the classical capability indices cannot be applied. In this paper we obtain a confidence interval for inertial capability index Cpi (defined by [Pillet, TQM Mag. 16, 202–209 (2004)]) based on fuzzy data and propose a membership function for it