نشریه مهندسی و مدیریت کیفیت
سال دوازدهم شماره 1 (بهار 1401)

We introduce a method for the statistical design of a depth-based control chart, using the percentile-based approach. The proposed control chart is affine invariant and is asymptotically distribution-free. Generally, the performance of a control chart is evaluated with the average run length metric. The average run length metric has a geometric distribution skewed to the right with a large standard deviation and may not be a proper measure for evaluating the control chart. Therefore, we use the statistical design method of control charts with the PL approach, which is an improvement and development on classical statistical design. By employing constraints on average run length, the length of in-control and out-of-control performances are guaranteed with predetermined probabilities and we can ensure that the in-control run length exceeds the desired value and the out-of-control run length is less than the desired value. Simulation studies show that the proposed control chart is more efficient than the average run length approach.

The two parameter exponential distribution is particularly important among statistical distributions due to its constant failure rate and has applications in the fields of medicine, biology, clinical trials, public health, engineering, economics, demographics, and life span data, and reliability. Due to the importance of life span data, two parameter exponential distribution with censored data has recently attracted the attention of many researchers, but so far the inference about the location parameter with random censored data in the presence of outlier data has not been discussed. In this article, the location and scale parameters of two parameter exponential distribution under random censoring with the presence of k outliers are estimated by Bayesian and classical methods. Due to the importance of the spatial parameter, when censoring the two parameter exponential distribution with the presence of outlier data, the spatial parameter is considered the same but the scale parameter is different. In the Bayesian estimation of parameters, the is checked using Gibbs sampling under the error squared loss function. We recommend used the Bayesian estimation.The generalized variance is given according to the dimensions of the parameters using the maximum likelihood method.

Reliability is one of the most important issues in the engineering design process. When we use a system, we often want to determine the reliability of this system. Clearly, higher reliability systems are more valuable. On the other hand, the reliability of each system depends on the structure and reliability of its components. Therefore, to increase the reliability of the system, the reliability of its components can be improved. In addition, to increase the reliability of the components of a system, it is necessary to consider its costs. This study, by considering a series-parallel system, determines the amount of increase required for the reliability of system components so that the reliability of the whole system is maximized and the cost of this increase does not exceed a predetermined value. The following is a numerical example for reviewing the results. Finally, a summary of the results of the article is given.

The purpose of the current research is to design a structure so that it can be used to investigate the drivers of the appropriate implementation of quality 4.0 in the country's steel industry. In order to carry out this research, ten stimuli were initially identified using research literature. Then, using the interpretative structural modeling technique, these stimuli were structured using the opinions of 13 experts. , in order to fit the obtained structure, the structural equation modeling of the tools related to it, including measurement model fitting, structural and general model fitting, were used. For this purpose, a questionnaire containing 33 questions with a five-point Likert scale was designed and in order to complete it, opinions were sought from 214 managers and employees of the country's steel industry. The findings of the research on the high effectiveness of reward stimuli and control of big data in proper implementation have quality 4.0.

In this research, a hybrid approach based on fuzzy logic and artificial neural network is presented to predict the failure of machines in order to increase productivity. The subject of this research is one of the factories of the automobile industry named Diaco Ide Aria, which operates in the field of automobile parts production. Preventive maintenance requires correct prediction of breakdowns and accidents, equipment and machines so that productivity can be increased by timely and correct maintenance of machines as well as fixing defects and breakdowns. To model the multi-layer perceptron fuzzy-neural network (MLP), first, 100 failures and stops were collected in a period of 15 months and then entered into MATLAB software. The obtained results show that the implementation of fuzzy-neural network and the prediction of machine failure time has reduced the duration and cost of repairs. Therefore, the working time and accessibility of the machines increased and ultimately increased the productivity by 57%, also, the accuracy of the developed neural-fuzzy model was estimated at 94%.

The technique of control charts to monitor process behavior is one of the basic tools of statistical process control. Process changes can be divided into two main categories: common (random) cause, which is a fundamental feature of any process, and cause (definable) deviation, the occurrence of which is an unusual disorder that must be eliminated in order for the process to reverse. The main purpose of using management control charts is to separate these two different sources.Control charts are widely used in the analysis and control of production processes to produce satisfactory, sufficient, reliable and economical quality. Optimizing chart parameters is an important issue for quality engineers to improve processes. In this paper, the economic statistical design of the X ̅ control chart under the Pareto shock model based on the double objective design is presented. Actually by applying restrictions on the first type of error, the cost as the economic objective and the second type of error as the statistical objective is considered and then the optimal solutions are selected based on the Pareto front. Finally, through a practical example, the advantages of the proposed approach are shown by preparing a list of optimal solutions and graphical representations.