Journal of Quality Engineering and Management

Abstract Today, supply chain networks in a competitive business environment are faced with the occurrence of potential disruptions, uncertain nature of business parameters and constant changes in market demand that affect the efficiency and performance of the network. This research has developed a new stable-feasibility possibility combination for designing a multi-product closed-loop supply chain network under uncertainty conditions to develop a new approach to planning. Mathematics of credit limitation has been used. The above network has been designed with the objectives of maximizing accountability, reliability and cost minimization. . Reliable models based on credit constraint planning and new robust-credit constraint combination were presented and evaluated using real data from a national industrial project. The results show the proposed robust new combination with average cost-effectiveness and minimum standard deviation , Has improved the stability of the model and its effectiveness.

Abstract Redundancy allocation is one of the important approaches to increase reliability used by system designers. In this approach, to improve the reliability of the system, components or subsets (components) are considered in the system, which in case of failure of sensitive components of the system are quickly replaced and intermittently stop the operation of the system.
It is prevented. In this research, the problem of redundancy allocation for a system with one component with defined constraints and considering the cost of lost production is presented. In this article, the goal is to minimize the total cost of the system, which considers two strategies: cold plug-in and inventory. Cold plug-in refers to a situation where the surplus component is not normally under load and the probability of failure before replacing the damaged component is independent of system performance time. The decision variable in this study is the values ​​of the number of redundancy components and the stock of spare components in stock. The difference between the two is that the plug-in component quickly and without delay replaces the defective component in the system, and its presence does not stop during the maintenance of the main component. The mathematical planning model has been developed to achieve the objectives of the problem as a nonlinear complex problem. An example for the system is also given and solved by GAMS optimization software. Finally, the results of solving the model are discussed.

Abstract In many statistical process control applications, the relationship between a response variable and one or more control variables is evaluated by a function called a profile. Profiles are divided into different types according to the nature of the response variable, such as linear and nonlinear profiles. In this research, a new control diagram based on the generalized linear test approach and fractional regression is presented to monitor multiple linear profiles with interactions in phase 2. The simulation results of the proposed control diagram show its much better performance than the control diagram based on the least squares error method.

Abstract Traditional control diagrams are based on the basic assumption that process data are sequentially independent of each other and have a normal distribution. However, in many cases in the real world, including in chemical and continuous processes, this assumption does not exist and there is a kind of autocorrelation between the data collected from the process. The use of traditional control charts in self-correlated processes is unreliable and increases erroneous warnings. One of the methods developed to control autocorrelation processes is to identify the structure of process time series and use residual values ​​to control process. In this paper, a model based on adaptive neural-fuzzy systems to identify time series structure and predict design use. May be. Is in AR (2). Also, the residual control diagrams based on this system for the self-return data of degree 2 and for (EWMA) finally using the simulated data, the efficiency of the proposed method in the moving average diagram are evaluated. Different degrees of correlation are evaluated.

Abstract Equipment is usually damaged by a random pattern based on a gradual deterioration process. In these cases, the level of deterioration gradually increases, and when its value exceeds the predefined decline threshold, it is considered disabled. In addition, environmental disturbance factors such as temperature, humidity, pressure, etc. may experience uncontrollable changes and alter or accelerate failure patterns. Since estimating the remaining equipment life is very important in the effectiveness of forecast maintenance planning and this estimate should be based on identifying accelerated failure patterns, the present study is the first to estimate the remaining equipment life in the presence of effects. The accelerator is focused on two correlated disturbance factors and in it to monitor the environmental factors affecting the life of the equipment from control charts and how to meet the average residual life of the equipment in different conditions under control, out of control due to the first disturbance factor, Being out of control due to the second factor and being out of control due to both factors are presented. A numerical example is also provided to illustrate the details of the calculations.

Abstract The main purpose of this study is to identify the causes of vibration and detectable defects of bearings through sonometry using a multilayer neural network. Neural network is an intelligent method and due to its main properties, ie its high ability to estimate nonlinear functions and adaptive learning, it has been used to troubleshoot mechanical vibrations of machines, ie bearing acoustics and their frequency analysis. To collect the data, a type of healthy ball bearing cone bearing and a similar bearing with defective bullets were used and tested in desktop drills and radial-based drills in 5 different rounds. In this study, according to the network with 10 hidden layers, the signal frequency is considered as the input of the multilayer neural network and finally the bearing defects and its probable cause are determined and corrective measures are proposed.