International Journal of Industrial Engineering & Production Research

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Human-based quality control reduces the accuracy of this process. Also, the speed of decision making in some industries is very important. For removing these limitations in human-based quality control, in this paper, the design of an expert system for automatic and intelligent quality control is investigated. In fact, using an intelligent system, the accuracy in quality control is increased. It requires the knowledge of experts in quality control and design of expert systems based on the knowledge and information provided by human and equipment. For this purpose, Fuzzy Inference System (FIS) and Image Processing approach are integrated. In this expert system, the input information is the images of the products and the results of processing on images for quality control are as output. At first, they may be noisy images; the pre-processing is done and then a fuzzy system is used to be processed. In this fuzzy system, according to the images, the rules are designed to extract the specific features that are required. At second, after the required attributes are extracted, the control chart is used in terms of quality. Furthermore, the empirical case study of copper rods industry is presented to show the abilities of the proposed approach.
 

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The present research aims at investigating feasible improvements by determining optimal number of stations and required workforce using a simulation system, with the ultimate goal of reaching optimal throughput while respecting the problem constraints in an attempt to achieve maximum feasible performance in terms of production rate. For this purpose, similar research works were investigated by reviewing the relevant pieces of the literature on simulation model, car signoff station, and techniques for optimizing the station, and the model of the car signoff unit was designed using data gathering tools, existing documents, and actual observations. Subsequently, the model was validated by means of descriptive statistics and analysis of variance (ANOVA). Furthermore, available data was analyzed using ARENA and SPSS software tools. In a next step, potential improvements of the unit were identified and the model was evaluated accordingly. The results indicated that some 80% of the existing problems could be addressed by appropriately planning for human resources, on-time provision of the required material at reworking units, and minimization of transportation at the stations that contributed the most to the working queue. Thus, the waiting time per station could be minimized while increasing the production rate.

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This work used two methods for Monitoring and control of autocorrelated processes based on time series modeling. The first method was the simultaneous monitoring of common and assignable causes. This method included applying five steps of data gathering, normality test, autocorrelation test, model selection and control chart selection on all non-stationary process observations. The second method was a novel one for the separate monitoring and control of common and assignable causes. In this method, the process was divided into the parts with and without assignable causes.
The first method was greatly non-stationary due to not separating common and assignable causes. This method also implied that the common causes were hidden in the process. The novel method for the separate monitoring of common and assignable causes could turn the process into a stationary one, leading to identifying, monitoring, and controlling common causes without any interference from the assignable causes. The results showed that, unlike the first method, the second method could be very sensitive to the common causes; it could, therefore, suitably monitor, identify and control both assignable and common causes.
The current work was aimed to use control charts to monitor and control the bootomhole pressure during the drilling operation.
 

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In this paper, a multivariable control of a two-link robot is performed by fuzzy-sliding mode control. Robots on the one hand have complex dynamics due to nonlinearity, uncertainty and indeterminacy resulting from friction and other factors. The uncertainty and nonlinearity of the governing equations more and more necessitates the use of these two types of controllers in spite of a two-link and multivariable dynamic system. In this paper simulation, a fuzzy system is used in two parts. In the first part, a fuzzy system is used to approximate the uncertainty of the robot arm dynamic model in the control law and in the second part the nonlinear term of the signal function is replaced by an adaptive neuro-fuzzy controller to produce appropriate s and to track the output properly. The comparison of simulation results suggests that the intelligent method based on the proposed adaptive neuro-fuzzy control has better performance in tracking reference signal with slight tracking error and higher accuracy compared to sliding mode method.

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One of the goals of the manufacturing industry in the globalisation era is to reduce defects. Due to a variety of factors, the products manufactured in the industry may not be defect-free. Six Sigma is one of the most effective methods for reducing defects. This paper focuses on implementing Six Sigma in the automobile industry's stator motor shaft assembly. The high decibel noise produced by the stator motor is regarded as a rejected piece. Six Sigma focuses on continuous improvement and aids in process optimization by identifying the source of the defect. In the Six Sigma process, the problem is measured and analysed using various tools and techniques. Before beginning this case study, its impact on the company in terms of internal and external customer cost savings is assessed. This case study was discovered to be in a high-impact area. The issue was discovered during the Core and Shaft pressing process. Further research leads to dimensional tolerance, which reduces the defect percentage from 16.5 percent to 0.5 percent.

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In recent years, it has been proven that integrating statistical process control, maintenance policy, and production can bring more benefits for the entire production systems. In the literature of triple-concept integrated models, it has generally been assumed that the observations are independent. However, the existence of correlated structures in some practical applications put the traditional control charts in trouble. The mixed EWMA-CUSUM (MEC) control chart and the ARMA control chart are effective tools to monitor the mean of autocorrelated processes. This paper proposes an integrated model subject to some constraints for determining the decision variables of triple concepts in the presence of autocorrelated data. Three types of autocorrelated processes are investigated to study their effects on the results. Moreover, the results of the MEC and ARMA charts are compared. Due to the complexity of the model, a particle swarm optimization (PSO) algorithm is applied to select optimal decision variables. An industrial example and extensive comparisons are provided

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The article highlights the development of a Non-Gaussian Process Monitoring Strategy for a Steel Billet Manufacturing Unit (SBMU). The non-Gaussian monitoring strategy being proposed is based on Modified Independent Component Analysis (ICA) which is a variant of the widely employed conventional ICA. The Independent Components(IC) being extracted by modified ICA technique are ordered as per the variance explained akin to that of Principle Component Analysis (PCA). Whereas in conventional ICA the variance explained by the ICs are not known and thereby causes hindrance in the selection of influential ICs for eventual building of the nominal model for the ensuing monitoring strategy. Hotelling T² control chart based on modified ICA scores was used for detection of fault(s) whose control limit was estimated via Bootstrap procedure owing to the non-Gaussian distribution of the underlying data. The Diagnosis of the Detected Fault(s) was carried out by employment of Fault Diagnostic Statistic. The Diagnosis of the Fault(s) involved determination of the contribution of the responsible Process and Feedstock characteristics. The non-Gaussian strategy thus devised was able to correctly detect and satisfactory diagnose the detected fault(s)

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This paper aims to introduce a joint optimization approach for maintenance, quality, and buffer stock policies in single machine production systems based on a P control chart. The main idea is to find the optimal values of the preventive maintenance period, the buffer stock size, the sample size, the sampling interval, and the control limits simultaneously, such that the expected total cost per time unit is minimized. In the considered system, we have a fixed rate of production and stochastic machine breakdowns which directly affect the quality of the product. Periodic preventive maintenance (PM) is performed to reduce out-of-control states. In addition, corrective maintenance is required after finding each out-of-control state. A buffer is used to reduce production disturbances caused by machine stops. To ensure that demand is met during a preventive and corrective maintenance operation. All features of three sub-optimization problems including maintenance, quality control, and buffer stock policies are formulated and the proposed integrated approach is defined and modeled mathematically. In addition, an iterative numerical optimization procedure is developed to provide the optimal values for the decision variables. The proposed procedure provides the optimal values of the preventive maintenance period, the buffer stock size, the sample size, the sampling interval, and the control chart limits simultaneously, in a way that the total cost per time unit is minimized. Moreover, some sensitivity analyses are carried out to identify the key effective parameters.

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