International Journal of Industrial Engineering & Production Research

---

 In this research, a new control policy for the acceptance sampling problem is introduced. Decision is made based on the number of defectives items in an inspected batch. The objective of the model is to find a constant control level that minimizes the total costs, including the cost of rejecting the batch, the cost of inspection and the cost of defective items. The optimization is performed by approximating the negative binomial distribution with Poisson distribution and using the properties of binomial distribution. A solution method along with numerical demonstration on the application of the proposed methodology is presented. Furthermore, the results of sensitivity analysis show that the proposed method needs a large sample size .

---

In this research, the decision on belief (DOB) approach was employed to analyze and classify the states of uni-variate quality control systems. The concept of DOB and its application in decision making problems were introduced, and then a methodology for modeling a statistical quality control problem by DOB approach was discussed. For this iterative approach, the belief for a system being out-of-control was updated by taking new observations on a given quality characteristic. This can be performed by using Bayesian rule and prior beliefs. If the beliefs are more than a specific threshold, then the system will be classified as an out-of-control condition. Finally, a numerical example and simulation study were provided for evaluating the performance of the proposed method.

---

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.
 

---

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.