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A variety of quantitative specifications, usually in terms of numerical limits, have been developed in industry for the description, prediction and control of product quality. As the theoretical foundations of these specifications are often beyond the working knowledge of many manufacturing engineers and managers, this article gives a non‐mathematical account of some of the limits commonly encountered in discussions related to product design, manufacture and inspection; emphasis is placed on the distinctions in their intended purposes and methods of application.

THE first part of this article, published in last month's issue of AIRCRAFT ENGINEERING, laid stress on the importance of the technique of quality control to modern production engineering processes, and discussed the basic principles of this technique. The present issue describes the practical applications of quality control and the use of control charts.

Presents an approach to determine the optimum control limits of the x‐bar chart for skewed process distributions. The approach takes both the control limits of the x‐bar chart and the specification limits of x into consideration, and relates the out‐of‐control status directly with the nonconforming products. The proposed approach may be applied to industries to reduce the average number of scrap products, without increasing the type I error in statistical process control (SPC).

The purpose of this paper is to demonstrate the use of artificial intelligence methods in quality control and improvement. The paper introduces a systematic approach for the design of fuzzy control charts of tip shear carpets.

There are certain steps for designing fuzzy control charts. All input, state and output variables of the carpet plant and partition of the universe of discourse were first determined. The interval spanned by each variable and the number of fuzzy subsets each assigned with a linguistic label were identified. Then, the adaptive capability of neural network was used to determine the membership functions for each fuzzy subset. The fuzzy relationship functions between the inputs and outputs were assigned to form the fuzzy rule base (controller) in order to normalize the variables and certain intervals. Fuzzification of input parameters and max‐min composition of rules for inferring crisp outputs was the next step. The aggregation of fuzzified outputs and defuzzification of the outputs were the last step of this study, which helped to produce crisp outputs for latex weight.

Fuzzy linguistic terms were employed for overall quality assessment and rating of the end product. The outcomes of neuro‐fuzzy system were good supplements to other statistical process control tools.

Lack of qualified domain experts, knowledge acquisition of process parameters and time limitation for training of neuro‐fuzzy model were primary limitations.

The approach is more flexible and meaningful to identify the quality distribution of a product. The qualitative aspect of human reasoning for decision making was employed in this approach.

The paper is original and the first such work for local industry.

Control charts based on geometric distribution have shown to be useful when this is a better approximation of the underlying distribution than the Poisson distribution. The traditional c‐chart, if used, will cause too many false alarms. It is noted that for geometric distribution, the control limits are based on k times standard deviation which has been used previously, will cause a frequent false alarm, and cannot derive any reasonable lower control limits. Studies the use of probability limits to resolve these problems. Also discusses the use of geometric distribution for process control of high‐yield processes.

During the complicated production process in integrated circuit (IC) fabrication, various types of defects on a wafer surface are unavoidable. As the wafer size increases, the clustering phenomenon of the defects becomes increasingly apparent. To upgrade IC products’ yield and reliability, statistical process control is feasible for tracking a manufacturing process. However, the clustered defects frequently cause many false alarms when the standard control charts for defects are used. In this study, we present a method for constructing defect control charts in processes that yield clustered defects. A case study is also evaluated, indicating that the proposed method produces satisfactory control charts for the defect data in IC fabrication.

The purpose of this paper is to implement statistical process control (SPC) in service quality using three-level SERVQUAL, quality function deployment (QFD) and internal measure.

The SERVQUAL questionnaire is developed according to internal services of train. Also, it is verified by reliability scale and factor analysis. QFD method is employed for translating SERVQUAL dimensions’ importance weights which are derived from Analytic Hierarchy Process into internal measures. Furthermore, the limits of the Zone of Tolerance are used to determine service quality specification limits based on normal distribution characteristics. Control charts and process capability indices are used to control service processes.

SPC is used for service quality through a structured framework. Also, an adapted SERVQUAL questionnaire is created for measuring quality of train’s internal services. In the case study, it is shown that reliability is the most important dimension in internal services of train for the passengers. Also, the service process is not capable to perform in acceptable level.

The proposed algorithm is practically applied to control the quality of a train’s services. Internal measure is improved for continuous data collection and process monitoring. Also, it provides an opportunity to apply SPC on intangible attributes of the services. In the other word, SPC is used to control the qualitative specifications of the service processes which have been measured by SERVQUAL.

Since SPC is usually used for manufacturing processes, this paper develops a model to use SPC in services in presence of qualitative criteria. To reach this goal, this model combines SERVQUAL, QFD, normal probability distribution, control charts, and process capability. In addition, it is a novel research on internal services of train with regard to service quality evaluation and process control.

Studies the necessity of controlling the variation of the skewness of the process distribution in order to reduce the product scrap. Proposes a γ control chart for detecting the skewness shift, also implements a simulation procedure to decide the control limits of the γ chart.

This paper proposes a simplified procedure to approximate optimal values for the sample size, control limits, and sampling interval of a control chart based quality control station. The procedure considers the specifications in evaluating the control limits, permits asymmetry in these limits and accounts for the cost structure of the control process. The proposed procedure is compared with the optimal approach and with the current approach used by the company from which production information was obtained. This information was used to generate simulated data on which the comparisons are based.

The purpose of this paper is to develop an innovative and quite new Six Sigma quality control (SSQC) chart for the benefit of Six Sigma practitioners. A step-by-step procedure for the construction of the chart is also given.

Under the assumption of normality, in this paper, the construction of SSQC chart is proposed in which the population mean and standard deviation are drawn from the process specification from the perspective of Six Sigma quality (SSQ). In this chart, the concept of target range is used to restrict the shift in the process within plus or minus 1.5 times of standard deviation. This control chart is useful in monitoring the process to ensure that the process is well maintained within the specification limits with minimum variation (shift).

A step-by-step procedure is given for the construction of the proposed SSQC chart. It can be easily understood and its application is also simple for Six Sigma practitioners. The proposed chart suggests for timely improvements in process mean and variation. The illustrative example shows the improved performance of the proposed new procedure.

The proposed approach assumes a normal population described by the known specification of the process/product characteristics though it may not be in all cases. This may call for a thorough study of the population before applying the chart.

The proposed SSQC chart is an innovative approach and is quite new for the practitioners. The paper assumes that the population standard deviation is known and is drawn from the specification of the process/product characteristics. The proposed chart helps in fine-tuning the process mean and bringing the process standard deviation to the satisfactory level from the perspective of SSQ.

The paper is the first of its kind. It is innovative and quite new to the Six Sigma practitioners who will find its application interesting.