Journal of Quality Engineering and Management

Abstract The measurement process is usually accompanied by error. Measurement error creates a discrepancy between the true value and the observed value of a quality characteristic, affecting the performance of control charts and reducing their ability to detect process changes. In some processes, a quality characteristic is expressed as a function, which is referred to as a profile. Over the past decade, profiles and methods for monitoring them have attracted considerable attention from statistical quality control researchers. Although the impact of measurement error on various control charts and their parameters has been investigated, there is limited information regarding its effect on profiles and their monitoring methods. In this study, the effect of measurement error on the response variable of linear profiles in Phase II is examined. Simulation results indicate that measurement error also affects the performance of profile monitoring methods, reducing the power of techniques such as EWMA-R and EWMA-3 in detecting process changes. Furthermore, this study proposes a new approach for selecting an appropriate measuring device based on profile monitoring methods in the presence of measurement error.

Abstract Nowadays, competition in various industries focuses on reducing production waste and improving the quality of manufactured products. In this article, the improvement of surface roughness quality of AISI 4340 workpieces in the turning process using TiN-coated carbide tools is investigated. For this purpose, the parameters affecting the surface quality, namely cutting speed, feed rate, and depth of cut, were selected as the input factors for the experiments. Then, the degrees of freedom of the system and the required number of levels were determined, and the corresponding orthogonal arrays were calculated. Accordingly, twenty-seven experiments were designed to measure the surface roughness values, and for each experiment, three measurements of the surface roughness parameter were recorded. Next, by calculating the grey ratios, grey coefficients, and grey grades using the relevant formulas, the final grey relational graphs were plotted for all three levels of the experiments. Based on the grey relational diagram, the percentage influence of each input parameter on achieving the desired surface roughness was determined, and the optimal numerical values for these parameters were identified. A comparison of the grey analysis results with the actual experimental data confirms the accuracy and capability of this method in predicting surface roughness in the turning process.

Abstract The subject of time-dependent quality indices—whose values vary over time—has attracted significant attention from quality researchers in recent years. Various methods have been developed for this purpose, among which one of the most recent approaches involves analyzing performance profiles over time. Moreover, as competition among manufactured products intensifies, product design has become a primary focus within the product life cycle for manufacturing organizations. One of the most widely used approaches in this context is robust design. The application of response surface methodology and its related optimization tools has also gained great importance in design studies. In this article, a utility function–based method is presented for the robust design optimization of time-dependent quality indices. Based on the results obtained from a pharmaceutical case study, the proposed method performs significantly better than the two existing methods.

Abstract In image monitoring, the information contained in an image is evaluated using control charts. A spatial control chart is a type of control chart in which the horizontal axis represents the position within the image. These charts are used to detect abnormal points in an image. Thermovision is a branch of machine vision that deals with the analysis of infrared images. Although infrared cameras have long been used in preventive maintenance to identify faulty equipment, overloads, and loose connections, the images captured by these cameras are usually analyzed only through empirical methods, and the few quantitative studies conducted in this area have not utilized control charts. In identifying defects in power distribution panels, several challenges must be considered, including the variety of equipment used in electrical panels, the lack of sufficient data to train pattern recognition models, autocorrelation, and the complex behavior of heat transfer by radiation, convection, and conduction. The insufficient data for training pattern recognition models such as neural networks makes spatial control charts relatively more advantageous than these methods. In this study, a combination of spatial control charts and robust regression is employed to detect defects in power distribution panels, and the detection capabilities of various control charts for identifying these defects are compared.

Abstract Many researchers have shown that adaptive control charts perform more efficiently than fixed-parameter charts in detecting process shifts. The adaptive charts proposed in previous studies are generally based on the assumption that the observations follow a normal distribution. However, in many real-world processes, the distribution of observations is non-normal or, in most cases, unknown. The control chart proposed in this article is a sign control chart with variable sample size for monitoring the process median. It has two key advantages: first, it does not require the assumption of normality for the observations, and second, it is adaptive, which allows it to detect shifts in the process median more quickly than fixed-parameter charts. The performance of this chart is evaluated based on the Average Sample Number (ASN) until an alarm is triggered, which is obtained using the properties of a Markov chain.