This article summarizes experimental, theoretical, and computational assessments performed to understand the effect of filling and suck-back cycle factors on fluid behaviors that increase the ...propensity for filling needle clogging. Product drying under ambient conditions decreased considerably when the liquid front was altered from a droplet or meniscus at the needle tip to a point approximately 5 mm inside the needle. Minimizing the variation in size of product droplet formed after the fill cycle is critical to achieve a uniform meniscus height after the suck-back cycle. Several factors were found to contribute to droplet size variability, including filling and suck-back pump speed, suck-back volume, and product temperature. Filling trials and the computational fluid dynamics simulations showed that product meniscus stability during the suck-back cycle can be improved by reducing the suck-back flow rate. The computational fluid dynamics simulations also showed that a decrease in contact angle had the greatest effect in reducing meniscus stability. As the number of filling line stoppages increases, the product buildup at the needle increases. The interaction between stoppages and the number of dispenses between stoppages was established to minimize product buildup at the filling needle. Improved suck-back control was shown to improve process capability of large-scale batches.
When a process is statistically under control, one may be interested in assessing the process performance based on the specification limits provided by the customer. This evaluation is referred to as ...process capability analysis. Manufacturing operations are often involved with multistage processes, in which the output of a stage is the input of its subsequent stage. This property is known as the cascade property. Existing methods in capability analysis studies are not applicable when a process or product is represented by profiles. This study presents a method to conduct process capability analysis in a multistage process when quality of a product or process is characterized by a simple linear profile. The performance of the proposed method for a two-stage process is evaluated by numerical simulation using an example from the literature. The results indicate that the proposed method eliminates the effect of the cascade property for different shift sizes and autocorrelations.
We consider the process capability index Cpmc when a tolerance cost function is introduced. It is well known that Cpmc performs well under the general assumption that the data is not contaminated. ...Under this assumption, the standard sample mean and sample variance are used to estimate Cpmc. However, it is also well known that this estimate is extremely sensitive to data contamination since the sample mean and sample variance have a zero breakdown point. This in turn makes any constructed confidence interval (CI) also very sensitive to data contamination. In this paper, we develop robust estimators of the process capability index along with robust CIs. We compare the performance of the proposed estimators of Cpmc using the notion of statistical power and receiver operating characteristic curves. Finally, we investigate the use of bootstrapping approaches for improving power of associated hypothesis tests. The results clearly indicate that, when data contamination exists, the methods together with bootstrapping outperform the conventional method.
•A cost-based process capability index is proposed in this study.•Two robustified versions of the PCI are developed: with the median and MAD pair and with the HL and Shamos pair.•Robust confidence intervals for the PCI are constructed employing the delta method.•The performance of the proposed method is compared using power and receiver operating characteristic (ROC) curves.•Improved confidence intervals are presented using standard and percentile bootstrap confidence interval methods.
•We propose a robust process capability indices (PCIs) which can be used in non-normal distribution.•According to the results of the PCIs, a possible strategy to improve the process is ...provided.•Robust point estimation and interval estimation of PCIs are provided.•Two hypothesis testingof PCIs are proposed.•The relationship between the number of non-conforming parts per million and PCIs are provided.
Process capability index (PCI) is a common and simple way to evaluate the process capability in the field of quality control. So far, most existing PCIs have been studied based on using the normality assumption, while only few PCIs have been proposed for specified non-normal distributions or based on robust methods. In practical applications, users may not be able to identify the exact distribution of quality characteristics or to collect a large amount of samples to implement robust methods. To overcome these two problems, new modified Clements' PCIs based on a model selection approach, named robust PCIs method, for the location-scale distribution (LSD) family are proposed to evaluate the process capability of a production process. The most suitable model in the robust PCIs method can be automatically selected by using a competitive procedure from a pool of candidate LSDs. Furthermore, the point estimation, interval inference and new hypothesis testing methods for the PCIs are also studied. A strategy is proposed to improve the manufacturing process. Monte Carlo simulation results show that the proposed robust PCIs method performs well as the distribution of quality characteristic is unable to be clearly identified from several LSD competitors. Finally, the proposed robust PCIs method is applied to two examples for illustration and an improvement strategy is provided to improve the manufacturing process of products.
•A novel operating performance grade classification model with the Taguchi capacity index is proposed.•Multiple modes-based operating performance assessment strategy is developed.•The proposed method ...shows better assessment accuracy in actual fields.
Rate of penetration (ROP) is one of the crucial drilling condition monitoring parameters due to its vital role in real-time assessing drilling operating performance. Operators often adjust operating parameters to meet higher performance requirements. Therefore, drilling operating performance assessment is critical for controlling and optimizing of the drilling process. An operating performance assessment strategy with multiple modes based on least square support vector machines for drilling process is presented in this paper. First, the process capability index is to be taken as the indicator of the ROP and defines the drilling operating performance. Next, the K-means clustering algorithm is used to identify the operating modes. Then, for each mode, an individual drilling operating performance assessment model is established by the method of least squares support vector machines. Finally, drilling operating performance grade is obtained, and actual data of drill well are used for experiments. Further comparative analyses were performed with other state-of-the-art methods, including the Decision tree, Support vector machines (SVM), Least squares support vector machines (LS-SVM), Principal component analysis (PCA), and Partial least squares (PLS). Simulations revealed that the proposed method results in the accurate assessment of operating performance in the drilling process with the accuracy of 87%, the precision of 85.3%, the recall of 88.2%, and the F-Score of 87.6%. In particular, the assessment accuracy was improved by 18.6%, 11.3%, 5.2%, 9.68%, 8.32% in comparison to Decision Tree, SVM, LS-SVM, PCA, and PLS. Performance comparisons reflect the superiority of our model that can ensure high accuracy about operating performance in a drilling process.
•The paper develops a modified sampling plan that considers preceding lot information.•The OC curve of the proposed plan is derived using the exact sampling distribution.•The efficiency of the ...proposed and existing plans are examined and compared.•The proposed plan can provide the desired protections with less sampling.•Tables of plan parameters are provided and an example is studied for illustration.
Several process capability indices have been applied to develop variables single sampling plans (SSPs) and repetitive group sampling plans (RGSPs) for numerical measurements of quality characteristics. Both types of plans are simple to administer and easy to implement, but neither of them considers the available information of previous collected samples. In this study, we develop a modified variables repetitive group sampling plan (Modified-VRGSP) to overcome the disadvantages of the SSP and the RGSP by considering the quality history of preceding lots based on the one-sided capability index. By minimizing the average sample number while satisfying the quality levels demanded by both the producer and the consumer, the plan parameters can be obtained for product acceptance determination. The advantages of the proposed plan over existing variables sampling plans are presented.
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•An Inverse Normalizing Transformation (INT) method is presented for PCIs of non-normal processes.•A Simplified INT method using cubic spline interpolation is proposed to simplify its ...calculation.•Simulation results show the INT method and Simplified INT method are superior to the existed ones.•An example is given to show the advantage of these methods in terms of p value and non-conforming rate.
For process capability analysis of non-normal processes, the non-normal data are often converted into normal data using transformation techniques, then use the conventional normal method to estimate the process capability indices (PCIs), and they are heavily affected by the transformation accuracy of the transformation methods. To enhance the transformation accuracy and improve the PCIs estimation, an Inverse Normalizing Transformation (INT) method is introduced to estimate PCIs for non-normal processes, and a Simplified INT method using cubic spline interpolation is further proposed to simplify its calculation. The performance of the proposed methods is assessed by a simulation study under Gamma, Lognormal and Weibull distributions, and simulation results show that the INT method and Simplified INT method perform better than the existed ones on the whole. Finally, a real case study is presented to show the application of the proposed methods.
In multistage processes, two kinds of process capability indices (specific and total process capability indices) are defined in each stage. The total process capability index calculates the ...capability of each stage when it is affected by the previous stages and the specific process capability index calculates the capability of the stage when the effects of the previous stages are eliminated. Process capability indices in multistage processes are proposed under the assumption of no measurement errors. However, sometimes this assumption may be violated and leads to misleading interpretations. In this paper, the effects of measurement errors on the specific and total process capability indices in the second and third stages of the three-stage processes are statistically analysed. In addition, the effects of the measurement errors on the bias and the mean square error value of the total and specific process capability indices in the second and third stages are studied. Finally, the effects of the measurement errors on the specific and total process capability indices are shown through a numerical example.
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