Risk Analysis in Engineering and Economics

Process capability indices have been widely used in the manufacturing industry to provide numerical measures for process potential and process performance. However, from the perspective of improvin...

Capability performance analysis for processes with multiple characteristics using accuracy and precision

https://ir.nctu.edu.tw:443/bitstream/11536/21191/1/000315198300002.pdf

Capability assessment for processes with multiple characteristics: A generalization of the popular index Cpk

Often the quality of a process is determined by several correlated univariate variables. In such cases, the considered quality characteristic should be treated as a vector. Several different multiv ...

A multivariate process capability index based on the first principal component only

The growing popularity of do-it-yourself (DIY) projects has increased the penetration of hand tools devices into the market. This paper thus conducts a case study on ratchet torque wrenches and develops an assessment and verification model of process quality as a means of assisting hand tool manufacturers in managing the quality of ratchet torque wrench products and promoting the on-going development in the hand tool industry. We first propose the process quality index (PQI) that can directly determine a precise level of quality based on the relationship between various process capability indices and Six Sigma quality levels. Using inference, we further develop a testing model of PQI to evaluate the quality level reached of process performance. The present case study is investigated to illustrate the practical applicability of the proposed method. We believe that the proposed model will facilitate the production of high-value products and thereby enhance the competitive advantage of firms in Taiwan.

Process Quality Assessment Model of Hand Tools: A Case Study on the Handle of Ratchet Torque Wrench

Process capability indices have been widely used in industry today since they provide quantitative measures of process performance. Instead of using process capability index, the process incapability index C pp introduced by Greenwich and Jahr-Schaffrath (1995) has an advantage of separating the information of process accuracy and precision for a symmetric tolerance. Using C pp process incapability index, the process performance and its risk level can easily be understood by quality practitioners. Another process incapability index proposed by Chen (1998) is to measure the process performance for an asymmetric tolerance. However, it can't correctly reflect the true manufacturing risk. In order to accurately measure the process performance for both symmetric and asymmetric tolerances, a new process incapability index using desirability function is proposed in this article. The relationship between various new process incapability indices and their associated nonconforming rates has also been explored. Fina...

Development of a New Process Incapability Index with an Application to the Evaluation of Manufacturing Risk

Angiocardiography is a key to the evaluation of cardiac function. The index of left ventricular ejection fraction (LVEF) also helps interpret cardiac hypertrophy, valve regurgitation, and regional myocardial systolic dysfunction for medical decision-making. However, it is difficult and time-consuming to calculate the LVEF. Therefore imaging techniques become much more important for assistance. In this research, three neural networks including DenseNet, EfficientNet, and ResNet are introduced for cardiac area calculation. EfficientNet turns out to be the best model with a mean dice accuracy of 0.91. The other two models also show significant performance with 0.958 for DenseNet and 0.955 for ResNet. With these network models, physicians can select and calculate the area of the heart. With the time-series images of cardiac catheterization, the size of the entire cardiac systole can be determined automatically. Therefore, it greatly helps medical professionals diagnose abnormal cardiac systole problems immediately.

Multi-model Comparison of Cardiac Segmentation Model for Angiocardiography by Deep Learning

Left ventricular angiography is an assessment of cardiac structure and function that is often performed during cardiac catheterization. In addition to obtaining the index of left ventricular ejection rate (LVEF), left ventricular angiography also assists in the interpretation of cardiac hypertrophy, valve regurgitation, and regional myocardial systolic dysfunction. This also assists the clinician in the treatment and the judgment of opening the blood vessels. Although left ventricular angiography has been a well-established and internationally recognized detection method, it is not easy to train cardiac catheterization professionals, as image analysis requires sufficient experience but it is time-consuming. In this study, the deep learning models are introduced to reduce clinical interpretation errors and provide information on cardiac function and local cardiac systolic abnormalities in a short period. Then, clinicians can easily make medical decisions and judgments. The models in this study are expected to reduce 90% of operation time and improve the accuracy from 50% to 99%. It also helps detect myocardial hypoxia and determine whether to perform coronary surgery. Automatic and accurate detection of cardiac ejection rate, cardiac hypertrophy1, and abnormal local contraction is enabled to immediately warn and feedback to clinicians to make the most accurate treatment decisions.

Automatic Detection of Cardiac Ejection Rate in Left Ventricular Photography Using Deep Learning

Generally, an industrialproduct hasmore thanone qualitycharacteristic. In order toestablish performance measures for evaluating the capability of a multivariate manufacturing process, several multivariate process capability indices have been developed in the past few years. Among them, Taam’s MCp and MCpm indices have the drawback of overestimation and Hubele’s three-component capability vector lacks simplicity in practice. In this article, taking the correlation among multiple quality characteristics into account, we develop two novel indices; NMCp and NMCpm. Using two numerical examples we demonstrate that the true performance of multivariate processes are accurately reflected in our NMCp and NMCpm indices and in their associated interval estimates. Finally, simulation results show that our indices outperform both those of Taam and Hubele. Copyright © 2009 John Wiley & Sons, Ltd.

New capability indices for evaluating the performance of multivariate manufacturing processes

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Papers

Development of a New Process Incapability Index with an Application to the Evaluation of Manufacturing Risk

Multi-model Comparison of Cardiac Segmentation Model for Angiocardiography by Deep Learning

Automatic Detection of Cardiac Ejection Rate in Left Ventricular Photography Using Deep Learning

New capability indices for evaluating the performance of multivariate manufacturing processes