scholarly journals Two-Tailed Fuzzy Hypothesis Testing for Unilateral Specification Process Quality Index

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2129
Author(s):  
Chun-Min Yu ◽  
Win-Jet Luo ◽  
Ting-Hsin Hsu ◽  
Kuei-Kuei Lai
Keyword(s):  
Confidence Interval ◽  
Quality Index ◽  
Quality Characteristics ◽  
Process Quality ◽  
Estimation Accuracy ◽  
Fuzzy Membership Function ◽  
Quality Indices ◽  
Sampling Errors ◽  
Unknown Parameters ◽  
Target Values

The quality characteristics with unilateral specifications include the smaller-the-better (STB) and larger-the-better (LTB) quality characteristics. Roundness, verticality, and concentricity are categorized into the STB quality characteristics, while the wire pull and the ball shear of gold wire bonding are categorized into the LTB quality characteristics. In terms of the tolerance, zero and infinity (∞) can be viewed as the target values in line with the STB and LTB quality characteristics, respectively. However, cost and timeliness considerations, or the restrictions of practical technical capabilities in the industry, mean that the process mean is generally far more than 1.5 standard deviations away from the target value. Researchers have accordingly proposed a process quality index conforming to the STB quality characteristics. In this study, we come up with a process quality index conforming to the LTB quality characteristics. We refer to these two types of indices as the unilateral specification process quality indices. These indices and the process yield have a one-to-one mathematical relationship. Besides, the process quality levels can be completely reflected as well. These indices possess unknown parameters. Therefore, sample data are required for calculation. Nevertheless, interval estimates can lower the misjudgment risk resulting from sampling errors more than point estimates can. In addition, considering cost and timeliness in the industry, samples are generally small, which lowers estimation accuracy. In an attempt to increase the accuracy of estimation as well as overcome the uncertainty of measured data, we first derive the confidence interval for unilateral specification process quality indices, and then propose a fuzzy membership function on the basis of the confidence interval to establish the two-tailed fuzzy testing rules for a single indicator. Lastly, we determine whether the process quality has improved.

scholarly journals A Fuzzy Evaluation Model Aimed at Smaller-the-Better-Type Quality Characteristics

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2513
Author(s):  
Kuen-Suan Chen ◽  
Tsun-Hung Huang
Keyword(s):  
Confidence Interval ◽  
Quality Index ◽  
Evaluation Model ◽  
Quality Characteristics ◽  
Process Quality ◽  
Small Sample ◽  
Fuzzy Evaluation ◽  
Sample Sizes ◽  
Small Sample Sizes ◽  
Fuzzy Evaluation Model

Numerous key components of tool machines possess critical smaller-the-better-type quality characteristics. Under the assumption of normality, a one-to-one mathematical relationship exists between the process quality index and the process yield. Therefore, this paper utilized the index to produce a quality fuzzy evaluation model aimed at the small-the-better-type quality characteristics and adopted the model as a decision-making basis for improvement. First, we derived the 100(1 −α)% confidence region of the process mean and process standard deviation. Next, we obtained the 100(1 −α)% confidence interval of the quality index using the mathematical programming method. Furthermore, a one-tailed fuzzy testing method based on this confidence interval was proposed, aiming to assess the process quality. In addition, enterprises’ pursuit of rapid response often results in small sample sizes. Since the evaluation model is built on the basis of the confidence interval, not only can it diminish the risk of wrong judgment due to sampling errors, but it also can enhance the accuracy of evaluations for small sample sizes.

scholarly journals The Fuzzy Process Quality Evaluation Model for the STB Quality Characteristic of Machining

2020 ◽  
Vol 10 (22) ◽  
pp. 8272
Author(s):  
Win-Jet Luo ◽  
Kuen-Suan Chen ◽  
Chun-Min Yu ◽  
Ting-Hsin Hsu
Keyword(s):  
Machine Tool ◽  
Evaluation Model ◽  
Quality Characteristics ◽  
Process Quality ◽  
Quality Characteristic ◽  
Quality Level ◽  
Sampling Errors ◽  
Statistical Process ◽  
Accuracy And Precision ◽  
Fuzzy Process

Whether it is important components of a machine tool itself or various important components processed by the machine tool, many vital quality characteristics mostly belong to the smaller-the-better type. When the process quality levels of these quality characteristics do not attain to the criteria, friction loss may increase during the machine operation, affecting not only the process precision and accuracy but also the lifetime of the product. Therefore, this study applied a smaller-the-better six-sigma quality index simultaneously demonstrating process quality level and process yield. Besides, in coping with statistical process control data, a one-tail confidence-interval-based fuzzy testing method was developed to evaluate process quality. Because this approach is built on the basis of confidence intervals, it can reduce the possibility of misjudgment resulting from sampling errors as well as integrate past experience to enhance the accuracy and precision of the assessment, and then it can grasp the timeliness of improvement.

scholarly journals Fuzzy Supplier Selection Method Based on Smaller-The-Better Quality Characteristic

2020 ◽  
Vol 10 (10) ◽  
pp. 3635 ◽  
Author(s):  
Chun-Min Yu ◽  
Kuen-Suan Chen ◽  
Kuei-Kuei Lai ◽  
Chang-Hsien Hsu
Keyword(s):  
Confidence Interval ◽  
Quality Characteristics ◽  
Point Estimation ◽  
Quality Characteristic ◽  
Sampling Errors ◽  
Process Yield ◽  
Industry Chain ◽  
Testing Method ◽  
Increase Product Quality

Many important parts of tool machines all have the important smaller-the-better (STB) quality characteristics. The important STB quality characteristics will impact on the quality of the end-product. At the same time, supplier quality influences the quality and functionality of the end-product, so suppliers must be selected with caution. The six sigma quality index for the STB quality characteristics can directly reflect process quality levels. Besides, this index possesses a mathematical relationship with process yield. Nevertheless, the point estimation will cause the risk of misjudgment, due to sampling errors. As a result, this study applies the confidence interval of the index to a two-tailed fuzzy testing method, in order to select appropriate suppliers. Now that this method is on the basis of the confidence interval, the possibility of misjudgment caused by sampling errors will be reduced, while the precision of the selection will be enhanced. The method can help companies increase product quality, as well as the competitiveness of the industry chain as a whole. Finally, a numerical example is presented to show how to approach this method and its efficacy.

Dual dimensional fuzzy testing based on upper confidence limits for supplier selection

2021 ◽  
pp. 1-14
Author(s):  
Kuen-Suan Chen ◽  
Chun-Min Yu
Keyword(s):  
Supplier Selection ◽  
Selection Index ◽  
Process Quality ◽  
Fuzzy Membership Function ◽  
Confidence Limits ◽  
Sampling Errors ◽  
Time Performance ◽  
Proposed Model ◽  
Two Indices ◽  
Manufacturing Time

Many corporations purchase components from suppliers, which can reduce operating costs and enable firms to focus their resources on core advantages. Studies have indicated that process quality and manufacturing time performance are two crucial indicators for supplier selection. We used the process quality index and a manufacturing time performance index to create a dual dimensional fuzzy supplier selection model. First, the upper confidence limits of these two indices were derived, and a fuzzy membership function based on these limits was constructed. Based on the fuzzy test rules for process quality and manufacturing time performance, we divided the fuzzy supplier selection matrix into nine evaluation zones. Using the upper confidence limits of these two indices, we created evaluation coordinates and assigned weights based on the location of the coordinates. Then, the total of all the weights was employed to form a supplier selection index for which a higher value means a higher ranking. The use of confidence limits decreased the chance of misjudgment resulting from sampling errors while the fuzzy test rules increased the applicability of the model. Consequently, the proposed model can be used to select suppliers efficiently so as to form partnerships in which corporations and suppliers can grow together.

scholarly journals Fuzzy Quality Evaluation Model Constructed by Process Quality Index

2021 ◽  
Vol 11 (23) ◽  
pp. 11262
Author(s):  
Chun-Min Yu ◽  
Chih-Feng Wu ◽  
Kuen-Suan Chen ◽  
Chang-Hsien Hsu
Keyword(s):  
Quality Index ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Process Quality ◽  
Small Sample ◽  
Sample Sizes ◽  
Sampling Errors ◽  
Process Yield ◽  
Small Sample Sizes ◽  
Quality Evaluation Model

Many studies have pointed out that the-smaller-the-better quality characteristics (QC) can be found in many important components of machine tools, such as roundness, verticality, and surface roughness of axes, bearings, and gears. This paper applied a process quality index that is capable of measuring the level of process quality. Meanwhile, a model of fuzzy quality evaluation was developed by the process quality index as having a one-to-one mathematical relationship with the process yield. In addition to assessing the level of process quality, the model can also be employed as a basis for determining whether to improve the process quality at the same time. This model can cope with the problem of small sample sizes arising from the need for enterprises’ quick response, which means that the accuracy of the evaluation can still be maintained in the case of small sample sizes. Moreover, this fuzzy quality evaluation model is built on the confidence interval, enabling a decline in the probability of misjudgment incurred by sampling errors.

scholarly journals Developing a Novel Fuzzy Evaluation Model by One-Sided Specification Capability Indices

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1076
Author(s):  
Wei Lo ◽  
Chun-Ming Yang ◽  
Kuei-Kuei Lai ◽  
Shao-Yu Li ◽  
Chi-Han Chen
Keyword(s):  
Evaluation Model ◽  
Critical Value ◽  
Process Quality ◽  
Quality Characteristic ◽  
Fuzzy Membership Function ◽  
Environmental Damage ◽  
Quality Level ◽  
Fuzzy Evaluation ◽  
Radar Chart ◽  
Fuzzy Evaluation Model

When all of the one-sided specification indices of each quality characteristic reach the requirements of the process quality level, they can ensure that the process capability of the product meets the requirements of the process quality level. This study constructs a fuzzy membership function based on the upper confidence limit of the index, derives the fuzzy critical value, and then labels the fuzzy critical value on the axis of the visualized radar chart as well as connects adjacent critical points to shape a regular polygonal critical region. Next, this study calculates the observed value of the index to estimate and mark it on the axis for forming a visualized fuzzy radar evaluation chart. Obviously, this fuzzy evaluation model not only reduces the testing cost but also makes the quality level quickly meet the requirements of the specifications. Further, the radar chart can reduce the risk of misjudgment attributable to sampling errors and help improve the accuracy of evaluation by a confidence-upper-limit-based fuzzy evaluation model. Therefore, this easy-to-use visualized fuzzy radar evaluation chart is used as an evaluation interface, which has good and convenient management performance to identify and improve critical-to-quality quickly. Improving the quality of the process before the product is completed will also have the advantage of reducing social losses and environmental damage costs.

scholarly journals Nutritional status and dietary quality index of head and neck cancer patients undergoing chemo radiotherapy

2020 ◽  
Vol 8 (1) ◽  
pp. 14-14
Author(s):  
Shirin Fattahi ◽  
Farshad Seyyednejad ◽  
Sarvin Sanaie ◽  
Tahereh Parhizkar ◽  
Elnaz Faramarzi
Keyword(s):  
Head And Neck Cancer ◽  
Nutritional Status ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer ◽  
Quality Index ◽  
Dietary Diversity ◽  
Mini Nutritional Assessment ◽  
Dietary Quality ◽  
Quality Indices

Introduction: Considering the important role of early detection of malnutrition in patients with cancer and its negative effects on the outcome, as well as the lack of any published article (to the best of our knowledge) about the dietary quality index in head and neck cancer patients treated with chemoradio therapy, we decided to evaluate the nutritional status and dietary quality index in these patients. Methods: In this study, thirty-seven volunteer patients with head and neck cancer were recruited. Nutritional status of the patients was evaluated by Mini Nutritional Assessment (MNA) questionnaire. Dietary diversity score, dietary variety score, and diet quality index–international were calculated to assess the dietary quality of the patients. Results: Our findings indicated that about half of the patients were well nourished and 48.6%were at the risk of malnutrition. We did not find any significant differences between variousdietary quality indices and nutritional status of the patients. However, a significant reverse correlation was observed between dietary quality indices and nutritional status of the patients. Conclusion: According to our findings, the evaluation of nutritional status and the prediction of the patients at higher risks of chemoradio therapy-induced adverse events, may have a major role in the prevention of treatment gaps.

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