scholarly journals Research on manufacturing process quality evaluation model and method of complex product based on IAHP

2019 ◽  
Vol 1176 ◽  
pp. 042057
Author(s):  
Jihong Pang ◽  
Yini Jin ◽  
Huibin Feng ◽  
Zhong Yongteng ◽  
Yebo Zheng
2021 ◽  
Vol 11 (21) ◽  
pp. 10182
Author(s):  
Chiao-Tzu Huang ◽  
Kuei-Kuei Lai

Process Capability Indices (PCIs) are not only a good communication tools between sales departments and customers but also convenient tools for internal engineers to evaluate and analyze process capabilities of products. Many statisticians and process engineers are dedicated to research on process capability indices, among which the Taguchi cost loss index can reflect both the process yield and process cost loss at the same time. Therefore, in this study the Taguchi cost loss index was used to propose a novel process quality evaluation model. After the process was stabilized, a process capability evaluation was carried out. This study used Boole’s inequality and DeMorgan’s theorem to derive the (1 – α) ×100% confidence region of (δ,γ2) based on control chart data. The study adopted the mathematical programming method to find the (1 – α) ×100% confidence interval of the Taguchi cost loss index then employed a (1 – α) ×100% confidence interval to perform statistical testing and to determine whether the process needed improvement.


2021 ◽  
Vol 11 (23) ◽  
pp. 11262
Author(s):  
Chun-Min Yu ◽  
Chih-Feng Wu ◽  
Kuen-Suan Chen ◽  
Chang-Hsien Hsu

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.


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