| 성과구성 |
Ab s t r a c t
This paper proposes a decision-making method based on fuzzy inference to facilitate process capability
analysis based on the knowledge and experience of experts, and implement systematized statistical
process capability control. Data screening is implemented in the form of a rule-based decision-making
tree to perform normality testing, R- or s-control chart testing, and x-bar control chart testing on process
data to determine whether a process is in a state of statistical control. After setting the improvement
direction of the process using a four-block diagram, the processes with a high probability of defect leakage
due to large dispersion compared to the specification are reexamined after the fundamental improvement
is completed by reinforcing the technology. Additionally, an optimal process capability index is selected
using fuzzy inference by considering the degree of bias in a distribution and the differences between shortand
long-term process capabilities. The feasibility of the proposed method was verified by applying it to
a process for manufacturing home gas boilers. The method enables process control engineers to examine
the results of the statistical analysis and the priority of the process to be improved, which are visualized
in real time using a dashboard. These results are subsequently used for decision-making.
Keywords:Control chartFour-block diagramFuzzy inferenceProcess capability analysisProcess capability index Statistical process control |
