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 short- and 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.

提出了一种基于模糊推理的决策方法，以基于专家的知识和经验，促进过程能力的分析，并实现系统的统计过程能力控制。数据筛选以基于规则的决策树的形式实施，以对过程数据执行正态性测试，R或S控制图测试以及X形控制图测试，以确定过程是否处于以下状态：统计控制。在通过四方框图确定了工艺的改进方向之后，在通过加强技术完成基本改进之后，将重新检查与规范相比由于较大分散而导致缺陷泄漏可能性较高的工艺。另外，通过考虑分布的偏差程度以及短期和长期过程能力之间的差异，使用模糊推理来选择最佳过程能力指标。通过将该方法应用于家用燃气锅炉的制造过程中，验证了该方法的可行性。该方法使过程控制工程师可以检查统计分析的结果以及需要改进的过程的优先级，这些结果可以使用仪表板实时显示。这些结果随后用于决策。该方法使过程控制工程师可以检查统计分析的结果以及需要改进的过程的优先级，这些结果可以使用仪表板实时显示。这些结果随后用于决策。该方法使过程控制工程师可以检查统计分析的结果以及需要改进的过程的优先级，这些结果可以使用仪表板实时显示。这些结果随后用于决策。