Laboratory Introduction

• Quality Data Decision Laboratory – Introduction The purpose of establishing this laboratory is to cultivate students with advanced capabilities in quality engineering and industrial statistical analysis.By integrating theoretical foundations with data-driven analysis, the laboratory focuses on in-depth studies of process quality improvement and decision analysis. The primary research topics include the design of sampling plans and the development of risk models,covering multi-stage sampling mechanisms and the derivation of acceptance probabilities. Through balancing quality levels and acceptable risk, the laboratory aims to establish inspection standards based on a rigorous statistical foundation. In addition, the research scope extends to cost–benefit evaluation and optimization of sampling strategies, developing appropriate sampling approaches for various production environments. These include applications in high-yield precision manufacturing and target-oriented process control. By optimizing the parameters of sampling plans, the laboratory seeks to enhance the robustness and broad applicability of decision-making models.
• Implementation and Validation In terms of practical implementation and validation, the laboratory integrates statistical modeling with numerical analysis. The primary research tools include MATLAB and Minitab. Students will learn to apply MATLAB for algorithm implementation, solving optimization problems, and designing graphical user interfaces (GUI). In addition, Minitab will be utilized to perform statistical analysis and data interpretation. Through this training process, students will develop and implement decision-making models, which are further validated through the combination of numerical simulations and empirical analysis. This approach ensures that research outcomes possess both strong theoretical foundations and practical industrial applicability.