增强的焊接操作员质量绩效测量：工作经验整合贝叶斯先验确定
Enhanced Welding Operator Quality Performance Measurement: Work Experience-Integrated Bayesian Prior Determination
摘要：
在建筑制造业中，操作员质量绩效的测量一直是具有挑战性的。在各种原因中，学习效应是一个重要的因素，需要将其纳入实现可靠的操作员质量性能分析。本研究旨在通过引入学习效应（即，工作经验）。为了实现这一目标，高原学习模型的选择，定量表示质量绩效和工作经验之间的关系，通过β-二项式回归方法。基于这种关系，信息先验确定方法，其中包括操作员的工作经验信息，开发，以加强以前的贝叶斯为基础的操作员质量绩效测量。在学术上，本研究提供了一个系统的方法来获得贝叶斯信息先验，通过整合多源信息。实际上，所提出的方法可靠地测量制造质量控制过程中的操作员质量性能。
英文摘要：
Measurement of operator quality performance has been challenging in the construction fabrication industry. Among various causes, the learning effect is a significant factor, which needs to be incorporated in achieving a reliable operator quality performance analysis. This research aims to enhance a previously developed operator quality performance measurement approach by incorporating the learning effect (i.e., work experience). To achieve this goal, the Plateau learning model is selected to quantitatively represent the relationship between quality performance and work experience through a beta-binomial regression approach. Based on this relationship, an informative prior determination approach, which incorporates operator work experience information, is developed to enhance the previous Bayesian-based operator quality performance measurement. Academically, this research provides a systematic approach to derive Bayesian informative priors through integrating multi-source information. Practically, the proposed approach reliably measures operator quality performance in fabrication quality control processes.
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