Enhanced Welding Operator Quality Performance Measurement: Work Experience-Integrated Bayesian Prior Determination

TL;DR

This paper improves operator quality performance measurement in construction fabrication by integrating work experience effects using a Bayesian approach with a Plateau learning model, leading to more reliable assessments.

Contribution

It introduces a systematic method to incorporate work experience into Bayesian priors for operator performance measurement, enhancing accuracy over previous models.

Findings

The approach effectively models the learning effect on quality performance.

Incorporating work experience improves measurement reliability.

The method is applicable to fabrication quality control processes.

Abstract

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.