Reliability-Centered Quality Control in Additive Manufacturing: Quantifying Defect Detectability, Dimensional Uncertainty, and Qualification Risk Across the Digital Thread

Abstract

This article presents a reliability-centered quality control framework for metal powder bed fusion that treats quality assurance as a decision system spanning in-situ monitoring, post-build inspection, uncertainty propagation, and acceptance governance across the digital thread, and it quantifies decision performance using probability of defect non-detection, probability of tolerance exceedance, time-to-disposition, and expected cost of quality under controlled nuisance-alarm constraints. A scenario-based quantitative study is developed using Monte Carlo simulation of a multi-build production campaign with realistic drift events, defect-size distributions, and measurement-system uncertainties, comparing four architectures that range from inspection-heavy qualification to monitoring-forward production with risk-based sampling and governed baseline updating. Results show that monitoring value is maximized when indicators are engineered around defect mechanisms and tolerance-critical features, because generic anomaly scoring can either overload operations with unstable alarms or inflate thresholds to the point of insensitivity when baseline variance is large, while hybrid strategies that allocate high-resolution inspection to high-uncertainty builds based on calibrated risk scores can reduce total inspection burden and disposition time without increasing residual acceptance risk.