Quality-preserving Model for Electronics Production Quality Tests Reduction

2026-04-07

AI Summary

This study addresses the rigidity of conventional mass production test flows for electronic products, which struggle to adapt to shifting defect distributions and often incur high costs or quality escapes. To overcome this, the authors propose an adaptive test selection framework: in the offline phase, a minimum-cost diagnostic subset is constructed via greedy set cover; in the online phase, process stability monitoring is integrated with a Thompson sampling–based multi-armed bandit strategy to dynamically switch between full and reduced test suites. This work pioneers the incorporation of online learning and process stability awareness into test planning, achieving zero escape while substantially reducing test overhead. Evaluated on 28,000 printed circuit boards, the offline approach reduced test time by 18.78% and 91.57% under different configurations, and the adaptive strategy successfully eliminated all escape defects—reducing them from 118 to zero—in the presence of real concept drift.

Abstract

Manufacturing test flows in high-volume electronics production are typically fixed during product development and executed unchanged on every unit, even as failure patterns and process conditions evolve. This protects quality, but it also imposes unnecessary test cost, while existing data-driven methods mostly optimize static test subsets and neither adapt online to changing defect distributions nor explicitly control escape risk. In this study, we present an adaptive test-selection framework that combines offline minimum-cost diagnostic subset construction using greedy set cover with an online Thompson-sampling multi-armed bandit that switches between full and reduced test plans using a rolling process-stability signal. We evaluate the framework on two printed circuit board assembly stages-Functional Circuit Test and End-of-Line test-covering 28,000 board runs. Offline analysis identified zero-escape reduced plans that cut test time by 18.78% in Functional Circuit Test and 91.57\% in End-of-Line testing. Under temporal validation with real concept drift, static reduction produced 110 escaped defects in Functional Circuit Test and 8 in End-of-Line, whereas the adaptive policy reduced escapes to zero by reverting to fuller coverage when instability emerged in practice. These results show that online learning can preserve manufacturing quality while reducing test burden, offering a practical route to adaptive test planning across production domains, and offering both economic and logistics improvement for companies.

Problem

• test cost reduction
• escape risk
• concept drift
• adaptive testing
• manufacturing quality

Innovation

• adaptive test selection
• Thompson sampling
• multi-armed bandit
• test cost reduction
• escape risk control