UNISON Digital Twin framework for Advanced Process Time Prediction in Laser Micromachining Process for PCB Smart Manufacturing

Increasing needs for high-mix customization and shortening product life cycles for printed circuit board (PCB) are challenging since PCB has become critical components for heterogeneous integration for integrated circuits. Accurate process time prediction in bottleneck production of laser micromachining is crucial to ensure reliable production scheduling, timely delivery, and cost effectiveness for maintaining competitive advantages. However, limitations of existing approaches can be traced in part to the lack of a structured framework within which predictive intelligence and physical shop-floor feedback can be integrated in light of production dynamics, especially under small-scale yet high-dimensional data environments commonly encountered in prototyping and customized PCB manufacturing. This study aims to develop a UNISON Digital Twin framework for advanced process time prediction in PCB laser micromachining that synchronizes physical production states with a dual-step artificial intelligence driven engine. A stacked ensemble learning mechanism is developed to enhance prediction robustness and adaptively compensate for bias under limited and unbalanced datasets. The integration of UNISON decision architecture enables structured interaction among perception, prediction, evaluation, and feedback layers to support fast responsive and adaptive system interaction. An empirical study was conducted in a leading PCB manufacturing facility in Taiwan for validation. The results have shown that the proposed approach outperforms conventional machine learning models in prediction stability and accuracy under dynamic production scenarios. Indeed, the developed solution has shown practical viability to provide reliable early-stage process time estimation, enhance scheduling decision quality, reduce unnecessary cycle time, and empower smart production for high-mix PCB manufacturing.