Predictive maintenance for printed circuit boards using eXtreme gradient boosting

Purpose This study investigates the impact of reflow temperature on the reliability of printed circuit board assembly (PCBA) produced through surface mount technology (SMT). The authors define failure as deviations of the temperature curve from the process window (PW) of key PCB components. This study aims to develop a prognostic and health management system for failure prediction. Design/methodology/approach The study used reflow equipment in a real-world production environment. Key parameters affecting temperature curve deviations were identified, and the eXtreme Gradient Boosting (XGBoost) method was used to construct a failure prediction model. This model allows onsite monitoring and predicts failures based on various feature combinations. Upon detecting potential failures, the model alerts the on-duty engineer, providing failure time and recommending rechecks. Findings The failure prediction system achieved high accuracy (94%), precision (99%) and recall (89%). The PHM system effectively identifies temperature curve deviations, enabling timely interventions. It calculates the PCBA product serial number in the reflow furnace zone, assesses deviations from the PW and recommends rechecks, thus enhancing PCBA production reliability. Originality/value This study integrates the XGBoost method into a PHM system for failure prediction in PCBA production. Combining real-world production data with advanced machine-learning techniques offers a novel approach to addressing reliability concerns in the SMT reflow process. Integrating failure alerts into the Shop Floor system ensures prompt rechecks of high-risk PCBs, enhancing overall system efficiency.