Detecting Soldering Nozzle Degradation With Autoencoders: Anomaly Detection for Selective Soldering Processes

Abstract

Maintaining the quality and reliability of selective soldering processes in printed circuit board (PCB) manufacturing is crucial for economic efficiency and sustainability. This study explores the application of autoencoders, a neural network-based approach, for automatic anomaly detection in soldering nozzles, emphasizing the economic benefits of predictive maintenance. Traditional methods, such as visual inspections and rule based algorithms, are limited by subjectivity, delay, and susceptibility to false readings, leading to increased costs and production downtime. Autoencoders, on the other hand, use unsupervised learning to identify deviations from normal operational states by reconstructing input data and detecting anomalies based on reconstruction errors. This predictive maintenance approach can significantly reduce unexpected failures and maintenance costs, ensuring continuous production and enhancing sustainability. This research highlights the potential of autoencoder-based systems to automate and enhance the reliability of selective soldering processes, ultimately leading to significant economic benefits. The findings pave the way for real-time monitoring solutions, reducing dependency on manual inspections and rule-based algorithms, and improving production efficiency and sustainability in the electronics manufacturing industry.

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