Enhancing PCB Reliability with Autoencoder-Based Anomaly Detection for Oxidation Degradation in Selective Soldering Nozzles
Göhringer, Jürgen; Fleischmann, Josef; Trull Domínguez, Óscar; Sánchez, Galdón (2026)
Proceedings of the 36th European Safety and Reliability Conference, ESREL2026, Braga, Portugal.
DOI: 10.3850/ESREL2026061419_esrel26-p26093-cd
Open Access Peer Reviewed
According to the latest reports, selective soldering plays a critical role in the manufacturing of printed circuit boards (PCBs), especially for through-hole technology (THT) components, where molten solder is dispensed from a nozzle to create a continuous 360° wave around the part. The consistency of this wave is crucial for reliable electrical and mechanical connections. However, repeated exposure to high temperatures (up to 300°C) and molten solder (e.g., Sn-Pb or SAC alloys) causes nozzle oxidation, leading to impurity buildup and irregular waves. This degradation results in defective solder joints, compromising product quality, increasing rework costs, and potentially causing production downtime. Current monitoring relies on manual visual inspections, which are subjective and timeconsuming, or rule-based image algorithms that are prone to noise and require frequent recalibration. To overcome these limitations, this paper investigates autoencoders-a type of neural network for unsupervised anomaly detection-that reconstruct input images and flag deviations via reconstruction errors. Data was collected from camera-monitored nozzles under controlled conditions. Preprocessing included grayscale conversion, Gaussian blurring, normalization, and resizing. A Flat (fully connected baseline) was evaluated. Models trained on operable images using mean squared error (MSE) loss; performance assessed with MSE distributions and receiver operating characteristic (ROC) curves, using area under the curve (AUC) as the primary metric. Results show the flat autoencoder achieving ∼ 96 % AUC with a MSE separation (oxidized > 0.02). This is the first application of autoencoders to real-time nozzle oxidation detection in selective soldering, filling a gap in AI for tool-specific degradation. It enables predictive maintenance, reducing defects.
Detecting Soldering Nozzle Degradation With Autoencoders: Anomaly Detection for Selective Soldering Processes
Fleischmann, Josef; Galdón Sánchez, Ana Isabel; Trull Domínguez, Óscar; Göhringer, Jürgen (2025)
Fleischmann, Josef; Galdón Sánchez, Ana Isabel; Trull Domínguez, Óscar...
Proceedings - 4th DOE-UPV International Predoctoral Symposium on Business Management Universitat Politécnica, Valéncia, Spain, 110-116.
Open Access Peer Reviewed
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.
Die digitale Transformation in der Produktion – Herausforderungen und Trends
Göhringer, Jürgen (2024)
Workshop Smart Factory & Industrie 4.0 Ansbach.
Open Access
Die digitale Transformation in der Produktion – Herausforderungen und Trends
Göhringer, Jürgen (2024)
Kongress Smart Factory & Industrie 4.0.
Peer Reviewed
Predictive maintenance for soldering machines based on multi-modal datasets on cloud architectures
Fleischmann, Josef; Göhringer, Jürgen (2024)
Kongress: XI Congreso I+D+i Campus de Alcoi. Creando Sinergias, .
Peer Reviewed
Digitale Produktion – Trends und Möglichkeiten
Göhringer, Jürgen (2024)
Digitale Trends der Kunststofftechnik in der Automobilzulieferindustrie, IHK-Fachforum.
Open Access
Die Digitale Transformation am Beispiel eines Ökosystems für Predictive Maintenance
Göhringer, Jürgen (2024)
Forschungs- und InnovationsTag (FIT) 2024 der Hochschule Ansbach.
Open Access
Unternehmensübergreifende Wertschöpfungsketten auf Basis optimierter Produktionsplanung mit KI-Modellen
Göhringer, Jürgen (2023)
Kongress KIT Karlsruhe.
Peer Reviewed
Einsatz von KI-Methoden zur Optimierung der Service-Prozesse zwischen mittelständischem Maschinenbau und Endkunden (KI4Service)
Göhringer, Jürgen; Fleischmann, Josef (2023)
Abschlussbericht Forschungsprojekt Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie / VDI.
Predictive Maintenance und Condition Monitoring für Lötanlagen – KI4Service Cloud
Göhringer, Jürgen; Fleischmann, Josef (2022)
Elektronische Baugruppen und Leiterplatten EBL 375, 191-195.
optimize service processes for soldering machines
Göhringer, Jürgen (2022)
IPC APEX EXPO 2022 .
Peer Reviewed
Einsatz von KI-Methoden zur Optimierung der Service-Prozesse zwischen mittelständischem Maschinenbau und Endkunden
Göhringer, Jürgen (2021)
Zwischenbericht KI4Service Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie / VDI.
Digitale Ökosysteme in der Industrie –Typologie, Beispiele und zukünftige Entwicklung
Göhringer, Jürgen; Falk, S; Lehmmann-Brauns, Sicco; Otto, Boris (2021)
Bundesministerium für Wirtschaft und Klima (BMWK).
Open Access
Die Digitale Transformation – Neue Chancen durch digitale Prozesse und Geschäftsmodelle
Göhringer, Jürgen (2019)
Kongressband OPEXCON 2019, CETPM.
Internet-based diagnosis of assembly systems
Göhringer, Jürgen (1999)
Volume 50, Issue 1, 2001, Cirp Annals.
Peer Reviewed
Multimedia system for remote diagnosis of complex placement machines
Göhringer, Jürgen (1999)
International Journal of Advanced Manufacturing Technology, Volume 15, Issue 10, pp 722-729, Springer-Verlag London, 1999.
Peer Reviewed
Multimedia system for remote diagnosis of complex placement machines
Göhringer, Jürgen (1998)
Proceedings of the V International Conference on Monitoring an Automatic Supervision in Manufacturing 1998.
Peer Reviewed
Prof. Dr.-Ing. Jürgen Göhringer
Hochschule Ansbach
Fakultät Technik
Residenzstr. 8
91522 Ansbach
T 0981 4877-573 juergen.goehringer[at]hs-ansbach.de