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.
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