Comparative Study on Different Methods to Generate Synthetic Data for the Classification of THT Solder Joints - Thielen, Nils; Rachinger, Ben; Schröder, Felix; Preitschaft, Anja; Meier, Sven; Seidel, Reinhardt; Reinhardt, Andreas; Franke, Jörg (2024 1st International Conference on Production Technologies and Systems for E-Mobility (EPTS), 2024)
Automated Optical Inspection (AOI) is still one of the major tools for the quality control of solder joints; especially, if the requirements for the solder joints' mechanical and electrical properties are high. This is the case for products in the automotive sector, e.g. air bags or brake systems. Conventional test routines are time-consuming to define and not flexible regarding quality limits and lead to a high amount of manual reinspection. Thus, Artificial Intelligence offers the potential to address these disadvantages. Artificial Intelligence, on the other hand, suffers from highly imbalanced datasets resulting from the low amount of defects on solder joint level. One possibility to face this challenge is the utilization of synthetic data. This work compares different methods to generate data using both conventional data augmentation and deep generative models as well as virtual world rendering in order to enhance dataset quality in general. These are applied to an industrial dataset of THT solder joints. All tested approaches lead to an improvement in model quality. The best results are achieved with synthetic images from a Generative Adversarial Network, relatively increasing performance by around 6 % while minimizing error slip.<br
Keywords: Safety devices; Production; Quality control; Optical imaging; Rendering (computer graphics); Adaptive optics; Mechanical factors; Synthetic Data; Artificial Intelligence; Automated Optical Inspection; Electronics Production

Development and validation of a digital twin framework for SMT manufacturing - Seidel, Reinhardt; Rachinger, Ben; Thielen, Nils; Schmidt, Konstantin; Meier, Sven; Franke, Jörg (Computers in Industry, 2023)
Electronics manufacturing is a global industry and key to innovation in the virtual world because it is the physical backbone. The cost-effective development and manufacture of such electronic modules are critical to maintaining the competitiveness of high-wage manufacturing countries. Therefore, two approaches suggest themselves: Design for Manufacturing (DfM) and manufacturing optimization. To apply both approaches in in-dustry, it is necessary to introduce models that describe the relationship between process input and process output. Applied to electronics manufacturing processes, this means that design, process parameters, and material properties must be mapped to process quality criteria in end-of-line testing. Recently, machine learning (ML) algorithms have been emerging and dominating other modeling methods such as numerical simulation. To develop such complex ML models, a unified data structure for each input and output must be defined. This paper proposes an extensible ML-enabled framework that provides direct and structured access to the printed circuit board (PCB) design and process parameters. This framework is used to perform structured data acquisition using a custom data mining board. During the manufacturing of these PCBs on a full surface mount technology (SMT) process line, all available process machine-level data is collected, archived, and parsed into a uniform, stan-dardized, and flat data structure. This enables fast analysis of the correlation between inputs and quality criteria, direct access by ML algorithms, and training of models. Through these measures, the quality of the framework is validated and correlations are revealed and compared to previous literature. This shows the great importance of a data framework for the integration of data analysis technologies into industrial processes.
CRIS-Team WoS Importer:2023-01-13

Improving THT-AOI Image Classification through Federated Learning: A Study on Model Performance and Training Stability under Various Data Distributions - Rachinger, Ben; Thielen, Nils; Meier, Sven; Franke, Jörg; Risch, Florian (IPC APEX EXPO 2025 Technical Conference Proceedings, 2025)
Automated optical inspection (AOI) systems are commonly applied for in-line quality control of through hole technology (THT) solder joints. However, these systems usually rely on conventional image processing, often leading to high pseudo error rates and extensive workloads during subsequent manual inspection. Deep learning approaches have shown promise in reducing pseudo errors by classifying solder joint images flagged as defective by AOI systems into two classes: real defects and pseudo errors. Yet, their performance is limited by the availability of sufficient local training data, and while cross-company data sharing could address this issue, it remains constrained due to privacy concerns. Federated learning (FL) offers a promising solution by enabling collaborative model training across companies without the need to share raw data, thus preserving data privacy. This study evaluates the application of FL for THT-AOI image classification, specifically addressing common FL challenges such as divergence of local model updates and performance decay under non-independent and identically distributed (non-IID) client data. Therefore, local, centralized and federated learning are compared based on real and artificially separated data under IID and non-IID conditions. Our results demonstrate that convolutional neural network (CNN)-based FL can be effectively applied for THT-AOI image classification, achieving training stability and model performance comparable to centralized learning. By implementing FL, we reduce AOI pseudo errors by an average of 23.6% on an aggregated test set containing heterogeneous data from multiple electronics manufacturers, significantly lowering the need for manual inspection. This represents a 12.1% relative improvement in pseudo error reduction over local learning, with both approaches maintaining a controlled error slip rate of 1.0
Keywords: Federated Learning; Elektronikproduktion; THT

Optimierung der Prozessparameter beim Selektivwellenlöten mit Hilfe von Offline Reinforcement Learning - Rachinger, Ben; Seidel, Reinhardt; Meier, Sven; Franke, Jörg (GMM-Fb. 107: EBL 2024 – Elektronische Baugruppen und Leiterplatten, 2024)
Im Zuge der Miniaturisierung elektrischer Baugruppen wurden THT-Komponenten weitgehend durch SMT-Bauteile verdrängt. Dennoch verbleiben zumeist vereinzelte Bauteile in Durchsteckmontage aufgrund deren überlegener mechanischer und thermischer Widerstandsfähigkeit [1], [2]. Für diese ausgewählten THT-Komponenten stellt das Selektivwellenlöten ein attraktives Verbindungsverfahren dar, weil dieses mit einem minimalen thermischen Einfluss auf umgebende SMT-Bauteile einhergeht [3]. Das Erreichen eines hohen Lotdurchstiegs ist bei diesem Verfahren stark von der Lötstellengeometrie und den gewählten Prozessparametern abhängig [4]. Bei der Einführung neuer Leiterplattendesigns in die Serienproduktion werden diese Parameter jedoch aktuell häufig durch aufwändige Versuchsreihen ausgewählt [5]. Ziel des Beitrags ist es, diesen Prozess durch eine objektive Optimierung der Prozessparameter mittels Reinforcement Learning (RL) auf Basis vorhandener Prozessdaten zu beschleunigen. Neben Gerberdaten zur Beschreibung der Lötstellengeometrie werden Bauteilcharakteristika und initiale Prozessparameter verwendet. RL ermöglicht eine gleichzeitige Modellierung und Optimierung des Prozesses, sodass Parameter empfohlen werden, welche das Erzielen eines hohen Lotdurchstiegs ermöglichen. Während der Anwendungsphase erlaubt das Framework über eine grafische Benutzeroberfläche eine Interaktion zwischen dem RL-Modell und dem Prozessexperten. Diese können gemeinsam basierend auf Trainingsdaten sowie Expertenwissen die Parameter schrittweise und zielgerichtet optimieren. Eine Validierung des Frameworks mittels Lötversuchen zeigt, dass bereits vor Beginn dieser Interaktion die empfohlenen Parameter für 75 % der getesteten Verbindungen einen Lotdurchstieg gemäß IPC-A-610 Klasse 3 [6] erzeuge