Anomaly Detection for Dispensing of Solder Paste on 3D Circuit Carriers Using Machine Learning - Thielen, Nils; Wagner, Marco; Meier, Sven; Voigt, Christian; Franke, Jörg (Proceedings of the 25th Electronics Packaging Technology Conference, EPTC 2023, 2023)
This paper addresses the challenges in manufacturing of 3D mechatronic integrated devices, particularly focusing on the anomalies that occur during the transfer of solder paste as an interconnection material. Utilizing machine learning techniques, the study extends prior research done on 2D circuit carriers to identify these anomalies in 3D substrates and predicts solder paste depot quality in general. The research employs a design of experiments to develop a comprehensive dataset, taking into account the unique properties of 3D substrates. The paper also presents a low-cost camera and segmentation algorithm setup to measure quality parameters like the area and offset of solder paste, as conventional systems fall short in this context. The system has a very good measurement capability for determining the area of the solder paste. Based on the data obtained, transfer learning techniques are utilized to leverage existing 2D data for model training, showing a significant improvement in model performance and especially reducing training times. Specifically, an R2 score of 0.88 is achieved using transfer learning, compared to 0.83 with only 3D data, while an AutoML-model even reaches an R2 score of 0.94. The study suggests that AI-based methods offer cost-effective alternatives to established systems, enabling flexible manufacturing with fewer iterations and lower setup costs.
CRIS-Team Scopus Importer:2024-12-16

A process model for systematically setting up the data basis for data-driven projects in manufacturing - Meier, Sven; Klarmann, Steffen; Thielen, Nils; Pfefferer, Christian; Kuhn, Marlene; Franke, Jörg (Journal of Manufacturing Systems, 2023)
In the rapidly advancing fields of Artificial Intelligence (AI) and Big Data, creating a robust and high-quality data foundation is a critical requirement for data-driven projects. However, the lack of a standard procedure for ensuring the existence of a sufficient and high-quality data basis often leads to misunderstandings, inefficiencies, and resource waste, resulting in a high risk of project failure. Existing methodologies often presuppose the availability of a data basis, which is a significant challenge, particularly in the manufacturing sector with its diverse and complex data sources. This challenge is further compounded by the interdisciplinary nature of these projects, where domain experts and data scientists with different expertise and vocabularies must collaborate. Addressing this gap, this paper introduces ML-SIPOC, a novel methodology for creating a standardized and high-quality data basis for data-driven projects. ML-SIPOC builds upon the traditional SIPOC analysis from the Six Sigma management system for operational excellence, adapted to meet the unique challenges of data-intensive projects. It provides a structured framework for systematically building a data foundation, facilitating effective communication between domain experts and data scientists. When applied in electronics manufacturing, ML-SIPOC proved efficient in creating a robust data basis, reducing time and cost overheads. This approach minimizes reliance on prior knowledge for data collection, opening up possibilities for broader AI and Big Data applications across various manufacturing sectors. The key innovation of this paper is the introduction of a first-of-its-kind methodology that provides a structured approach to building a data foundation for data-driven decision making in manufacturing.
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Keywords: Data mining; Electronics production; Industry 4.0; Intelligent manufacturing; Machine learning; Six Sigma

Attention-based Image Compression in Sensor Assembly - Meier, Sven; Erkan, Acelya; Thielen, Nils; Klarmann, Steffen; Franke, Jörg (2022 IEEE 28th International Symposium for Design and Technology in Electronic Packaging, SIITME 2022 - Conference Proceedings, 2022)
The rapid development of the Industrial Internet of Things (IIoT) has led to an explosion in the amount of data to be transmitted and stored in the industrial sector. The investigated images are used in particular for quality control after individual process steps as part of Automated Optical Inspections (AOI). Image processing systems are among the key technologies here. Nevertheless, the storage and transmission of image data is still a major challenge for many companies. The use of AI-based image compression methods with higher quality performance compared to conventional methods becomes inevitable. Convolutional Neural Networks (CNNs) or Transformers are currently gaining ground in this area. However, the efficiency of these architectures has not yet been demonstrated in an industrial scenario. In this work, both a CNN- and a Transformer-based model with built-in Attention Mechanisms are trained with 8 bits per pixel (BPP) grayscale images generated in an industrial ultrasonic sensor production line. Even in the first test phase, in which only 2,000 images were used for model training, a compression rate of 0.162 BPP is achieved, far exceeding the quality of comparable JPEG-compressed images. When training the models with 100,000 images, the lowest BPP rate of 0.009 is achieved by the Transformer-based algorithm, resulting in an image quality that is sufficient for most quality assessment use cases. Thus, our results demonstrate the applicability and superiority of AI-based compression algorithms in industry. Their use leads not only to more efficient data transmission, but also to huge savings in storage space and costs for companies compared to traditional image compression methods.
CRIS-Team Scopus Importer:2023-03-07
Keywords: Attention-based image compression; Big Data; Electronics Production; Machine Vision; Vision Transformer

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

Implementation of a Camera System for the Application of Artificial Intelligence in Monitoring the Cold Atmospheric Plasma Spray Process - Ockel, Manuela; Meier, Sven; Stelter, Oliver; Thielen, Nils; Bründl, Patrick; Franke, Jörg (2024 32nd Mediterranean Conference on Control and Automation (MED), 2024)
Monitoring of the complex cold atmospheric plasma spraying process currently relies on technologically advanced systems, that often measure only a single parameter. This research introduces a more integrated approach, that uses a comprehensive set of parameters, consolidated in the characteristics of the plasma flame, is used to accurately classify particle temperature and coating materials. A simple optical camera system and image analysis based on a transfer learning model predicts with an 85% success rate. This approach simplifies and complements the monitoring process. Accuracy could be increased by using additional machine data already recorded, which could allow a complete replacement of additional the plasma flame monitoring.<br
Keywords: Temperature measurement; Temperature sensors; Temperature; Atmospheric measurements; Transfer learning; Process control; Fires; Cold Atmospheric Plasma Spray; Artificial Intelligence; Optical Process Monitoring; Electronics Production

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

Machine Learning Based Quality Prediction for Solder Paste Dispensing in Electronics Production - Thielen, Nils; Pan, Wenzhe; Piechulek, Niklas; Voigt, Christian; Meier, Sven; Schmidt, Konstantin; Franke, Jörg (Proceedings of the 24th Electronics Packaging Technology Conference, EPTC 2022, 2022)
Transfer of solder paste for manufacturing of electronic assemblies is a crucial process step for final product quality. While the most common technologies are stencil- and screen-printing, dispensing of solder paste can be seen as an alternative, especially for three-dimensional circuit carriers and small-scale production. Nonetheless, dispensing lacks in process speed and suffers from anomalies in the process, such as insufficient solder paste transfer. In this paper, we investigated a data-driven approach to predict quality of these solder depots in terms of height, area and volume including both process data and previous dispensing quality itself. This work contains three steps. First, a suitable data set was generated by means of Design of Experiment and the dispensing steps based on it. The data was first statistically analyzed. For the model development primarily using automated machine learning (AutoML) is chosen as an approach, in order to reduce model development time and hyperparameter tuning. Furthermore, a deep neural network is trained as a comparison. The performance of the AutoML model ensemble outperforms both the neural network and the statistical benchmark for all quality parameters considered: height, area and volume of the deposited solder paste. The best models offer an R2 score of 0.71 for height, 0.89 for area and 0.81 for solder paste volume, on a separated test dataset. Thus, indicating strong correlation.
CRIS-Team Scopus Importer:2023-03-07

Modeling glass transition temperatures of epoxy systems: a machine learning study - Meier, Sven; Albuquerque, Rodrigo Q.; Demleitner, Martin; Ruckdaeschel, Holger (Journal of Materials Science, 2022)
Abstract: The use of machine learning (ML) models to screen new materials is becoming increasingly common as they accelerate material discovery and increase sustainability. In this work, the chemical structures of 16 epoxy resins and 19 curing agents were used to build an ML ensemble model to predict the glass transition (Tg) of 94 experimentally known thermosets. More than 1400 molecular descriptors were calculated for each molecule, of which 119 were chosen based on feature selection performed by principal component analysis. The quality of the trained model was evaluated using leave-one-out cross-validation, which yielded a mean absolute error of 16.15∘C and an R2 value of 0.86. The trained model was also used to predict Tg for 4 randomly selected resin/hardener combinations for which no experimental data were available. The same combinations were then prepared and measured in the laboratory to further validate the ML model. Excellent agreement was found between experimental and predicted Tg values. The current ML model was created using only theoretical features, but could be further improved by adding experimental or quantum mechanical properties of the individual molecules as well as experimental processing parameters. The results presented here contribute to improving sustainability and accelerating the discovery of novel materials with desired target properties. Graphical Abstract: [Figure not available: see fulltext.]
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Multi-Model Machine Learning based Industrial Vision Framework for Assembly Part Quality Control - Schwab, Maximilian; Madeline-Derou, Charles; Klarmann, Steffen; Thielen, Nils; Meier, Sven; Franke, Jörg; Chintanippu, Sandan; Stork, Wilhelm (IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2022)
The predicted drop in prices for automotive sensors and their increasing demand are putting pressure on sensor suppliers. One possible solution is to reduce production costs by expanding automation. Nevertheless, visual quality control in particular is a process step that is often performed by human inspectors, even in the age of Industry 4.0. Software solutions can currently not be used for all types of sensor assembly quality control. This is mainly due to the difficulty of detecting both structural and logical errors and evaluating their severity. We present a machine learning-based software framework that is able to mimic the methodical behavior of a human in error detection and assessment. The framework is based on three types of models, an object recognition model, an anomaly detection model, and a segmentation model. All models are based on convolutional neural networks. An initial proof of concept (PoC) has been performed to prove the usefulness of the models and shows promising results. The initial anomaly detection model is able to reduce the number of objects to be manually tested by 16%. The object detection and segmentation are still in progress and could not be evaluated yet. In addition, a dataset preparation method is presented to use data from industrial practice and relabel it with information from an inspector survey.
CRIS-Team Scopus Importer:2022-11-25
Keywords: anomaly detection; assembly part; autoencoder; machine learning; object detection; quality control; segmentation

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

Optimizing Semantic Search in Industrial Knowledge Retrieval: A Novel SHAP-Based Attention Mask Modification Approach - Mahr, Felix; Freese, Gerhard; Meier, Sven; Kratsch, Christina; Raum, Christoph; Thielen, Nils; Franke, Jörg; Risch, Florian (2025 26th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE), 2025)
Knowledge management in industrial settings faces significant challenges due to information overload and knowledge loss. This investigation examines a RetrievalAugmented Generation (RAG) solution for quality reports at Siemens Electronics Works, where engineers currently spend significant time searching through 4,500+ technical documents, resulting in annual costs of approximately C=200,000. We developed an optimized data preprocessing pipeline for semantic search that combines generalizable components (duplicate removal, HTML cleaning, chunking strategies) with novel domain-specific enhancements. Our key innovation is a systematic approach integrating Shapley Additive exPlanations (SHAP) with attention mask modification (AMM) to identify and downweight tokens that disproportionately influence search results. This method, combined with LLM-based contextual enhancement of domain-specific abbreviations and semantic chunking, achieved a 21.1 % improvement in retrieval accuracy compared to raw data processing, as measured by Mean Reciprocal Rank (MRR). The resulting RAG application operates locally without internet access, ensuring data security while significantly reducing document retrieval time. Our methodology provides a framework for optimizing semantic search in specialized technical domains without requiring extensive model retrainin
Keywords: Micromechanical devices; Technological innovation; Systematics; Semantic search; Data security; Pipelines; Data preprocessing; Knowledge management; Microelectronics; Faces