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False calls are an undesired occurrence in manufacturing of printed circuit boards that have to be verified manually after the automated optical inspection

Analysis of the Suitability of Inkjet-Printed Ag-Particle-Filled Inks for Use in Connections of Fine-Pitch Electrical Components - Voigt, Christian; Petersen, Matthias; Thielen, Nils; Utsch, Daniel; Häußler, Felix; Kirchberger, Manfred; Franke, Jörg (2023 46th International Spring Seminar on Electronics Technology (ISSE), 2023)
This paper explores a method for contacting Surface Mount Devices (SMD) using inkjet-printed media. A series of experiments is conducted to determine the needed layer thickness, drop size, and print parameters to facilitate adequate connections and evaluates the mechanical forces needed to break such a connection. Analysis of the joining partners using laser scan microscopy indicates necessary ink volumes between 75.1*10 3 and 800*10 3 μm 3 of connective media depending on the SMD size. The necessary amount of material is delivered to the substrate surface using inkjet printing, with the appropriate dwell voltage being determined in a separate experiment. After sintering for 10 minutes at 150°C, the inkjet-printed connections demonstrated electrical conduction in testing but showed 43% less resistance to shear forces, when compared to conventionally soldered connections, indicating a need for further optimization regarding the adhesive properties of the ink. The study provides insights for developing mechanically and electrically stable connections using inkjet-printed media, although further refinement is needed with regards to ink formulation and sintering tim

An Automated Optical Inspection System for PIP Solder Joint Classification Using Convolutional Neural Networks - Schmidt, Konstantin; Rauchensteiner, Daniel; Voigt, Christian; Thielen, Nils; Bönig, Jochen; Beitinger, Gunter; Franke, Jörg (IEEE, 2021)
In the fields of electronics manufacturing, the application of through-hole devices is still required, as heat dissipation and high current carrying capacity plays an important role. To ensure the highest quality standards, these electronics production processes take a multitude of inspection processes into account. For the detection of error patterns regarding the quality of the solder connections, usually, high-end inspection machines are utilized in the industrial application. The Automated Optical Inspection is a commonly conducted process, using visible light and rule-based inspection routines, setup by process experts for the evaluation of the Region of Interest. The high overhead of creating and maintaining product-specific checking routines and machine acquisition leads to increased costs and severe dependency on expert know-how. A flexible inspection algorithm, implemented into low-cost equipment for image generation is expected to reduce acquisition and optimization costs, and lower dependency on expert knowledge and high-end machinery. In this contribution, we present a novel framework for the automatic, near real-time solder joint classification based on Convolutional Neural Networks, flexibly detecting, and classifying solder connections. We utilize existing Deep Learning architectures for detection and classification. The localization model utilizes a YOLO-architecture (you-only-look-once), learning feature inputs based on a supervised learning approach. Pseudo-labeling is carried out automatically by an anomaly detection model. The image generation is executed by an industrial low-cost camera and an industrial rack-PC. The developed prototype is integrated into the existing production infrastructure. The results indicate a satisfactory detection and classification of the investigated solder connections with the proposed system. Hence, this system represent an alternative to commercially available high-end inspection systems being used for an inline control of Pin-in-Paste and through-hole device solder connections.
Keywords: Location awareness; Deep learning; Image synthesis; Process control; Production; Optical imaging; Real-time systems; electronics production; optical quality inspection; machine learning; computer vision; Convolutional Neural Networks; pseudo-labeling

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

Clustering of Image Data to Enhance Machine Learning Based Quality Control in THT Manufacturing - Thielen, Nils; Jiang, Zonghan; Schmidt, Konstantin; Seidel, Reinhardt; Voigt, Christian; Reinhardt, Andreas; Franke, Jörg (2021 IEEE 27TH INTERNATIONAL SYMPOSIUM FOR DESIGN AND TECHNOLOGY IN ELECTRONIC PACKAGING (SIITME 2021), 2021)
In this work, machine learning (ML) models are presented to identify false calls during quality control with automated optical inspection (AOI) in through hole technology (THT) manufacturing. While ML-based approaches with both, image data and numerical data, have already been investigated extensively in SMT manufacturing due to the higher market share, research for THT manufacturing does not have the same extend [1]. The presented models classify images into false calls and true defects, which were identified by the AOI as defects beforehand. Since the AOI uses different test routines to control pin, meniscus and foil of the different components and the board’s surface, multiple models for groups of images are developed to achieve better performance than a single one. To assign the images to the corresponding models, clustering of image data is done in two steps. First, the dataset is divided into subcategories based on the supplementary and descriptive data on the test routine. Second, the unsupervised machine learning algorithm k-means is used on images in each subgroup for further assignment to a dataset. A cumulative examination of the results of different convolutional neural networks (CNN) on the individual clusters leads to a relative improvement in false call detection of 6.8% while error slip can be reduced from 0.6% to 0% in an independent test data set, which is not used for model training.<br
CRIS-Team WoS Importer:2022-05-06
Keywords: Electronics Production; Through Hole Technology; Machine Learning; Automated Optical Inspection; Quality Control

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

Data-Driven Bed of Nails Wear Analysis for the in-Circuit-Testing of Electronic Modules - Sindel, Till; Thielen, Nils; Mahr, Felix; Reichenstein, Tobias; Erdogan, Hüseyin; Franke, Jörg (2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA), 2024)
In order to optimize the timing of maintenance operations, this work aims at laying the foundation for applying predictive maintenance strategies for the In-Circuit-Test (ICT) by proposing a data analysis pipeline for capturing wear impact on process data. The ICT is an established inspection process within the electronic module production. Subsequently to the assembly of components on a printed circuit board (PCB) mainly using the surface mount technology (SMT) and through hole technology (THT), parts are tested for their conformity of electrical characteristics to predefined tolerances, as well as logical functionalities. Thereby, the ICT is conducted by physically connecting an adapter containing a bed of nails to the circuits of the assembled part and subsequently measuring component dependent characteristics such as the electrical resistance, impedance, inductivity and capacity. Due to the mechanical and electrical impact on the probes and the high-volume nature of the process, these temporarily connected components are subject to wear. Conventional maintenance approaches follow a scheduled preventive strategy by conducting maintenance operations following a predefined number of cycles or strokes. Due to the heuristic nature of the scheduling, the wear margin is not fully exploited and therefore additional costs for prematurely replacing the probes or the complete bed of nails occur. The findings for a real-world dataset from high-volume electronics production show temporal shifts in data distributions between maintenance operations, suggesting adapter-wide needle degeneration. Considering PCB and component batch effects is proposed for successive research methodologies in order to improve the accuracy of the approac

Data Mining System Architecture for Industrial Internet of Things in Electronics Production - Seidel, Reinhardt; Hassan Amada, Mohamadou; Fuchs, Jonathan; Thielen, Nils; Schmidt, Konstantin; Voigt, Christian; Franke, Jörg (2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging (SIITME), 2020)
Data collection and Machine Learning (ML) have already become reality in industrial applications. Also in electronics manufacturing, some successful approaches for the application of ML have been reported. However, for industrial applications, the infrastructure and the respective analysis-models for such approaches need to cope with the occurring data flow and structured storage in production. This contribution presents a cross-vendor data mining infrastructure setup that allows realtime tracking and structured storage of process data during operation in order to complement the material tracking of manufacturing execution systems (MES). Hence, the developed data mining system is the essential basis for real-time prediction use cases of ML in manufacturing.<br
Keywords: Manufacturing; Production; Servers; TCPIP; Data mining; Sockets; Protocols; Machine learning; Smart electronics manufacturing; Smart electronic module assembly; Data analytics; Electronics manufacturing

Development and Test of a Data Framework for Prediction of Soldering Quality in Selective Wave Soldering Applying K-Nearest Neighbors - Seidel, Reinhardt; Thielen, Nils; Schmidt, Konstantin; Voigt, Christian; Franke, Jörg (2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging (SIITME), 2020)
Machine Learning has been proven to be a powerful tool to model and predict complex applications. Selective wave soldering is a widely applied interconnection technology for THT components. It is mostly used if components are not substitutable by surface mount devices due to high thermal load or mechanical stress. Especially in power electronic circuit boards, large copper layers or high thermal mass components lead to critical soldering situations. This paper suggests a Machine Learning framework to identify thermally challenging solder joints. The hybrid approach consisting of an analytical thermal description of THT components and solder joints in the multilayer circuit board and the ML analysis allows the prediction of arbitrarily complex solder joint configurations. The data framework represents electronic components and solder joints. Utilizing solder joint, component and soldering process parameters as input, the K-Nearest Neighbors algorithm predicts the probable hole fill following IPC-A-610 with an overall accuracy of about 75%.<br

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

Ein Ansatz zu Qualitätsvorhersage mittels intelligenter SMT-Lötstelleninspektion durch den Einsatz von Maschinellem Lernen - Schmidt, Konstantin; Bönig, Jochen; Beitinger, Gunter; Thielen, Nils; Franke, Jörg (GMM-Fb. 94: EBL 2020 – Elektronische Baugruppen und Leiterplatten, 2020)
The success factors of modern, global competition are changing the framework conditions of industrial production in a sustainable way. The optimization of the manufacturing process of electronic assemblies must ensure that they meet the requirements for adapted strategies and new design concepts, in particular in the field of digitization. Due to the already low error rates, which are achieved by highly optimized manufacturing and testing processes, future process improve-ments must be realized by reducing non-value-adding processes without endangering error rates. This can be reached through the targeted evaluation of recorded operating and machine data and the use of machine learning procedures. Due to the high standardization of processes and advanced plant networking, electronics manufacturing offers high application potential for data-driven process optimization. The state of the art often provides for cost- and time-intensive X-ray in-spection of hidden solder joints. According to the literature, the majority of solder joint defects are already caused by solder paste printing. This article therefore describes an approach for using the inspection data of the solder paste inspec-tion for quality prediction of the X-ray process. For the training of the model, the optical measurements are used as characteristics, together with the corresponding quality labels of the X-ray inspection, to establish a relationship between the solder paste printing and the quality result of the X-ray inspection for a selected product. The results were evaluated on the basis of various metrics and analysed for transferability into practice.<br
Keywords: Machine Learning; SMT; Manufacturing; X-Ray

Enhanced X-Ray Inspection of Solder Joints in SMT Electronics Production using Convolutional Neural Networks - Schmidt, Konstantin; Thielen, Nils; Voigt, Christian; Seidel, Reinhardt; Franke, Jörg; Milde, Yannik; Bonig, Jochen; Beitinger, Gunter (2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging (SIITME), 2020)
The electronics production is prone to a multitude of possible failures along the production process. Therefore, the manufacturing process of surface-mounted electronics devices (SMD) includes visual quality inspection processes for defect detection. The detection of certain error patterns like solder voids and head in pillow defects require radioscopic inspection. These high-end inspection machines, like the X-ray inspection, rely on static checking routines, programmed manually by the expert user of the machine, to verify the quality. The utilization of the implicit knowledge of domain expert(s), based on soldering guidelines, allows the evaluation of the quality. The distinctive dependence on the individual qualification significantly influences false call rates of the inbuilt computer vision routines. In this contribution, we present a novel framework for the automatic solder joint classification based on Convolutional Neural Networks (CNN), flexibly reclassifying insufficient X-ray inspection results. We utilize existing deep learning network architectures for a region of interest detection on 2D grayscale images. The comparison with product-related meta-data ensures the presence of relevant areas and results in a subsequent classification based on a CNN. Subsequent data augmentation ensures sufficient input features. The results indicate a significant reduction of the false call rate compared to commercial X-ray machines, combined with reduced product-related optimization iterations.<br
Keywords: Inspection; Training; Soldering; X-ray imaging; Production; Image classification; Object detection; electronics production; automated X-ray inspection; machine learning; computervision; Convolutional Neural Networks

Experimental Investigation on Thermocouple Attachment Methods for Reliable Temperature Measurement - Seidel, Reinhardt; Volland, Jana; Thielen, Nils; Voigt, Christian; Franke, Jörg (ISSE 2020, 2020)
Keywords: Temperature Measurement Reliability; Thermocouples; Design of Experiments; Soldering oven profiling

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 Approach towards Quality Control of Aerosol-Jet Printed Polymer Optical Waveguides Material - Hamjah, Mohd Khairulamzari; Thielen, Nils; Hagelloch, Jan Erik; Franke, Jörg (TENSYMP 2021 - 2021 IEEE Region 10 Symposium, 2021)
In this work, a Machine Learning approach to predict the quality of the aerosol jet printed polymer optical waveguides material is presented. For the acquisition of necessary data, a video camera system was integrated into the aerosol jet printing. An image processing tool was developed to extract printed line width and to map to the process data of the printing. For modeling, an Automated Machine Learning framework was utilized to reduce development time for a proof of concept. The final ML model reached R2 score of 74 % in predicting line width and surpassed the statistical approaches.
CRIS-Team Scopus Importer:2021-10-29
Keywords: Aerosol jet printing; Machine Learning; polymer optical waveguides

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

Maschinelles Lernen in der Elektronikproduktion: Herausforderungen und Anwendungsmöglichkeiten - Thielen, Nils; Schmidt, Konstantin; Seidel, Reinhardt; Franke, Jörg (GMM-Fb. 94: EBL 2020 – Elektronische Baugruppen und Leiterplatten, 2020)
Aufgrund der fortschreitenden Miniaturisierung sowie zunehmenden Anforderungen an Zuverlässigkeit und Leistung, steigt auch der weitere Optimierungsbedarf in der Oberflächenmontage (SMT). Der erreichte Standard erschwert diese Optimierung jedoch. Der hohe Automatisierungsgrad in der SMT-Produktion und die bereits existierenden großen Mengen an Prozess- und Inspektionsdaten erleichtern die Anwendung von Methoden Künstlicher Intelligenz (KI), wie Maschinellem Lernen (ML) und Computer Vision. Um Stillstandszeiten von Anlagen zu reduzieren, die Qualitätskontrolle zu verbessern und Werkzeuge zur Prozessanpassungen zu integrieren, können datenbasierte Verfahren nicht triviale Zusammenhänge von Prozess- und Anlagenparametern erkennen. Dieses Paper gibt eine Übersicht von ML-basierten Modellen, ihren Vor- und Nachteilen, sowie Grenzen in der Elektronikproduktion. Des Weiteren wird eine Literaturübersicht zu bereits veröffentlichten Forschungsansätzen vorgestellt. Diese werden in Abhängigkeit zu ihren Anwendungsfeldern und ihrer Relevanz zur Prozessoptimierung in verschiedene Kategorien unterteilt. Im letzten Teil des Papers werden Anwendungsfälle und die damit verbundenen Herausforderungen betrachtet. Diese schließen bedarfsorientierte Reinigungen von Maschinen, Vorhersagemodelle für Fehler und Klassifizierung von Defekten sowie deren Ursachen mit ein. Die zugehörige Auswertung fokussiert den Aufwand der Modellimplementierung, Möglichkeiten für anlagen- und linienübergreifende Kommunikation und einen Vergleich mit etablierter statistischer Prozesskontrolle. Aufbauend wird die Einsatzmöglichkeit der ML-Modelle für den entsprechenden Anwendungsfall in der Elektronikproduktion bewertet.<br
Keywords: Machine Learning; SMT; Electronics Production

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

Optimizing Machine Learning Performance via Dataset Generation for X-ray Image Classification - Mahr, Felix; Schmidt, Konstantin; Thielen, Nils; Sindel, Till; Franke, Jörg (2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE), 2024)
Automated X-ray inspection (AXI) plays a crucial role in ensuring the quality of electronic modules, especially post Surface Mount Technology (SMT) manufacturing. However, the presence of pseudo errors complicates accurate defect classification, necessitating efficient solutions like machine learning (ML) automation. This paper explores the impact of various dataset characteristics on ML model performance enhancement. Leveraging 2D grayscale X-ray images classified into “OK” (pseudo errors) and “not OK” (real errors), this methodology employs statistical tests to detect data drift over time. The hypothesis is that ML models trained on datasets with temporal and grayscale similarities exhibit superior performance. A dataset generation pipeline is proposed, integrating quality validation, grayscale segmentation, and temporal cropping, aiming to enhance model performance. While these optimized datasets demonstrate enhanced performance, they require frequent retraining due to reduced generalization. To address this, advocacy is made for a retraining pipeline and the recommendation is to utilize statistical tests for detecting data drift, enabling timely model retraining. These investigations reveal that datasets optimized for temporal and grayscale congruence significantly enhance classification accuracy. Additionally, the study demonstrates improvements in performance metrics: accuracy (99.03% to 99.63%), precision (98.59% to 99.75%), recall (97.21% to 99.33%), and F1-score (97.9% to 99.54%). The study highlights the importance of dataset optimization for improving the reliability of ML-based AXI-reclassification and suggests practical strategies for maintaining model efficiency over time X.<br
Keywords: Micromechanical devices; Measurement; Pipelines; Surface mount technology; Machine learning; Gray-scale; Microelectronics

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

Prediction of the Solder Rise in Selective Wave Soldering Comparing Decision Tree and Logistic Regression - Seidel, Reinhardt; Leibold, Fabian; Thielen, Nils; Franke, Jörg (2020)

Thermische Beurteilung von THT-Lötstellendesigns für die IPC-konforme Kontaktierung durch Selektivwellenlöten - Seidel, Reinhardt; Thielen, Nils; Voigt, Christian; Franke, Jörg (EBL 2020, 2020)

Trustworthiness of machine learning models in manufacturing applications using the example of electronics manufacturing processes - Seidel, Reinhardt; Schmidt, Konstantin; Thielen, Nils; Franke, Jörg (Procedia CIRP, 2022)
The design phase of products decides about a large portion of their costs. Both applied manufacturing technologies and systems are ultimately defined by the products that have to be assembled. Due to global trends towards e-mobility and regenerative energy, electronic printed circuit boards with high copper content and mixed SMT/THT assembly is the predominant assembly scenario. This leads to thermally challenging soldering operations in THT-soldering as high copper masses dissipate the required heat and no reliable manufacturability check is available. Consequently, in electronic manufacturing complex thermal processes have still to be defined in iterative experimental steps. Reliable models have great potential to reduce time-to-market and scrap during new product introduction. Future manufacturing systems require infrastructure that allows manufacturability checks in the early design phase and automatically provides process programs on the shop floor once the design is finished. However, the lack of availability, long-term experience and consequently trust in automatically, by artificial intelligence generated and optimized process programs, blocks the realization and hinders further productivity gain. In this paper, an ML audit methodology is suggested that mitigates this lack of trust by providing physically plausible prediction series and quantitative risk assessment to the user. This complements typical evaluation scores and adds the model behavior in the process context and transmits a sense of transparency. A neural network is trained with an augmented dataset, exemplary for the prediction of the hole fill in mini wave soldering process. The model is then used to predict series of predictions for increasing soldering time and temperatures comparable to design of experiments. The resulting time versus hole fill plots helps the operator understanding the behavior of the model and thus, demonstrate the potential benefit of this approach to the entire manufacturing site.
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Keywords: Barrel fill; Design for manufacturing; Fontain soldering; Lead-free; Selective wave; Through hole technology; Transperent artificial intelligence