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Rezaei, Ahmad; Nau, Johannes; Streitferdt, Detlef; Schambach, Jörg; Vangelov, Todor
ReProInspect: framework for reproducible defect datasets for improved AOI of PCBAs. - In: Engineering of computer-based systems, (2024), S. 205-214

Today, the process of producing a printed circuit board assembly (PCBA) is growing rapidly, and this process requires cutting-edge debugging and testing of the boards. The Automatic Optical Inspection (AOI) process detects defects in the boards, components, or solder pads using image processing and machine learning (ML) algorithms. Although state-of-the-art approaches for identifying defects are well developed, due to three main issues, the ML algorithms and datasets are incapable of fully integrating into industrial plants. These issues are privacy limitations for sharing data, the distribution shifts in the PCBA industry, and the absence of a degree of freedom for reproducible and modifiable synthetic datasets.
Boettcher, Konrad; Terkowsky, Claudius; Ortelt, Tobias R.; Aubel, Ines; Zug, Sebastian; Soll, Marcus; Haase, Jan; Meussen, Bernhard; Versick, Daniel; Finck, Matthias; Helbing, Pierre; Nau, Johannes; Streitferdt, Detlef
Work in progress - did you check it? : checklist for redesigning a laboratory experiment in engineering education addressing competencies of learning and working 4.0. - In: Open science in engineering, (2023), S. 601-609

Due to the possibilities of digitalization, the world of work is undergoing a profound change towards Industry 4.0 and related Learning and Working 4.0. In this context, new competences are expected from employees, which must also be addressed in STEM disciplines, especially in higher engineering education. Lab courses are particularly suitable for this, because here students can actively work on devices and potentially cyber-physical systems. This contribution is the undertaking of a group of lab teachers from various disciplines working on the joint project CrossLab to formulate what they consider the important aspects of lab teaching as learning outcomes for Industry 4.0. Furthermore, as a final goal, they will be transferred into a checklist that can be used in the implementation of existing and newly designed lab experiments with regard to the required competences of Learning and Working 4.0. Constructive Alignment forms the pedagogical framework, in which intended learning outcomes, teaching-learning activities and learning outcome monitoring must be thought through and planned as a whole. The checklist will extend an existing checklist for the thirteen conventional fundamental lab learning-objectives according to Feisel and Rosa. This work in progress describes first results of this attempt.
Rezaei, Ahmad; Nau, Johannes; Richter, Johannes; Streitferdt, Detlef; Schambach, Jörg
FACEE: framework for automating CNN explainability evaluation. - In: 2023 IEEE 47th Annual Computers, Software, and Applications Conference, (2023), S. 67-78

Convolutional Neural Networks (CNNs) are used mainly for image classification because of their high accuracy and fast performance. Due to their complexity, their functionality is like a black box to the human user. Hence, this black box functionality may classify an image based on the wrong features, which can lead to severe consequences in critical applications, such as disease detection. Therefore, explainer methods provide the users with the reasoning behind each classification. However, selecting well-matching pairs of CNN models and explainers is challenging.This paper proposes a framework for automated explainability evaluation in CNN models, which follows a quantitative approach for assessing the model and explainer pairs. The proposed framework supersedes the previous attempts towards an explainability evaluation framework by replacing the time-consuming qualitative measure with a unified quantitative metric. This quantitative approach allows the users to assess several models and explainer pairs and compare the results based on two aspects on the one hand, selecting the most prominent model/explainer pair or, on the other hand, making compromises for better real-time performance. The framework is applied to a defect detection problem for printed circuit board assembly(PCBA) automatic optical inspection (AOI). The results are analyzed for thirteen built-in CNN models and ten built-in explainer methods. The results demonstrate the superiority of the "CoAtNet0" and "Grad-Cam selfmatch" pair with the 55.38% Explanation Score (ES) metric. The authors also provide a discussion on other criteria in the selection of a prominent pair using the proposed novel FACEE framework.
Rezaei, Ahmad; Richter, Johannes; Nau, Johannes; Streitferdt, Detlef; Kirchhoff, Michael
Transparency and traceability for AI-based defect detection in PCB production. - In: Modelling and development of intelligent systems, (2023), S. 54-72

Automatic Optical Inspection (AOI) is used to detect defects in PCB production and provide the end-user with a trustworthy PCB. AOI systems are enhanced by replacing the traditional heuristic algorithms with more advanced methods such as neural networks. However, they provide the operators with little or no information regarding the reasoning behind each decision. This paper explores the research gaps in prior PCB defect detection methods and replaces these complex methods with CNN networks. Next, it investigates five different Cam-based explainer methods on eight selected CNN architectures to evaluate the performance of each explainer. In this paper, instead of synthetic datasets, two industrial datasets are utilized to have a realistic research scenario. The results evaluated by the proposed performance metric demonstrate that independent of the dataset, the CNN architectures are interpretable using the same explainer methods. Additionally, the Faster Score-Cam method performs better than other methods used in this paper.
Richter, Johannes;
Analyse und Entwicklung einer Softwarearchitektur für die intelligente, optische Inspektion. - Ilmenau : Universitätsverlag Ilmenau, 2023. - 1 Online-Ressource (ix, 205 Seiten)
Technische Universität Ilmenau, Dissertation 2022

Die automatische optische Inspektion ist das wichtigste Werkzeug der Qualitätskontrolle in der modernen Elektronikfertigung. Durch die automatisierte Bildaufnahme und das Ausführen vordefinierter Bildverarbeitungsschritte haben diese Systeme die manuelle optische Inspektion weitestgehend verdrängt. Trotz des großen Maßes an Automatisierung sind menschliche Experten an vielen Schritten der Prüfung unverzichtbar und damit potenzielle Fehlerquellen. In den letzten Jahren wurden zahlreiche Ansätze untersucht, welche einzelne Aspekte der optischen Qualitätssicherung durch die Anwendung von Methoden der künstlichen Intelligenz deutlich verbessern. Für den Wandel der optischen Inspektion hin zu einer verlässlichen und voll autonomen Prüfung wird in dieser Arbeit ein Modell mit fünf Phasen vorgestellt, welches die Entwicklungsschritte auf diesem Weg abbildet. Das neue Modell unterscheidet sich von bisherigen Ansätzen durch einen ganzheitlichen Blick auf die Qualitätskontrolle und die Berücksichtigung aller Prozessschritte. Um die Umsetzung dieses Modells zu tragen, zeigt diese Arbeit ein neues Architekturmuster auf, welches Lösungen auf Basis von künstlicher Intelligenz trainieren und ausführen kann. Durch seine hohe Flexibilität kann die neue Architektur über unterschiedliche Auslieferungen auf einer heterogenen Menge an Systemen angewendet werden und viele unterschiedliche Anwendungen von künstlicher Intelligenz über das Feld der optischen Inspektion hinaus umsetzen. Für die allgemeingültige Beschreibung von KI-Lösungen basiert diese Architektur auf einer Menge an Objekten, welche in dieser Arbeit definiert werden. Eine Umsetzung dieser Architektur wird diskutiert und ihre Anwendbarkeit anhand von drei Experimenten bewiesen. Die Implementierung der beschriebenen Architektur ist unter einer OpenSource-Lizenz veröffentlicht.
Nau, Johannes; Helbing, Pierre; Henke, Karsten; Streitferdt, Detlef
Latency resistant safety in distributed remote laboratories. - In: Artificial Intelligence and Online Engineering, (2023), S. 112-124

This work will focus on the problems of creating a safe distributed laboratory. We explicitly will not discuss how to make individual elements of an experiment safe, as this is highly application-dependent. Instead, the goal is to find and evaluate different methods to detect and respond to fault conditions that an individual laboratory device might detect. Specifically, the methods should differentiate between user-based faults and those introduced through network communications. We develop a mathematical model to simulate distributed laboratories. We will introduce (time-dependent) network latency and jitter between all elements. Based on the model, a discrete event simulation is created. This simulation environment simulates three different fault detecting methods: the token method, the timestamp method, and the full-state-transfer method. We will compare detection ratios, bandwidth usage, and memory usage between the three methods based on the simulation.
Henke, Karsten; Nau, Johannes; Streitferdt, Detlef
Hybrid Take-Home Labs for the STEM education of the future. - In: Smart Education and e-Learning - Smart Pedagogy, (2022), S. 17-26

The acceptance of digitally supported teaching has increased strongly in recent years - and not only due to Corona. In the STEM subjects, online labs are increasingly being used to ensure that the requirements for availability, usability and granularity of the offerings are met. This ensures the connection of theoretically taught fundamentals and their application and deepening in the form of practical courses in the basic subjects. However, practical experimentation and the associated haptic learning is somewhat lost as a result. The Hybrid Take-Home Labs project aims to develop and test the basis for practical support of learning processes in STEM subjects, which allows students to conduct even complex virtual and remote-controlled laboratory experiments from home using their own resources, combined as needed for student-centered teaching to meet the requirements of future-oriented competence-based learning. It is one of nine projects supported by the Thuringian Ministry of Economics, Science and Digital Society and the German Stifterverband.
Yaremchenko, Yevhenii; Nau, Johannes; Streitferdt, Detlef; Henke, Karsten; Parkhomenko, Anzhelika
Virtual environment smart house for hybrid laboratory GOLDi. - In: Mobility for smart cities and regional development - challenges for higher education, (2022), S. 250-257

The necessity to integrate virtual laboratories into the study process is becoming more significant, especially in pandemic time. Virtual based e-learning is seen as a reliable and effective support of teaching and learning process in different fields of study. The hybrid laboratory GOLDi uses the possibilities of remote and virtual experiments actively. At the same time, the implementation of the new experiment for teaching students in the area of Smart House systems will expand the functionality of the laboratory. The implementation of virtual experiments in the field of home automation systems provides an interactive learning environment that allows to engage students in an active educational process and increase their motivation to study modern information technologies and processes. The paper presents the results of the development of educational virtual environment for learning basics of Smart House systems development and control.
Nau, Johannes; Henke, Karsten; Streitferdt, Detlef
New ways for distributed remote web experiments. - In: Learning with technologies and technologies in learning, (2022), S. 257-284

Remote Laboratories are widely used in the education of stem subjects. While the first generation of remote labs was based on individually developed local experiments with an integrated web interface, the next generation combined multiple experiments in a remote laboratory management system, making it possible to share whole experiments between institutions. At the same time, with cheap hardware available more and more experiments are conducted by the students at home. The sharing of experiments is already a step towards a more prosperous learning environment. The next step is to collaboratively develop and operate experiments by offering parts of experiments that are coupled over the internet to execute the whole experiment. This form of remotely coupled experiments allows for better collaboration between different institutions and also has benefits within a single institution. By remixing the components, the curriculum can be adapted to changing teaching scenarios, especially when considering that components from other institutions might be used. Also, extensive or expensive apparatuses can be hosted by the institution while more mobile parts are given to the students creating a hybrid take-home lab which is an improvement compared to an all virtual or all remote laboratory in terms of immersion.
Aubel, Ines; Zug, Sebastian; Dietrich, André; Nau, Johannes; Henke, Karsten; Helbing, Pierre; Streitferdt, Detlef; Terkowsky, Claudius; Boettcher, Konrad; Ortelt, Tobias R.; Schade, Marcel; Kockmann, Norbert; Haertel, Tobias; Wilkesmann, Uwe; Finck, Matthias; Haase, Jan; Herrmann, Franziska; Kobras, Louis; Meussen, Bernhard; Soll, Marcus; Versick, Daniel
Adaptable digital labs - motivation and vision of the CrossLab project. - In: 2022 IEEE German Education Conference (GeCon), (2022), insges. 6 S.

The flexibility and performance of digital laboratory elements such as remote labs, VR/AR or simulations summarized under the term cross-reality labs (CrossLabs), can be seen with the development in last and has been proven under the pandemic situation. Even though the potential of cross-reality labs is obvious referring to availability and flexibility for the students, these didactic solutions remain isolated at universities as well as for individual users. The implementations are mostly so rigid that the individual didactic objectives are not interchangeable between different universities and disciplines, hence there is a lack of interoperability. The CrossLab project seeks to design didactical, technical, and organizational solutions for open digital lab objects linking student-centered teaching and a cross-university learning environment. Of importance thereby is the fact that teaching is not adaptable to the digital laboratory, but the laboratories are adaptable to the requirements of the teaching-learning setting. The four project partners are working on a cross-type and cross-element mixture of diverse types of laboratories for cross-disciplinary use in a cross-universities settings. Thereby, the project leans on existing digital laboratories in various disciplines to create an open teaching and learning environment which can be adapted to the needs of students and to provide students with the skills necessary for future working scenarios.