Within the profile Technical Information Technology (ICT Department) the most important specializations are Embedded Software and Industrial Automation. About half of the Technical Information curriculum consists of learning modules, the other half is organized in projects. The whole study lasts four years. After two-and-a-half year students choose a specialization. Before the choice is made students have several occasions in which they learn something about the possible fields of specialization. In the first and second year there are two modules about Industrial Automation. First there is a module on actuators, sensors and interfacing, later a module on production systems. Finally there is an Industrial Automation project. In this project groups of students get the assignment to develop the control for a scale model flexible automation cell or to develop a monitoring system for this cell. In the last year of their studies students participate in a larger Industrial Automation project, often with an assignment from Industry. Here also the possibility exists to join multidisciplinary projects (IPD; integrated product development).
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The present study aims at understanding and addressing certain challenges of automation of composite repairs. This research is part of a larger, SIA-RAAK funded project FIXAR, running in three Universities of Applied Sciences in the Netherlands and a cluster of knowledge institutions and industry partners.The approach followed in the current study, consists of three steps. First, the identification of the feasibility and most promising procedures for automated composite repair by analysis of current state-of-the-art methods as prescribed by OEMs and standards. Processes which are tedious or even contain health risks may qualify for automation. Second, a comparison of curing alternatives for composite repairs is made, by means of the creation and testing of specimen using different curing strategies. Lastly, a benchmark test of human made composite repairs is used in order to set a reference baseline for automation quality. This benchmark can be then applied to define a lower limit and prevent over-optimization. The employed methodology includes data collection, analysis, modelling and experiments.
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A description of our experiences with a model for education in innovative, interdisciplinary and international engineering. (Students from different (technical) disciplines in Higher Education are placed in industry for a period of eighteen months after completing two-and-a-half year of theoretical studies). They work in multi-disciplinary projects on different themes, in order to grow to fully equal employees in industry. Besides students, teachers and company employees participate in the projects. The involvement of other level students, both from University and from Vocational Education, is recommended. The experiments in practice give confidence in the succesful implementation of this model.
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The increasing amount of electronic waste (e-waste) urgently requires the use of innovative solutions within the circular economy models in this industry. Sorting of e-waste in a proper manner are essential for the recovery of valuable materials and minimizing environmental problems. The conventional e-waste sorting models are time-consuming processes, which involve laborious manual classification of complex and diverse electronic components. Moreover, the sector is lacking in skilled labor, thus making automation in sorting procedures is an urgent necessity. The project “AdapSort: Adaptive AI for Sorting E-Waste” aims to develop an adaptable AI-based system for optimal and efficient e-waste sorting. The project combines deep learning object detection algorithms with open-world vision-language models to enable adaptive AI models that incorporate operator feedback as part of a continuous learning process. The project initiates with problem analysis, including use case definition, requirement specification, and collection of labeled image data. AI models will be trained and deployed on edge devices for real-time sorting and scalability. Then, the feasibility of developing adaptive AI models that capture the state-of-the-art open-world vision-language models will be investigated. The human-in-the-loop learning is an important feature of this phase, wherein the user is enabled to provide ongoing feedback about how to refine the model further. An interface will be constructed to enable human intervention to facilitate real-time improvement of classification accuracy and sorting of different items. Finally, the project will deliver a proof of concept for the AI-based sorter, validated through selected use cases in collaboration with industrial partners. By integrating AI with human feedback, this project aims to facilitate e-waste management and serve as a foundation for larger projects.
Automation is a key enabler for the required productivity improvement in the agrifood sector. After years of GPS-steering systems in tractors, mobile robots start to enter the market. Localization is one of the core functions for these robots to operate properly on fields and in orchards. GNSS (Global Navigation Satellite System) solutions like GPS provide cm-precision performance in open sky, but buildings, poles and biomaterial may reduce system performance. On top, certain areas do not provide a dependable grid communication link for the necessary GPS corrections and geopolitics lead to jamming activities. Other means for localization are required for robust operation. VSLAM (Visual Simultaneous Localization And Mapping) is a complex software approach that imitates the way we as humans learn to find our ways in unknown environments. VSLAM technology uses camera input to detect features in the environment, position itself in that 3D environment while concurrently creating a map that is stored and compared for future encounters, allowing the robot to recognize known environments and continue building a complete, consistent map of the environment covered by its movement. The technology also allows continuous updating of the map in environments that evolve over time, which is a specific advantage for agrifood use cases with growing crops and trees. The technology is however relatively new, as required computational power only recently became available in tolerable cost range and it is not well-explored for industrialized applications in fields and orchards. Orientate investigates the merits of open-source SLAM algorithms on fields - with Pixelfarming Robotics and RapAgra - and in an orchard - with Hillbird - preceded by simulations and initial application on a HAN test vehicle driving in different terrains. The project learnings will be captured in educational material elaborating on VSLAM technology and its application potential in agrifood.
The automobile industry is presently going through a rapid transformation towards autonomous driving. Nearly all vehicle manufacturers (such as Mercedes Benz, Tesla, BMW) have commercial products, promising some level of vehicle automation. Even though the safe and reliable introduction of technology depends on the quality standards and certification process, but the focus is primarily on the introduction of (uncertified) technology and not on developing knowledge for certification. Both industry and governments see the lack of knowledge about certification, which can ensure the safety of autonomous technology and thus will guarantee the safety of the driver, passenger, and environment. HAN-AR recognized the lack of knowledge and the need for novel certification methodology for emerging vehicle technology and initiated the PRAUTOCOL project together with its SME partners. The PRAUTOCOL project investigated certification methodology for two use-cases: certification for automated highway overtaking pilot; and certification for automatic valet parking. The PRAUTOCOL research is conducted in two parallel streams: certification of the driver by human factors experts and certification of vehicle by technology experts. The results from both streams are published and presented in respective but limited target groups. Also, an overview of the PRAUTOCOL certification methodology is missing, which can enable its translation to different use-cases of automated technology (other than the used ones). Therefore, to realize a better pass-through of PRAUTOCOL's results to a broader audience, the top-up is required. Firstly, to write a (peer-reviewed) Open Access article, focusing on the application and translation of PRAUTOCOL's methodology to other automated technology use-cases. Secondly, to write a journal article, focusing on the validation of automatic highway overtaking system using naturalistic driving data. Thirdly, to organize a workshop to present PRAUTOCOL's results (valorization) to industrial, research, and government representatives and to discuss a follow-up initiative.