@article{MTMT:33668415, title = {Data Integration Framework to Collect Data from OT/IT Systems}, url = {https://m2.mtmt.hu/api/publication/33668415}, author = {Szűcs, Balázs}, journal-iso = {ACTA TECH JAURIN}, journal = {ACTA TECHNICA JAURINENSIS}, unique-id = {33668415}, issn = {1789-6932}, abstract = {Industry 4.0 and industrial data processing, due to its inherent possibilities, is gaining more and more emphasis in production companies these days. In a corporate environment, the age of equipment is extremely heterogeneous, in addition to state-of-the-art equipment, legacy systems can also be found in the machine park, which do not have appropriate communication protocols. Also, with the increase in the number of data sources, the management of data is becoming more and more challenging. Not only the operational technology, but also the connection of different IT systems and the extraction of data pose challenges. The different data processing use-cases using partly or entirely the same data sources, so it is necessary to extract and transmit the data to the target systems in a standard way, and avoiding an increase in the number of point-to-point interfaces. In this work we present a possible framework, to solve the above mentioned problems in industrial environment, with the introduction of standardized naming conventions, OT/IT gateways, data integration and distribution layers.}, keywords = {Data acquisition; Data integration; MQTT; Operational Technology; Industry 4.0}, year = {2023}, eissn = {2064-5228} } @misc{MTMT:33668428, title = {Machine learning based optimization of tool replacement strategy in machine tools}, url = {https://m2.mtmt.hu/api/publication/33668428}, author = {Szűcs, Balázs}, unique-id = {33668428}, year = {2021} } @article{MTMT:31795590, title = {Autoencoder based flexible industrial supervision system for process- and quality monitoring}, url = {https://m2.mtmt.hu/api/publication/31795590}, author = {Szűcs, Balázs and Ballagi, Áron}, journal-iso = {ACTA CYBERN-SZEGED}, journal = {ACTA CYBERNETICA}, unique-id = {31795590}, issn = {0324-721X}, abstract = {In the industrial process- and quality control systems the measurement data like press-in, fitting, screwing and other curves are often supervised with window functions. One drawback of these window functions are that they are cannot handle the deviation of the measurement data, thus cannot adjust dynamically or to as small as possible the window size and detect low magnitude anomalies inside the window function. The other drawback of the above mentioned technique is that it cannot detect anomalies outside the predefined window functions. In this paper we present a neural network based method for the monitoring of measurement data. The new method, in contrast with the classical approaches, which are using envelope test or window functions, the autoencoder based approach is capable to detect unexpected events and anomalies, which are cannot be pre-programmed. By applying the above mentioned method, a higher level of quality assurance can be achieved.}, keywords = {Artificial intelligence; quality control; manufacturing; Deep learning; neural networks (NNs); Autoencoders; supervision system}, year = {2021}, eissn = {2676-993X} } @misc{MTMT:31795573, title = {Challenges of the application of machine learning in the serial production}, url = {https://m2.mtmt.hu/api/publication/31795573}, author = {Szűcs, Balázs and Ballagi, Áron}, unique-id = {31795573}, abstract = {The industrial applications of the machine learning methods have promising results in the field of smart manufacturing and quality assurance; however, the development of these models have challenges. In this paper we present the classical steps of model creation and the challenges and questions of industrial applications in each step, especially for series production. Finally, we present a possible workflow for model development in the manufacturing.}, keywords = {intelligent manufacturing; machine learning; Quality assurance; Smart manufacturing; Industry 4.0.}, year = {2021} } @CONFERENCE{MTMT:31795561, title = {An Industrial Application of Autoencoders for Force-Displacement Measurement Monitoring}, url = {https://m2.mtmt.hu/api/publication/31795561}, author = {Szűcs, Balázs and Ballagi, Áron}, booktitle = {The 12th Conference of PhD Students in Computer Science}, unique-id = {31795561}, abstract = {The applications of artificial intelligence and neural networks in the industrial process monitoring and supervision are on the rise. One potential use case of these technologies are the anomaly detection in processes and measurements, without the need of pre-programming well defined patterns and supervision functions, thus unexpected events can be detected dynamically. In this paper we present a novel, neural network based method for the monitoring of press-in and joining processes. The new method, in contrast with the classical approaches, which are using envelope test or window functions, the autoencoder based approach is capable to detect unexpected events and anomalies, which are cannot be pre-programmed. By applying the above mentioned method, a higher level of quality assurance can be achieved. We present the new method through the example of force-displacement monitoring of mounting a sealing ring.}, keywords = {MONITORING; machine learning; Anomaly detection; neural networks (NNs); measurement; Autoencoders; Industry 4.0.}, year = {2020}, pages = {28} } @article{MTMT:31128205, title = {Artificial Intelligence in Maintenance: The Industrial Application of Natural Language Processing}, url = {https://m2.mtmt.hu/api/publication/31128205}, author = {Szűcs, Balázs and Dr. Ballagi, Áron}, journal-iso = {BÁNKI KÖZLEMÉNYEK}, journal = {BÁNKI KÖZLEMÉNYEK}, unique-id = {31128205}, issn = {2560-2810}, abstract = {Text- and natural language processing improved a lot in the recent years. Applications like translators, chatbots and virtual assistants made the everyday life easier, but the industrial use of the technology and its potential in the maintenance planning remained unexploited. In this paper we present two possible ways how to utilize these algorithms to achieve their positive benefits. For humans it’s often impossible to handle historical maintenance data and error logs due to their enormous amount or because the descriptions of errors are subjective. With the analysis of error messages, maintenance and shift logs, the correlations between failures and events can be detected, thus the effectiveness of the maintenance planning and the interventions can be increased.}, year = {2020} } @article{MTMT:30687828, title = {Reducing pseudo-error rate of industrial machine vision systems with machine learning methods}, url = {https://m2.mtmt.hu/api/publication/30687828}, author = {Szűcs, Balázs and Ballagi, Áron}, journal-iso = {ACTA TECH JAURIN}, journal = {ACTA TECHNICA JAURINENSIS}, volume = {12}, unique-id = {30687828}, issn = {1789-6932}, abstract = {Nowadays machine learning and artificial neural networks are hot topic. These methods gains more and more ground in everyday life. In addition to everyday usage, an increasing emphasis is placed on industrial use. In the field of research and development, materials science, robotics and thanks to the spread of Industry 4.0 and digitalization, more and more machine learning based systems are being introduced in production. This paper gives examples of possible ways of using machine learning algorithms in manufacturing, as well as reducing pseudo-error rate of machine vision quality control systems. Even the simplest algorithms and models can be very effective on real-world problems. With the usage of convolution neural networks the pseudo-error rate of the examined system can be reduced by 83 percent.}, keywords = {machine learning; Machine vision; Convolutional neural network; INDUSTRY 4.0}, year = {2019}, eissn = {2064-5228}, pages = {294-305} } @article{MTMT:30393053, title = {Gépi tanulás alkalmazási lehetőségei hibadetektálásra belsőégésű motorok összeszerelésénél}, url = {https://m2.mtmt.hu/api/publication/30393053}, author = {Szűcs, Balázs}, journal-iso = {BÁNKI KÖZLEMÉNYEK}, journal = {BÁNKI KÖZLEMÉNYEK}, unique-id = {30393053}, issn = {2560-2810}, abstract = {A gépi tanulás manapság egyre nagyobb teret hódít a hétköznapi életben. Az egészségügyi alkalmazásokon át, gépi látásban, ajánló rendszerekben, különböző virtuális asszisztensekben, beszédfelismerő, beszédszintetizáló, fordító alkalmazásokban, valamint a leghétköznapibb dolgokban is találkozhatunk gépi tanulás és mesterséges intelligencia algoritmusokkal. A mindennapi alkalmazásokon túl egyre nagyobb hangsúly kerül az ipari felhasználásra. A kutatás-fejlesztésben, az anyagtudományban, robotikában, illetve az Ipar 4.0 terjedésének és a digitalizációnak köszönhetően a gyártásban is egyre több gépi tanulásra épülő rendszer kerül bevezetésre. A különböző érzékelők, mérő átalakítók és mérőberendezések által előállított nagy adathalmaz kiváló kiindulási alapot biztosít a berendezések és termékjellemzők vizsgálatára, nem ismert összefüggések feltárásra. A tanulmányban példákon keresztül bemutatásra kerülnek a gépi tanulás algoritmusok lehetséges felhasználási módjai gyártásban, valamint egy hibadetektálási eljárás, melynek következtében a szerelősori kihozatal növelhető, illetve a kritikus szerelési hibák elkerülhetőek. A belsőégésű motorok főtengelyeinek átforgatási nyomaték méréseit gépi tanulás algoritmussal elemezve nem ismert összefüggések kerültek feltárásra, melyek alapján a szerelési folyamat korai szakaszában detektálható az idegen anyag a csapágyakon és a főtengelycsapokon, így elkerülhetőek az utómunka miatt feleslegessé váló további műveletek.}, keywords = {Mesterséges intelligencia; gyártás; big data; gépi tanulás}, year = {2019} }