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Investigation of ductile/brittle chip formation zone in the context of manufactured geometry with different CAM paths strategies

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IFAC-PapersOnLine 2022年第10期55卷 2300-2305页

作者： László Móricz Zsolt Viharos János University of Pannonia Faculty of Engineering Mechatronic Education and Research Institute Zalaegerszeg Hungary Zalaegerszeg Center of Vocational Training ZSZC Ganz Ábrahám Technical School Institute for Computer Science and Control (SZTAKI) Centre of Excellence in Production Informatics and Control Center of Excellence of the Hungarian Academy of Sciences (HAS) Eötvös Loránd Research Network (ELKH) Budapest Hungary John von Neumann University Kecskemét Hungary

For maintenance conform processing of brittle materials (ceramics, glass), the resulting chips are not continuous, as in the case of soft and tough materials, it is called a brittle chip removal range. In this machining zone, the quality of the produced surface may decline (appears the cracks and pitting of the material) and the tool life also decreases. When defining the cutting technology (technological parameters, tool edges, tool cooling / lubrication, etc.), chip removal must be carried out in the ductile range. However, due to tool wear, this range may cross the transition and then it can work in the brittle chip formation zone. In this paper, the machinability of zirconia oxid ceramics has been investigated. The aim of the research is to find out the correlation between the wear of the cutting tool, the volume change of the manufactured pockets and the transitions between the chip formation ranges measured by cracks and pitting deformations on the edge of the machined material. It was observed that they overlap to a large extent, consequently, these observed phenomena can be easily applied for tool wear and quality monitoring for realising condition-based maintenance, it is the main scientific contribution of the paper.

关键词： Ductile/brittle chip formation zone ceramics micro-milling indirect tool wearing monitoring

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Tool failure recognition using inconsistent data

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Procedia CIRP 2022年 107卷 1204-1209页

作者： Júlia Bergmann Klaudia Éva Zeleny József Váncza Andrea Kő EPIC Center of Excellence in Production Informatics and Control Institute for Computer Science and Control (SZTAKI) Eötvös Loränd Research Network (ELKH) Budapest Hungary Eötvös Loränd University Doctoral School of Informatics Budapest Hungary EPIC InnoLabs Nonprofit Ltd. Budapest Hungary Corvinus University of Budapest Budapest Hungary Budapest University of Technology and Economics Dept. of Manufacturing Science and Technology Budapest Hungary

Data is everything - at least this is one of the main messages of the ongoing industrial revolution. Manufacturing companies all over the world are expanding their digital infrastructure and knowledge on data analysis in the hope of increasing their KPIs with the help of artificial intelligence (AI). Although several well-designed data-driven solutions are available, the most crucial part, data preparation is still not fully supported. In this paper a framework is presented for processing sensor data of machining processes with variable cycle times in an unstable environment. Traditional and novel AI algorithms are tested on the data of a vulcanization process from the automotive industry, namely from tire manufacturing’s curing phase. The process in question consists of several subprocesses, and the quality of curing is mostly dependent of the status of a specific type of machine tool. Conventional methods (e.g., examining the cured product manually) are currently used for failure recognition, however the examination is only feasible after a long delay due to the extreme level of heat, which leads to unnecessary and unwanted scrap production. Therefore, a more sophisticated and complex approach is required to increase quality score. A combination of mathematical methods is proposed combining t-SNE feature representation, convolutional neural network, and linear programming optimization. The model highly relies on the tool’s continuous degradation characteristics. The threshold for the given binary classification is set by maximizing the accuracy of the detection model. The main contribution of the research is the method of inconsistent sensor data manipulation which supports a unique combination of AI models for early failure recognition.

关键词： Type your keywords here separated by semicolons artificial intelligence failure detection data preparation t-SNE deep insight linear programming

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Identification of the nonlinear steering dynamics of an autonomous vehicle ⁎

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IFAC-PapersOnLine 2021年第7期54卷 708-713页

作者： G. Rödönyi G.I. Beintema R. Tóth M. Schoukens D. Pup Á. Kisari Zs. Vígh P. Körös A. Soumelidis J. Bokor Széchenyi István University Research Center of Vehicle Industry (SZE-JKK) H-9026 Egyetem tér 1. Győr Hungary Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) Control Systems Eindhoven University of Technology Eindhoven The Netherlands

Automated driving applications require accurate vehicle specific models to precisely predict and control the motion dynamics. However, modern vehicles have a wide array of digital and mechatronic components that are difficult to model, manufactures do not disclose all details required for modelling and even existing models of subcomponents require coefficient estimation to match the specific characteristics of each vehicle and their change over time. Hence, it is attractive to use data-driven modelling to capture the relevant vehicle dynamics and synthesise model-based control solutions. In this paper, we address identification of the steering system of an autonomous car based on measured data. We show that the underlying dynamics are highly nonlinear and challenging to be captured, necessitating the use of data-driven methods that fuse the approximation capabilities of learning and the efficiency of dynamic system identification. We demonstrate that such a neural network based subspace-encoder method can successfully capture the underlying dynamics while other methods fall short to provide reliable results.

关键词： Nonlinear system identification vehicle dynamics artificial neural networks nonparametric modelling

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Machine learning methods in Smartphone-Based Activity Recognition

Machine learning methods in Smartphone-Based Activity Recogn...

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International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Istvan Pintye Laboratory of Parallel and Distributed Systems Institute for Computer Science and Control (SZTAKI) Hungary

ISBN: (数字)9781728173771

ISBN: (纸本)9781728173788

In this paper, we present a system for human physical Activity Recognition (AR) using smartphone with embedded sensors. This paper addresses the question whether there is a comfortable way to predict human activities based on collected data from smartphone embedded gyroscope and accelerometer. Computational background of this work based on self-learning machine learning methods. In order to train the machine learning algorithms, The University of California, Irvine (UCI) dataset was used and the different models were compared. After selecting the best model further modifications were suggested in order to improve the accuracy of the model. At the end 96.88% accuracy was reached.

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Innovations in simulation: experiences with cloud-based simulation experimentation 20

Innovations in simulation: experiences with cloud-based simu...

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Proceedings of the Winter Simulation Conference

作者： Simon J. E. Taylor Anastasia Anagnostou Nura Tijjani Abubakar Tamas Kiss James DesLauriers Gabor Terstyanszky Peter Kacsuk Jozsef Kovacs Shane Kite Gary Pattison James Petry Brunel University London Uxbridge UK University of Westminster London UK Institute for Computer Science and Control (SZTAKI) Budapest HUNGARY Saker Solutions Ltd Alcester Warwickshire UK

ISBN: (纸本)9781728194998

The amount of simulation experimentation that can be performed in a project can be restricted by time, especially if a model takes a long time to simulate and many replications are required. Cloud Computing presents an attractive proposition to speeding up, or extending, simulation experimentation as computing resources can be hired on demand rather than having to invest in costly infrastructure. However, it is not common practice for simulation users to take advantage of this and, arguably, rather than speeding up simulation experimentation users tend to make compromises by using unnecessary model simplification techniques. This may be due to a lack of awareness of what Cloud Computing can offer. Based on several years' experience of innovation in this area, this article presents our experiences in developing Cloud Computing applications for simulation experimentation and discusses what future innovations might be created for the widespread benefit of our simulation community.

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Managing Fluctuations in Production via an Optimal Portfolio of Assembly Line Configurations

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Procedia CIRP 2024年 130卷 1372-1377页

作者： Daisuke Tsutsumi Júlia Bergmann Péter Dobrovoczki Naohiro Hayashi András Kovács Zsombor Szádoczki Ádám Szaller Hitachi Ltd. Research & Development Group 292 Yoshida-chyo Totsuka-ku Yokohama-shi Kanagawa 244-0817 Japan HUN-REN Institute for Computer Science and Control (SZTAKI) Budapest Hungary Eötvös Loránd University Doctoral School of Informatics Budapest Hungary EPIC InnoLabs Nonprofit Ltd. Budapest Hungary Eötvös Loránd University Doctoral School of Mathematics Budapest Hungary Department of Operations Research and Actuarial Sciences Corvinus University of Budapest Hungary

The traditional approach to mass production involves maintaining a consistent volume of the same product type for an extended period, minimizing fluctuations in parameters outlined in the process plan to ensure planned production efficiency. However, in mixed human-machine production lines, delays in human work and machine malfunctions are common, leading to significant fluctuations in assumed parameters and decreased throughput during the production control period. This research aims to develop technology for swiftly establishing production lines, accounting for fluctuations such as variations in task duration in mixed human-machine production lines (proactive planning approach). To manage configurations robustly, the approach involves pre-generating alternative configurations based on fluctuation scenarios and selecting and implementing suitable configurations to maximize throughput within budget constraints. Additionally, a novel technology is proposed to enable production continuity through flexible re-planning in response to operational fluctuations (reactive planning approach). The goal is to reduce the production preparation period and maintain target throughput during operation. Validation of the introduced method is demonstrated through computational analysis using an industrial case study simulating a battery assembly line for electric vehicles. The experiments results show throughput improvement even with the implementation of a small number of alternative configurations. This research contributes to enhancing production line adaptability and efficiency in the face of fluctuating operational conditions, ultimately improving overall manufacturing performance and competitiveness.

关键词： production line system configuration dynamic re-planning

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LPV-Fuzzy control approach for road adaptive semi-active suspension system

LPV-Fuzzy control approach for road adaptive semi-active sus...

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IEEE Symposium on Intelligent Vehicle

作者： Hakan Basargan Andrá s Mihá ly Pé ter Gá spá r Oliver Sename Department of Control for Transportation and Vehicle Systems BME Stoczek u. 2 Budapest Hungary Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) E&#x00F6 tv&#x00F6 s Lor&#x00E1 nd Research Network (ELKH) Kende utca 13-17 Budapest Hungary GIPSA-lab INPG Universit&#x00E9 Grenoble Alpes 11 Rue des Math&#x00E9 matiques Grenoble France

This paper proposed a road adaptive semi-active suspension control method, where a trade-off between driving comfort and vehicle stability/road-holding is accomplishable in order to achieve desirable performance results at different road irregularities and velocities by modifying the scheduling variable that is designed by Fuzzy Logic control. The proposed semi-active controller is founded on the Linear Parameter-Varying framework. Hungarian highway route data has been implemented into the TruckSim simulation environment based on real geographical data having road irregularities in order to compare the proposed adaptive method with a non-adaptive scenario. Simulation results show that all performances have been improved with the proposed method in different road irregularities and velocities.

关键词： Fuzzy logic Suspensions (mechanical systems) Adaptation models Adaptive systems Intelligent vehicles Roads Simulation

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Data-Driven Predictive control for Linear Parameter-Varying Systems ⁎

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IFAC-PapersOnLine 2021年第8期54卷 101-108页

作者： Chris Verhoek Hossam S. Abbas Roland Tóth Sofie Haesaert Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven 5600MB The Netherlands Institute for Electrical Engineering in Medicine Universität zu Lübeck 23558 Lübeck Germany Systems and Control Lab Institute for Computer Science and Control (SZTAKI) Kende u. 13-17 1111 Budapest Hungary

Based on the extension of the behavioral theory and the Fundamental Lemma for Linear Parameter-Varying (LPV) systems, this paper introduces a Data-driven Predictive control (DPC) scheme capable to ensure reference tracking and satisfaction of Input-Output (IO) constraints for an unknown system under the conditions that (i) the system can be represented in an LPV form and (ii) an informative data-set containing measured IO and scheduling trajectories of the system is available. It is shown that if the data set satisfies a persistence of excitation condition, then a data-driven LPV predictor of future trajectories of the system can be constructed from the IO data set and online measured data. The approach represents the first step towards a DPC solution for nonlinear and time-varying systems due to the potential of the LPV framework to represent them. Two illustrative examples, including reference tracking control of a nonlinear system, are provided to demonstrate that the data-based LPV-DPC scheme, achieves similar performance as LPV model-based predictive control.

关键词： Predictive control Data-Driven control Linear Parameter-Varying Systems Non-Parametric Methods

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Object localization utilizing 3D point cloud clustering approach

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Procedia CIRP 2020年 93卷 508-513页

作者： Gergely Horváth Gábor Erdős SZTAKI: Institute for Computer Science and Control Budapest 1111 Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest 1111 Hungary

In this paper we present a novel method to recognize multiple workpieces on a flat surface, based on a 3D point cloud and a prebuilt pose database. First, the method identifies the main surface, which is the base of every following steps. The surface determines the plane stretched by X and Y axis, establishing the orientation of the objects on it and helping their isolated recognition. Isolation of objects is followed by determining their specific type and exact pose, based on the prebuilt database. The presented method is demonstrated by recognizing workpieces and tool in an assembly cell.

关键词： identification object localization point cloud

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Gradient representations in ReLU networks as similarity functions 29

Gradient representations in ReLU networks as similarity func...

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29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021

作者： Rácz, Dániel Daróczy, Bálint Eötvos Loránd University Institute of Mathematics Pázmány Péter sétány 1/C BudapestH-1117 Hungary Université catholique de Louvain INMA ICTEAM Avenue Georges Lemaître 4 Louvain-la-Neuve B-1348 Belgium Institute for Computer Science and Control SZTAKI ELKH Kende utca 13-17 BudapestH-1111 Hungary

ISBN: (纸本)9782875870827

Feed-forward networks can be interpreted as mappings with linear decision surfaces at the level of the last layer. We investigate how the tangent space of the network can be exploited to refine the decision in case of ReLU (Rectified Linear Unit) activations. We show that a simple Riemannian metric parametrized on the parameters of the network forms a similarity function at least as good as the original network and we suggest a sparse metric to increase the similarity gap. © 2021 ESANN Intelligence and Machine Learning. All rights reserved.

关键词： Machine learning

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