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    Wyświetlanie 1-4 z 4
    Tytuł:
    Potential of tool clamping surfaces in forming machines for cognitive production
    Autorzy:
    Alaluss, Mohaned
    Kurth, Robin
    Tehel, Robert
    Wagner, Martin
    Wagner, Nico
    Ihlenfeldt, Steffen
    Powiązania:
    https://bibliotekanauki.pl/articles/2142354.pdf
    Data publikacji:
    2022
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    industry 4.0
    sensor
    machine behavior
    forming machine
    Opis:
    High reproducibility of forming processes along with high quality expectations of the resulting formed parts demand cognitive production systems. The prerequisite is process transparency, which can be improved by increased knowledge of interdependencies between forming tool and forming machine that affects the tool clamping interface behavior. Due to the arrangement as surfaces transmitting process forces, their closeness to the forming process, and yet machine inherent, tool clamping interface provide greater potential for intelligent monitoring. This paper presents a holistic analysis of the interdependencies at the tool clamping interface. Here, the elastic deflection behavior of the press table and slide with their related clamping surfaces, the frictional slip behavior between the interacting machine components and the used clamping devices are described on qualitative level and verified by simulative analysis. Based on the results, available sensor systems are assessed regarding the capability to monitor the identified phenomena inline.
    Źródło:
    Journal of Machine Engineering; 2022, 22, 3; 116--131
    1895-7595
    2391-8071
    Pojawia się w:
    Journal of Machine Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Smart tool-related faults monitoring system using process simulation-based machine learning algorithms
    Autorzy:
    Ebrahimi Araghizad, Arash
    Tehranizadeh, Faraz
    Kilic, Kemal
    Budak, Erhan
    Powiązania:
    https://bibliotekanauki.pl/articles/28407322.pdf
    Data publikacji:
    2023
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    Industry 4.0
    machining
    machine learning
    monitoring
    Opis:
    In this paper a novel approach for monitoring tool-related faults in milling processes by utilizing process simulation-based machine learning algorithms, specifically Random Forest algorithms, for fault detection is presented. In order to train machine learning models in tool condition monitoring, laboratory tests have traditionally been required. This method eliminates the need for costly, time-consuming laboratory tests. The training process has been simplified by utilizing analytical simulation data and provides a more cost-effective solution by leveraging analytical simulation data. Based on the results of this study, the proposed approach has been demonstrated to be 94% accurate at predicting tool-related faults, demonstrating its potential to serve as an efficient and viable alternative to conventional methods. These findings have been supported by actual measurement data, with a notable accuracy rate of 93% in the predictions. Furthermore, the results indicate that process simulation-based machine learning algorithms will have a significant impact on the tools condition monitoring and the efficiency of manufacturing processes more generally. To further enhance the capabilities of the proposed fault monitoring system, process-related and machine-related faults will be investigated in future research. Several machine learning algorithms will be explored as well as additional data sources will be integrated in order to enhance the accuracy and reliability of fault detection.
    Źródło:
    Journal of Machine Engineering; 2023, 23, 4; 18--32
    1895-7595
    2391-8071
    Pojawia się w:
    Journal of Machine Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Seamless and modular architecture for autonomous machine tools
    Autorzy:
    Fleischer, Jürgen
    Puchta, Alexander
    Gönnheimer, Philipp
    Powiązania:
    https://bibliotekanauki.pl/articles/1833772.pdf
    Data publikacji:
    2021
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    machine tool
    cloud computing
    Industry 4.0
    edge computing
    Opis:
    In machine tools, existing solutions for process monitoring and condition monitoring rely on additional sensors or the machine control system as data sources. For a higher level of autonomy, it becomes necessary to combine several data sources, which may be within or outside of the machine. Another requirement for autonomy is additional computing power, which may be hosted on edge devices or in the cloud. A seamless and modular architecture, where sensors are integrated in smart machine components or smart sensors, which are in turn connected to edge devices and cloud platforms, provides a good basis for the incremental realisation of autonomy in all phases of the machine life cycle.
    Źródło:
    Journal of Machine Engineering; 2021, 21, 3; 40--46
    1895-7595
    2391-8071
    Pojawia się w:
    Journal of Machine Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Machine Learning in Cyber-Physical Systems and manufacturing singularity – it does not mean total automation, human is still in the centre: Part II – In-CPS and a view from community on Industry 4.0 impact on society
    Autorzy:
    Putnik, Goran D.
    Shah, Vaibhav
    Putnik, Zlata
    Ferreira, Luis
    Powiązania:
    https://bibliotekanauki.pl/articles/1428709.pdf
    Data publikacji:
    2021
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    human role
    artificial intelligence
    machine learning
    manufacturing singularity
    intelligent machine architecture
    cyber-physical systems
    Industry 4.0
    Opis:
    In many discourses, popular as well as scientific, it is suggested that the "massive" use of Artificial Intelligence (AI), including Machine Learning (ML), and reaching the point of "singularity" through so-called Artificial General Intelligence (AGI), and Artificial Super-Intelligence (ASI), will completely exclude humans from decision making, resulting in total dominance of machines over human race. Speaking in terms of manufacturing systems, it would mean that the intelligence and total automation would be achieved (once the humans are excluded). The hypothesis presented in this paper is that there is a limit of AI/ML autonomy capacity, and more concretely, the ML algorithms will be not able to become totally autonomous and, consequently, the human role will be indispensable. In the context of the question, the authors of this paper introduce the notion of the manufacturing singularity and present an intelligent machine architecture towards the manufacturing singularity, arguing that the intelligent machine will always be human dependent. In addition, concerning the manufacturing, the human will remain in the centre of Cyber-Physical Systems (CPS) and in Industry 4.0. The methodology to support this argument is inductive, similarly to the methodology applied in a number of texts found in literature, and based on computational requirements of inductive inference based machine learning. The argumentation is supported by several experiments that demonstrate the role of human within the process of machine learning. Based on the exposed considerations, a generic architecture of intelligent CPS, with embedded ML functional modules in multiple learning loops, is proposed in order to evaluate way of use of ML functionality in the context of CPS. Similar to other papers found in literature, due to the (informal) inductive methodology applied, considering that this methodology does not provide an absolute proof in favour of, or against, the hypothesis defined, the paper represents a kind of position paper. The paper is divided into two parts. In the first part a review of argumentation from literature in favour of and against the thesis on the human role in future was presented, as well as the concept of the manufacturing singularity was introduced. Furthermore, an intelligent machine architecture towards the manufacturing singularity was proposed, arguing that the intelligent machine will be always human dependent and, concerning the manufacturing, the human will remain in the centre. The argumentation is based on the phenomenon related to computational machine learning paradigm, as intrinsic feature of the AI/ML1, through the inductive inference based ML algorithms, whose effectiveness is conditioned by the human participation. In the second part, an architecture of the Cyber-Physical (Production) Systems (CPPS) with multiple learning loops is presented, together with a set of experiments demonstrating the indispensable human role. Finally, a discussion of the problem from the manufacturing community point of view on future of human role in Industry 4.0 as the environment for advanced AI/ML applications is registered.
    Źródło:
    Journal of Machine Engineering; 2021, 21, 1; 133-153
    1895-7595
    2391-8071
    Pojawia się w:
    Journal of Machine Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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