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    Wyświetlanie 1-13 z 13
    Tytuł:
    Material identification during turning by neural network
    Autorzy:
    Denkena, Berend
    Bergmann, Benjamin
    Handrup, Miriam
    Witt, Matthias
    Powiązania:
    https://bibliotekanauki.pl/articles/99572.pdf
    Data publikacji:
    2020
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    machine learning
    turning
    monitoring
    Opis:
    A design concept for high-performance components involves the combination of different materials in hybrid workpieces. Different material properties and chemical compositions influence the machining quality of hybrid workpieces. To achieve a constant workpiece and process quality, it is necessary to adjust the process parameters to the individual material. Thus, it is mandatory to classify the material during machining for the relevant range of process parameters. This paper examines teaching strategies for neural networks to determine the machined material in process by a small amount of cross points. For this purpose, different training sets are compared. Process parameters with different cutting speeds, feeds and with constant and varying depth of cut are examined. In addition, the signal sources necessary for robust material classification are compared and investigated. The investigation is performed for the cylindrical turning of friction welded EN AW-6082/20MnCr5 shafts. The study shows that an F1 score of 0.99 is achieved at a constant cutting depth, provided that only the corner points of the process window and the machine control signals are used for training. With an additional variation of the cutting depth, the classification rate is significantly improved by the use of external sensors such as the acceleration sensor.
    Źródło:
    Journal of Machine Engineering; 2020, 20, 2; 65-76
    1895-7595
    2391-8071
    Pojawia się w:
    Journal of Machine Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of machine learning in the precise and cost-effective self-compensation of the thermal errors of CNC machine tools – a review
    Autorzy:
    Czwartosz, Robert
    Jedrzejewski, Jerzy
    Powiązania:
    https://bibliotekanauki.pl/articles/2142358.pdf
    Data publikacji:
    2022
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    CNC machine tool
    thermal error compensation
    machine learning
    self-learning
    Opis:
    The current development of production engineering takes place through the innovative improvement of machine tools and machining processes at the constantly growing application of intelligent self-improvement functions. Machine learning opens up possibilities for machine tool self-improvement in real time. This paper discusses the state of knowledge relating to the application of machine learning for precise and cost-effective thermal error self-compensation. Data acquisition and processing, models and model learning and self-learning methods are also considered. Three highly effective error compensation systems (supported with machine learning) are analysed and conclusions and recommendations for future research are formulated.
    Źródło:
    Journal of Machine Engineering; 2022, 22, 3; 59--77
    1895-7595
    2391-8071
    Pojawia się w:
    Journal of Machine Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Autonomous machining – recent advances in process planning and control
    Autorzy:
    Dittrich, Marc-André
    Denkena, Berend
    Boujnah, Haythem
    Uhlich, Florian
    Powiązania:
    https://bibliotekanauki.pl/articles/99503.pdf
    Data publikacji:
    2019
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    machine tool
    process planning
    adaptive machining
    machine learning
    Opis:
    While autonomous driving has come close to reality over the recent years, machining is still characterised by many manual tasks and prone to costly errors. In this article, an overview is given about the potential of autonomous machining and uprising technologies that support this vision. For that purpose, a definition of autonomous machine tools and the required elements is presented. Next, selected elements of an autonomous machine tool, e.g. sensory machine components and control loops, are discussed. Finally, some insights into ongoing research projects regarding the use of machine learning for process planning and control are given.
    Źródło:
    Journal of Machine Engineering; 2019, 19, 1; 28-37
    1895-7595
    2391-8071
    Pojawia się w:
    Journal of Machine Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Development of an automated assembly process supported with an artificial neural network
    Autorzy:
    Bobka, P.
    Heyn, J.
    Henningson, J.-O.
    Römer, M.
    Engbers, T.
    Dietrich, F.
    Dröder, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/99408.pdf
    Data publikacji:
    2018
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    assembly
    machine learning
    neural network
    industrial robot
    Opis:
    A central problem in automated assembly is the ramp-up phase. In order to achieve the required tolerances and cycle times, assembly parameters must be determined by extensive manual parameter variations. Therefore, the duration of the ramp-up phase represents a planning uncertainty and a financial risk, especially when high demands are placed on dynamics and precision. To complete this phase as efficiently as possible, comprehensive planning and experienced personnel are necessary. In this paper, we examine the use of machine learning techniques for the ramp-up of an automated assembly process. Specifically we use a deep artificial neural network to learn process parameters for pick-and-place operations of planar objects. We describe how the handling parameters of an industrial robot can be adjusted and optimized automatically by artificial neural networks and examine this approach in laboratory experiments. Furthermore, we test whether an artificial neural network can be used to optimize assembly parameters in process as an adaptive process controller. Finally, we discuss the advantages and disadvantages of the described approach for the determination of optimal assembly parameters in the ramp-up phase and during the utilization phase.
    Źródło:
    Journal of Machine Engineering; 2018, 18, 3; 28-41
    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:
    Automated root cause analysis of non-conformities with machine learning algorithms
    Autorzy:
    Mueller, T.
    Greipel, J.
    Weber, T.
    Schmitt, R. H.
    Powiązania:
    https://bibliotekanauki.pl/articles/99625.pdf
    Data publikacji:
    2018
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    root cause analysis
    machine learning
    decision tree
    simulation
    Opis:
    To detect root causes of non-conforming parts - parts outside the tolerance limits - in production processes a high level of expert knowledge is necessary. This results in high costs and a low flexibility in the choice of personnel to perform analyses. In modern production a vast amount of process data is available and machine learning algorithms exist which model processes empirically. Aim of this paper is to introduce a procedure for an automated root cause analysis based on machine learning algorithms to reduce the costs and the necessary expert knowledge. Therefore, a decision tree algorithm is chosen. A procedure for its application in an automated root cause analysis is presented and simulations to prove its applicability are conducted. In this paper influences affecting the success of detection are identified and simulated e.g. the necessary amount of data dependent on the amount of variables, the ratio between categories of non-conformities and OK parts as well as detectable root causes. The simulations are based on a regression model to determine the roughness of drilling holes. They prove the applicability of machine learning algorithms for an automated root cause analysis and indicate which influences have to be considered in real scenarios.
    Źródło:
    Journal of Machine Engineering; 2018, 18, 4; 60-72
    1895-7595
    2391-8071
    Pojawia się w:
    Journal of Machine Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An investigation of the relationship between encoder difference and thermo-elastic machine tool deformation
    Autorzy:
    Brecher, Christian
    Dehn, Mathias
    Neus, Stephan
    Powiązania:
    https://bibliotekanauki.pl/articles/24084708.pdf
    Data publikacji:
    2023
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    machine tool
    thermal error compensation
    machine learning
    artificial neural network
    Opis:
    New approaches, using machine learning to model the thermo-elastic machine tool error, often rely on machine internal data, like axis speed or axis position as input data, which have a delayed relation to the thermo-elastic error. Since there is no direct relation to the thermo-elastic error, this can lead to an increased computation inaccuracy of the model or the need for expensive sensor equipment for additional input data. The encoder difference is easy to obtain and has a direct relationship with the thermo-elastic error and therefore has a high potential to improve the accuracy thermo-elastic error models. This paper first investigates causes of the encoder difference and its relationship with the thermo-elastic error. Afterwards, the model is presented, which uses the encoder difference to compute the thermo-elastic error. Due to the complexity of the relationship, it is necessary, to use a machine learning approach for this. To conclude, the potential of the encoder difference as an input of the model is evaluated.
    Źródło:
    Journal of Machine Engineering; 2023, 23, 3; 26--37
    1895-7595
    2391-8071
    Pojawia się w:
    Journal of Machine Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Offline-online pattern recognition for enabling time series anomaly detection on older NC machine tools
    Autorzy:
    Netzer, Markus
    Palenga, Yannic
    Goennheimer, Philipp
    Fleischer, Juergen
    Powiązania:
    https://bibliotekanauki.pl/articles/1428705.pdf
    Data publikacji:
    2021
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    data provision
    anomaly detection
    machine learning
    manufacturing
    condition monitoring
    Opis:
    Intelligent IoT functions for increased availability, productivity and component quality offer significant added value to the industry. Unfortunately, many old machines and systems are characterized by insufficient, inconsistent IoT connectivity and heterogeneous parameter naming. Furthermore, the data is only available in unstructured form. In the following, a new approach for standardizing information models from existing plants with machine learning methods is described and an offline-online pattern recognition system for enabling anomaly detection under varying machine conditions is introduced. The system can enable the local calculation of signal thresholds that allow more granular anomaly detection than using only single indexing and aims to improve the detection of anomalous machine behaviour especially in finish machining.
    Źródło:
    Journal of Machine Engineering; 2021, 21, 1; 98-108
    1895-7595
    2391-8071
    Pojawia się w:
    Journal of Machine Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A hybridization of machine learning and NSGA-II for multi-objective optimization of surface roughness and cutting force in AISI 4340 alloy steel turning
    Autorzy:
    Nguyen, Anh-Tu
    Nguyen, Van-Hai
    Le, Tien-Thinh
    Nguyen, Nhu-Tung
    Powiązania:
    https://bibliotekanauki.pl/articles/2200263.pdf
    Data publikacji:
    2023
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    multi-objective optimisation
    machine learning
    AISI 4340
    NSGA-II
    ANN
    Opis:
    This work focuses on optimizing process parameters in turning AISI 4340 alloy steel. A hybridization of Machine Learning (ML) algorithms and a Non-Dominated Sorting Genetic Algorithm (NSGA-II) is applied to find the Pareto solution. The objective functions are a simultaneous minimum of average surface roughness (Ra) and cutting force under the cutting parameter constraints of cutting speed, feed rate, depth of cut, and tool nose radius in a range of 50–375 m/min, 0.02–0.25 mm/rev, 0.1–1.5 mm, and 0.4–0.8 mm, respectively. The present study uses five ML models – namely SVR, CAT, RFR, GBR, and ANN – to predict Ra and cutting force. Results indicate that ANN offers the best predictive performance in respect of all accuracy metrics: root-mean-squared-error (RMSE), mean-absolute-error (MAE), and coefficient of determination (R2). In addition, a hybridization of NSGA-II and ANN is implemented to find the optimal solutions for machining parameters, which lie on the Pareto front. The results of this multi-objective optimization indicate that Ra lies in a range between 1.032 and 1.048 μm, and cutting force was found to range between 7.981 and 8.277 kgf for the five selected Pareto solutions. In the set of non-dominated keys, none of the individual solutions is superior to any of the others, so it is the manufacturer's decision which dataset to select. Results summarize the value range in the Pareto solutions generated by NSGA-II: cutting speeds between 72.92 and 75.11 m/min, a feed rate of 0.02 mm/rev, a depth of cut between 0.62 and 0.79 mm, and a tool nose radius of 0.4 mm, are recommended. Following that, experimental validations were finally conducted to verify the optimization procedure.
    Źródło:
    Journal of Machine Engineering; 2023, 23, 1; 133--153
    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
    Tytuł:
    Cutting force prediction of Ti6Al4V using a machine learning model of SPH orthogonal cutting process simulations
    Autorzy:
    Klippel, Hagen
    Sanchez, Eduardo Gonzalez
    Isabel, Margolis
    Röthlin, Matthias
    Afrasiabi, Mohamadreza
    Michal, Kuffa
    Wegener, Konrad
    Powiązania:
    https://bibliotekanauki.pl/articles/2052187.pdf
    Data publikacji:
    2022
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    machining
    Ti6Al4V
    machine learning
    SPH
    smoothed particle hydrodynamics
    meshfree method
    Opis:
    The prediction of machining processes is a challenging task and usually requires a large experimental basis. These experiments are time-consuming and require manufacturing and testing of different tool geometries at various process conditions to find optimum machining settings. In this paper, a machine learning model of the orthogonal cutting process of Ti6Al4V is proposed to predict the cutting and feed forces for a wide range of process conditions with regards to rake angle, clearance angle, cutting edge radius, feed and cutting speed. The model uses training data generated by virtual experiments, which are conducted using physical based simulations of the orthogonal cutting process with the smoothed particle hydrodynamics (SPH). The ML training set is composed of input parameters, and output process forces from 2500 instances of GPU accelerated SPH simulations. The resulting model provides fast process force predictions and can consider the cutter geometry in comparison to classical analytical approaches.
    Źródło:
    Journal of Machine Engineering; 2022, 22, 1; 111-123
    1895-7595
    2391-8071
    Pojawia się w:
    Journal of Machine Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Operator integrated – concept for manufacturing intelligence
    Autorzy:
    Wegener, Konrad
    Weikert, Sascha
    Mayr, Josef
    Maier, Markus
    Ali Akbari, Vahid Ostad
    Postel, Martin
    Powiązania:
    https://bibliotekanauki.pl/articles/2052207.pdf
    Data publikacji:
    2021
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    machine tool
    communication
    self-learning
    autonomy
    artificial operator
    Opis:
    Increasing autonomy and sustainability are major goals in manufacturing. Main technological trends provide enablers for achieving these goals and need to be implemented and combined in manufacturing machinery in a suitable manner. The paper exposes a vision of modern manufacturing machines, where the complexity of manufacturing processes is handled within the manufacturing machine and a simplistic front end is presented to the operator, which means that major elements of operators’ tasks are fulfilled by the intelligence of the machine. Research vectors paving the ground for this concept from different points of view are then discussed. Research is presented on intelligent grinding, intelligent recognition and suppression of chatter, adaptive thermal and motion error compensation exploting also self-learning abilities. It is necessary to point out, that not only intelligent mastering of process and machine becomes more and more important but communications among machine tools enabling process chain overarching intelligent approaches and creating intelligent factories.
    Źródło:
    Journal of Machine Engineering; 2021, 21, 4; 5-28
    1895-7595
    2391-8071
    Pojawia się w:
    Journal of Machine Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Discussion of Machine Tool Intelligence, Based on Selected Concepts and Research
    Autorzy:
    Jędrzejewski, J.
    Kwaśny, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/99824.pdf
    Data publikacji:
    2015
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    machine tool
    intelligence
    learning
    intelligent avoiding
    collisions
    trajectories
    real time
    Opis:
    This paper discusses the need to create intelligent machine tools, the expected properties of such machine tools and the benefits they will bring to business and the development of manufacturing. The basis for machine tool intelligence, and the evolution of machine tools towards the development of their intelligent functions, with a special emphasis on control, digitization and virtualization systems, the role of the STP-NC standard, and the prediction of machine tool operating properties, are presented. The arising concepts of the development of intelligent machine tool functions, which take into account the monitoring and planning of a collisionless tool path, are discussed. The approach to achieving real time and the intelligent machine tool is described for copy milling. Also the directions of implementing intelligent functions by machine tool manufacturers are presented using the MAZAK Corporation as an example.
    Źródło:
    Journal of Machine Engineering; 2015, 15, 4; 5-26
    1895-7595
    2391-8071
    Pojawia się w:
    Journal of Machine Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-13 z 13

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