Autor: Uhlmann, E. - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Key Technologies for Sustainable Manufacturing
Autorzy:: Uhlmann, E.
Koenig, J.
Powiązania:: https://bibliotekanauki.pl/articles/971112.pdf
Data publikacji:: 2008
Wydawca:: Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:: sustainability
manufacturing
adaptive systems
Opis:: Innovative production techniques require an efficient utilization of human, material and energetic resources to ensure competitive manufacturing positions. The aim of modern industrial production processes is to provide products with a higher added value in a shorter time-to-market. On the other hand, shorter life cycles of products are contrary to the necessity of expanded service time of manufacturing systems. Moreover, the whole life cycle of products is accompanied by customer related service provisions. Flexible and adaptive as well as self-organising means of production are a considerable key to solve this conflict of objectives. Here, man with specific technological knowledge has to be integrated with it's permanently newly defined role in production. This paper presents production technology related new developments and strategies to fulfil the requirements of sustainable manufacturing.
Źródło:: Journal of Machine Engineering; 2008, 8, 1; 5-10
1895-7595
2391-8071
Pojawia się w:: Journal of Machine Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Intelligent production systems in the era of Industrie 4.0 – changing mindsets and business models
Autorzy:: Uhlmann, E.
Hohwieler, E.
Geisert, C.
Powiązania:: https://bibliotekanauki.pl/articles/99773.pdf
Data publikacji:: 2017
Wydawca:: Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:: Industrie 4.0
internet of things (IoT)
cyber-physical systems
Industrial Product-Service Systems
condition monitoring
predictive maintenance
Opis:: Industrie 4.0 has been becoming one of the most challenging topic areas in industrial production engineering within the last decade. The increasing and comprehensive digitization of industrial production processes allows the introduction of innovative data-driven business models using cyber-physical systems (CPS) and Internet of Things (IoT). Efficient and flexible manufacturing of goods assumes that all involved production systems are capable of fulfilling all necessary machining operations in the desired quality. To ensure this, production systems must be able to communicate and interact with machines and humans in a distributed environment, to monitor the wear condition of functionally relevant components, and to self-adapt their behaviour to a given situation. This article gives an overview about the historical development of intelligent production systems in the context of value-adding business models. The focus is on condition monitoring and predictive maintenance in an availability oriented business model. Technical as well as organizational prerequisites for an implementation in the production industry are critically analysed and discussed on the basis of best practice examples. The paper concludes with a summary and an outlook on future research topics that should be addressed.
Źródło:: Journal of Machine Engineering; 2017, 17, 2; 5-24
1895-7595
2391-8071
Pojawia się w:: Journal of Machine Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Smart wireless sensor network and configuration of algorithms for condition monitoring applications
Autorzy:: Uhlmann, E.
Laghmouchi, A.
Geisert, C.
Hohwieler, E.
Powiązania:: https://bibliotekanauki.pl/articles/99644.pdf
Data publikacji:: 2017
Wydawca:: Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:: condition monitoring
data analysis
sensor network
algorithm
MEMS sensor
cloud
Opis:: Due to high demand on availability of production systems, condition monitoring is increasingly important. In recent years, the technical development have improved for realization of condition monitoring applications as a result of technological progress in fields such as sensor technology, computer performance and communication technology. Especially, the approaches of Industrie 4.0 and the use of the Internet of Things (IoT) technologies offer high potential to implement condition monitoring solutions. The connection of several sensor data of components to the cloud allows the identification of anomalies or defect pattern, this information can be used for predictive maintenance and new data-driven business models in production industry. This paper illustrates a concept of a smart wireless sensor network for condition monitoring application based on simple electronic components such as the single-board computer Raspberry Pi 2 modules and MEMS (Micro-Electro-Mechanical Systems) vibration sensors and communication standards MQTT (Message Queue Telemetry Transport). The communication architecture used for decentralized data analysis using machine learning algorithms and connection to the cloud is explained. Furthermore, a procedure for rapid configuration of condition monitoring algorithms to classify the current condition of the component is demonstrated.
Źródło:: Journal of Machine Engineering; 2017, 17, 2; 45-55
1895-7595
2391-8071
Pojawia się w:: Journal of Machine Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: FEM modeling of hard turning with consideration of viscoplastic asymmetry and phase transformation
Autorzy:: Uhlmann, E.
Mahnken, R.
Ivanov, I. M.
Cheng, C.
Powiązania:: https://bibliotekanauki.pl/articles/99847.pdf
Data publikacji:: 2013
Wydawca:: Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:: hard turning
finite element method
production technology
process optimization
Opis:: A material model for strain rate and temperature dependent asymmetric plastic behavior accompanied by phase transformation induced plasticity (TRIP) as an important phenomenon in steel production and machining processes was developed. To this end the well-known Johnson-Cook flow stress model has been extended by the concept of weighting functions considering the asymmetric plastic material behavior under tensile, compressive and torsion load. Furthermore, the extended Johnson-Cook model has been combined with the Leblond approach regarding the ductility increase by transformation induced plasticity occurring during hard turning of AISI 52100. On the basis of the theoretical approach for calculating the flow stress with consideration of the viscoplastic asymmetry, a material routine for the FEM-software DEFORM has been implemented. The material and friction model coefficients have been determined in accordance with force and surface temperature measurements during hard turning of AISI 52100. The model takes the phase transformations between martensite and austenite and the influence of externally applied stress on the austenite start temperature into account.
Źródło:: Journal of Machine Engineering; 2013, 13, 1; 80-92
1895-7595
2391-8071
Pojawia się w:: Journal of Machine Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Intelligent pattern recognition of SLM machine energy data
Autorzy:: Uhlmann, E.
Pastl Pontes, R.
Laghmouchi, A.
Hohwieler, E.
Feitscher, R.
Powiązania:: https://bibliotekanauki.pl/articles/99469.pdf
Data publikacji:: 2017
Wydawca:: Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:: pattern recognition
data analysis
additive manufacturing
energy data
Opis:: Selective Laser Melting (SLM) is an additive manufacturing process, in which the research has been increasing over the past few years to meet customer-specific requirements. Different parameters from the process and the machine components have been monitored in order to obtain vital information such as productivity of the machine and quality of the manufactured workpiece. The monitoring of parameters related to energy is also realized, but the utilisation of such data is usually performed for determining basic information, for instance, from energy consumption. By applying machine learning algorithms on these data, it is possible to identify not only the steps of the manufacturing process, but also its behaviour patterns. Along with these algorithms, evidences regarding the conditions of components and anomalies can be detected in the acquired data. The results can be used to point out the process errors and component faults and can be adopted to analyse the energy efficiency of the SLM process by comparing energy consumption of one single layer during the manufacturing of different components. Moreover, the state of the manufacturing process and the machine can be determined automatically and applied to predict failures in order to launch appropriate counter measures.
Źródło:: Journal of Machine Engineering; 2017, 17, 2; 65-76
1895-7595
2391-8071
Pojawia się w:: Journal of Machine Engineering
Dostawca treści:: Biblioteka Nauki

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