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Wyszukujesz frazę "energy manufacturing" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Energy and Carbon Footprint Analysis for Machiningtitanium Ti-6Al-4V Alloy
Autorzy:
Rajemi, M. F.
Mativenga, P. T.
Jaffery, S. I.
Powiązania:
https://bibliotekanauki.pl/articles/971220.pdf
Data publikacji:
2009
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
energy footprin
energy carbon
machining
sustainable manufacturing
Opis:
Titanium alloys are increasingly being used in manufacturing especially in aerospace industries. The environmental impact of using this material is rarely discussed especially with regards to energy consumption and its contribution to carbon emissions. The poor machinability of titanium leads to lower material removal rate and longer machining time. Coupled with high carbon footprints encountered, in extracting this material from ore, it is clear that the environmental impact of using this material needs to be optimised. In the research reported here, cutting tests were undertaken on a lathe and milling machine using unified cutting conditions. The associated energy and carbon footprints were analysed and discussed with emphasis on high speed machining. The paper clearly shows the impact of process choice and cutting speed on environmental footprints as a key performance measure in sustainable manufacturing.
Źródło:
Journal of Machine Engineering; 2009, 9, 1; 103-112
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
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
Artykuł
    Wyświetlanie 1-2 z 2

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