Research on parallel distributed clustering algorithm applied to cutting parameter optimization

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Fältnamn	Indikatorer	Metadata
000		03351naa a2200649 4500
001		oai:DiVA.org:kth-323499
003		SwePub
008		230206s2022 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3234992 URI
024	7	a https://doi.org/10.1007/s00170-022-09252-72 DOI
040		a (SwePub)kth
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a art2 swepub-publicationtype
100	1	a Wei, X.4 aut
245	1 0	a Research on parallel distributed clustering algorithm applied to cutting parameter optimization
264		c 2022-05-05
264	1	b Springer Nature,c 2022
338		a print2 rdacarrier
500		a QC 20230206
520		a In the big data era, traditional data mining technology cannot meet the requirements of massive data processing with the background of intelligent manufacturing. Aiming at insufficient computing power and low efficiency in mining process, this paper proposes a improved K-means clustering algorithm based on the concept of distributed clustering in cloud computing environment. The improved algorithm (T.K-means) is combined with MapReduce computing framework of Hadoop platform to realize parallel computing, so as to perform processing tasks of massive data. In order to verify the practical performance of T.K-means algorithm, taking machining data of milling Ti-6Al-4V alloy as the mining object. The mapping relationship among cutting parameters, surface roughness, and material removal rate is mined, and the optimized value for cutting parameters is obtained. The results show that T.K-means algorithm can be used to mine the optimal cutting parameters, so that the best surface roughness can be obtained in milling Ti-6Al-4V titanium alloy.
650	7	a NATURVETENSKAPx Data- och informationsvetenskap0 (SwePub)1022 hsv//swe
650	7	a NATURAL SCIENCESx Computer and Information Sciences0 (SwePub)1022 hsv//eng
653		a Big data
653		a Cutting parameter optimization
653		a Data mining
653		a Distributed clustering
653		a MapReduce framework
653		a T.K-means algorithm
653		a Aluminum alloys
653		a K-means clustering
653		a Metadata
653		a Milling (machining)
653		a Parameter estimation
653		a Surface roughness
653		a Ternary alloys
653		a Titanium alloys
653		a Vanadium alloys
653		a Computing power
653		a Cutting parameters
653		a Cutting parameters optimizations
653		a Data mining technology
653		a Distributed clustering algorithm
653		a Intelligent Manufacturing
653		a Mapreduce frameworks
653		a Massive data
653		a TK-mean algorithm
700	1	a Sun, Q.4 aut
700	1	a Liu, X.4 aut
700	1	a Yue, C.4 aut
700	1	a Liang, S. Y.4 aut
700	1	a Wang, Lihuiu KTH,Produktionsutveckling4 aut0 (Swepub:kth)u1blju84
710	2	a KTHb Produktionsutveckling4 org
773	0	t The International Journal of Advanced Manufacturing Technologyd : Springer Natureg 120:11-12, s. 7895-7904q 120:11-12<7895-7904x 0268-3768x 1433-3015
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-323499
856	4 8	u https://doi.org/10.1007/s00170-022-09252-7

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Av författaren/redakt...: Wei, X.; Sun, Q.; Liu, X.; Yue, C.; Liang, S. Y.; Wang, Lihui

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