Research on parallel distributed clustering algorithm applied to cutting parameter optimization

• 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. 

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Nyckelord

Big data

Cutting parameter optimization

Data mining

Distributed clustering

MapReduce framework

T.K-means algorithm

Aluminum alloys

K-means clustering

Metadata

Milling (machining)

Parameter estimation

Surface roughness

Ternary alloys

Titanium alloys

Vanadium alloys

Computing power

Cutting parameters

Cutting parameters optimizations

Data mining technology

Distributed clustering algorithm

Intelligent Manufacturing

Mapreduce frameworks

Massive data

TK-mean algorithm

Publikations- och innehållstyp

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