Experimental and fuzzy modelling analysis on dynamic cutting force in micro milling

Ren Qun , Marek Bałaziński , Krzysztof Jemielniak , Luc Baron , Sofiane Achiche

Abstract

Prediction of cutting forces is very important for the design of cutting tools and for process planning. This paper presents a fuzzy modelling method of cutting forces based on subtractive clustering. The subtractive clustering combined with the least-square algorithm identifies the fuzzy prediction model directly from the information obtained from the sensors. In the micro-milling experimental case study, four sets of cutting force data are used to generate the learning systems. The systems are tested against each other to choose the best model. The obtained results prove that the proposed solution has the capability to model the cutting force in spite of uncertainties in the micromilling process.
Author Ren Qun - Mechanical Engineering Department [École Polytechnique de Montréal (POLYMTL)]
Ren Qun,,
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, Marek Bałaziński - Mechanical Engineering Department, École Polytechnique de Montréal [Polytechnique Montréal]
Marek Bałaziński,,
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, Krzysztof Jemielniak (FPE / IoMP)
Krzysztof Jemielniak,,
- The Institute of Manufacturing Processes
, Luc Baron - Mechanical Engineering Department, École Polytechnique de Montréal [Polytechnique Montréal]
Luc Baron,,
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, Sofiane Achiche - Mechanical Engineering Department [École Polytechnique de Montréal (POLYMTL)] [Polytechnique Montréal]
Sofiane Achiche,,
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Journal seriesSoft Computing, ISSN 1432-7643
Issue year2013
Vol17
No9
Pages1687-1697
Publication size in sheets0.5
Keywords in Englishfuzzy logic, cutting force, subtractive clustering, micromilling, fuzzy modelling
ASJC Classification2608 Geometry and Topology; 2614 Theoretical Computer Science; 1712 Software
DOIDOI:10.1007/s00500-013-0983-0
URL http://link.springer.com/article/10.1007/s00500-013-0983-0
Languageen angielski
Score (nominal)25
Score sourcejournalList
ScoreMinisterial score = 25.0, 31-01-2020, ArticleFromJournal
Ministerial score (2013-2016) = 25.0, 31-01-2020, ArticleFromJournal
Publication indicators Scopus Citations = 19; Scopus SNIP (Source Normalised Impact per Paper): 2013 = 1.45; WoS Impact Factor: 2013 = 1.304 (2) - 2013=1.492 (5)
Citation count*36 (2020-09-02)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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