Using artificial intelligence to predict surface roughness in deep drilling of steel components

被引：46
作者：
Bustillo, Andres [1 ]
Correa, Maritza [2 ,3 ]
机构：
[1] Univ Burgos, Dept Civil Engn, Burgos 09006, Spain
[2] Ctr Automat & Robot CAR UPM CSIC, Madrid 28500, Spain
[3] Univ Autonoma Madrid, Escuela Politecn Super, E-28049 Madrid, Spain
来源：
JOURNAL OF INTELLIGENT MANUFACTURING | 2012年 / 23卷 / 05期
关键词：
Deep drilling; Bayesian networks; Supervised classification; Minimum quantity lubrication (MQL); Surface roughness; MINIMUM QUANTITY; TOOL WEAR; LUBRICANT; QUALITY; LIFE; MQL; DRY;
D O I：
10.1007/s10845-011-0506-8
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A predictive model is presented to optimize deep drilling operations under high speed conditions for the manufacture of steel components such as moulds and dies. The input data include cutting parameters and axial cutting forces measured by sensors on the milling centres where the tests are performed. The novelty of the paper lies in the use of Bayesian Networks that consider the cooling system as an input variable for the optimization of roughness quality in deep drilling operations. Two different coolant strategies are tested: traditional working fluid and MQL (Minimum Quantity Lubrication). The model is based on a machine learning classification method known as Bayesian networks. Various measures used to assess the model demonstrate its suitability to control this type of industrial task. Its ease of interpretation is a further advantage in comparison with other artificial intelligence tools, which makes it a user-friendly application for machine operators.
引用
收藏
页码：1893 / 1902
页数：10
相关论文
共 36 条
  • [11] Castillo E., 1997, Expert Systems and Probabilistic Network Models, V493, P543
  • [12] Application of soft computing techniques in machining performance prediction and optimization: a literature review
    Chandrasekaran, M.
    Muralidhar, M.
    Krishna, C. Murali
    Dixit, U. S.
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2010, 46 (5-8) : 445 - 464
  • [13] SMOTE: Synthetic minority over-sampling technique
    Chawla, Nitesh V.
    Bowyer, Kevin W.
    Hall, Lawrence O.
    Kegelmeyer, W. Philip
    [J]. 2002, American Association for Artificial Intelligence (16)
  • [14] Data mining in manufacturing: a review based on the kind of knowledge
    Choudhary, A. K.
    Harding, J. A.
    Tiwari, M. K.
    [J]. JOURNAL OF INTELLIGENT MANUFACTURING, 2009, 20 (05) : 501 - 521
  • [15] APPROXIMATING DISCRETE PROBABILITY DISTRIBUTIONS WITH DEPENDENCE TREES
    CHOW, CK
    LIU, CN
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 1968, 14 (03) : 462 - +
  • [16] Rapid machining of hardened AISI H13 and D2 moulds, dies and press tools
    Coldwell, H
    Woods, R
    Paul, M
    Koshy, P
    Dewes, R
    Aspinwall, D
    [J]. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2003, 135 (2-3) : 301 - 311
  • [17] A Bayesian network model for surface roughness prediction in the machining process
    Correa, M.
    Bielza, C.
    Ramirez, M. de J.
    Alique, J. R.
    [J]. INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2008, 39 (12) : 1181 - 1192
  • [18] Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process
    Correa, M.
    Bielza, C.
    Pamies-Teixeira, J.
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (03) : 7270 - 7279
  • [19] Experimental studies on drilling of aluminium (AA1050) under dry, minimum quantity of lubricant, and flood-lubricated conditions
    Davim, J. P.
    Sreejith, P. S.
    Gomes, R.
    Peixoto, C.
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE, 2006, 220 (10) : 1605 - 1611
  • [20] Bayesian network classifiers
    Friedman, N
    Geiger, D
    Goldszmidt, M
    [J]. MACHINE LEARNING, 1997, 29 (2-3) : 131 - 163
1234