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  <titleInfo>
    <title>5-axis positioning sequence optimization using genetic algorithms</title>
  </titleInfo>
  <name type="personal">
    <namePart>Kulkarni, Rajesh S.</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Bohez, Erik L.J.</namePart>
    <role>
      <roleTerm type="text">Chairperson</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Pandey, P.C.</namePart>
    <role>
      <roleTerm type="text">Examination committee</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Nagarur, Nagendra N.</namePart>
    <role>
      <roleTerm type="text">Examination Committee</roleTerm>
    </role>
  </name>
  <name type="corporate">
    <namePart>Asian Institute of Technology Fellowship</namePart>
    <role>
      <roleTerm type="text">Scholarship donor</roleTerm>
    </role>
  </name>
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  <originInfo>
    <place>
      <placeTerm type="code" authority="marccountry">th</placeTerm>
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    <place>
      <placeTerm type="text">Bangkok</placeTerm>
    </place>
    <publisher>Asian Institute of Technology</publisher>
    <dateIssued>1997</dateIssued>
    <issuance>monographic</issuance>
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  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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    <extent>41, [30] p.</extent>
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  <abstract>The flexibility of tool orientation in space is extremely high in five-axis machining unlike  in three-axis machining. The tool vector can be positioned in any direction in space. A  number of operations are performed in one set up of the work piece to obtain full  advantage of the five-axis machining concept. The control of machining (actual and  positioning) time in such cases can be very crucial. Planning the optimal path manually  can be very cumbersome and unfortunately most of the CAM systems supp01iing five axis machining available in the market today do not provide an efficient and effective  approach to optimal tool path generation. At the most they have an interactive approach  with the user.  In this report, an algorithm coupled with a programming language which is one of the  best methods to ovei'come the above drawbacks is presented. A genetic approach (GA)  was applied for solution of the model. The GA first minimizes the distance traveled by  the tool (positioning) and then the positioning time is calculated subject to the number of  tool changes The algorithm is tested for its performance with respect to population size  and different genetic operators. </abstract>
  <note>A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Advanced Technologies</note>
  <note>Thesis (M.Eng.) - Asian Institute of Technology,1997</note>
  <subject authority="lcsh">
    <topic>Genetic algorithms</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Machine-tools</topic>
    <topic>Numerical control</topic>
  </subject>
  <relatedItem type="series">
    <titleInfo>
      <title>Thesis ; no. ISE-97-03</title>
    </titleInfo>
    <name type="corporate">
      <namePart>Asian Institute of Technology.</namePart>
      <namePart/>
    </name>
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  <identifier type="uri">http://203.159.5.9/ait-thesis/detail.php?q=B14312</identifier>
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    <url displayLabel="Full-Text">http://203.159.5.9/ait-thesis/detail.php?q=B14312</url>
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