Temperature measurement point optimization and experimental research for bi-rotary milling head of five-axis CNC machine tool
Thermal deformation is the main factor affecting the machining accuracy of the bi-rotary milling head. To accurately determine the temperature-sensitive points of the bi-rotary milling head to suppress thermal deformation, this paper adopts back propagation (BP) neural network sensitivity analysis m...
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| Veröffentlicht in: | International journal of advanced manufacturing technology 2022-07, Vol.121 (1-2), p.309-322 |
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| creator | Dai, Ye Li, Yang Li, Zhaolong Wen, Wanjian Zhan, Shiqiang |
| description | Thermal deformation is the main factor affecting the machining accuracy of the bi-rotary milling head. To accurately determine the temperature-sensitive points of the bi-rotary milling head to suppress thermal deformation, this paper adopts back propagation (BP) neural network sensitivity analysis method with improved connection weights to optimize the temperature measurement points. The analysis results are subjected to randomized mean value processing to reduce the randomness of the initialization of the prediction model. The number of temperature measurement points is reduced from 15 to 4. Taking the 5AS01 direct-drive bi-rotary milling head as an example, a thermal-structural coupling model is established to analyze its thermal characteristics. The capillary copper tube cooling suppression experiment is arranged according to the position of the temperature-sensitive points. The experimental results show that cooling the temperature-sensitive points can simultaneously reduce the thermal error in X- and Z-directions by about 58%, providing a basis for the bi-rotary milling head to improve machining accuracy. |
| doi_str_mv | 10.1007/s00170-022-09317-7 |
| format | Article |
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To accurately determine the temperature-sensitive points of the bi-rotary milling head to suppress thermal deformation, this paper adopts back propagation (BP) neural network sensitivity analysis method with improved connection weights to optimize the temperature measurement points. The analysis results are subjected to randomized mean value processing to reduce the randomness of the initialization of the prediction model. The number of temperature measurement points is reduced from 15 to 4. Taking the 5AS01 direct-drive bi-rotary milling head as an example, a thermal-structural coupling model is established to analyze its thermal characteristics. The capillary copper tube cooling suppression experiment is arranged according to the position of the temperature-sensitive points. 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To accurately determine the temperature-sensitive points of the bi-rotary milling head to suppress thermal deformation, this paper adopts back propagation (BP) neural network sensitivity analysis method with improved connection weights to optimize the temperature measurement points. The analysis results are subjected to randomized mean value processing to reduce the randomness of the initialization of the prediction model. The number of temperature measurement points is reduced from 15 to 4. Taking the 5AS01 direct-drive bi-rotary milling head as an example, a thermal-structural coupling model is established to analyze its thermal characteristics. The capillary copper tube cooling suppression experiment is arranged according to the position of the temperature-sensitive points. The experimental results show that cooling the temperature-sensitive points can simultaneously reduce the thermal error in X- and Z-directions by about 58%, providing a basis for the bi-rotary milling head to improve machining accuracy.</description><subject>Accuracy</subject><subject>Advanced manufacturing technologies</subject><subject>Back propagation networks</subject><subject>CAE) and Design</subject><subject>Capillary tubes</subject><subject>Computer-Aided Engineering (CAD</subject><subject>Cooling</subject><subject>Deformation</subject><subject>Engineering</subject><subject>Five axis</subject><subject>Industrial and Production Engineering</subject><subject>Machine tools</subject><subject>Manufacturing</subject><subject>Mechanical Engineering</subject><subject>Media Management</subject><subject>Methods</subject><subject>Milling (machining)</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Original Article</subject><subject>Prediction models</subject><subject>Sensitivity analysis</subject><subject>Sensors</subject><subject>System theory</subject><subject>Temperature measurement</subject><issn>0268-3768</issn><issn>1433-3015</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp9kMtOwzAURC0EEqXwA6wssTb4kdjJElW8pAo2ZW05zk3rKomDnaKCxL_jEiR2bO5szszVDEKXjF4zStVNpJQpSijnhJaCKaKO0IxlQhBBWX6MZpTLgggli1N0FuM24ZLJYoa-VtANEMy4C4A7MDFpB_2IB-_S9cPoOvdpRud7bPoawz7R7kCYFgeIYILd4MYHXDkS_GjCB-5c27p-jTdgauwb3Lh3IGbvIl48L3Bn7Mb1gEfv23N00pg2wsWvztHr_d1q8UiWLw9Pi9slsbwUI2lyoFVWWyiYkCVTkmWWKVBZyXJpjBIlFDWURtm8zpmFnFZCQVXUnFJbsUrM0dWUOwT_toM46q3fhT691FyqnBaSZzxRfKJs8DEGaPSQqqZGmlF9mFlPM-s0s_6ZWatkEpMpJrhfQ_iL_sf1DU-oglM</recordid><startdate>20220701</startdate><enddate>20220701</enddate><creator>Dai, Ye</creator><creator>Li, Yang</creator><creator>Li, Zhaolong</creator><creator>Wen, Wanjian</creator><creator>Zhan, Shiqiang</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20220701</creationdate><title>Temperature measurement point optimization and experimental research for bi-rotary milling head of five-axis CNC machine tool</title><author>Dai, Ye ; Li, Yang ; Li, Zhaolong ; Wen, Wanjian ; Zhan, Shiqiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-f5e0b4dce8136917614c17e749156aa739e8de9a7c5d51ce50b37eb8d200cb1b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Advanced manufacturing technologies</topic><topic>Back propagation networks</topic><topic>CAE) and Design</topic><topic>Capillary tubes</topic><topic>Computer-Aided Engineering (CAD</topic><topic>Cooling</topic><topic>Deformation</topic><topic>Engineering</topic><topic>Five axis</topic><topic>Industrial and Production Engineering</topic><topic>Machine tools</topic><topic>Manufacturing</topic><topic>Mechanical Engineering</topic><topic>Media Management</topic><topic>Methods</topic><topic>Milling (machining)</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Original Article</topic><topic>Prediction models</topic><topic>Sensitivity analysis</topic><topic>Sensors</topic><topic>System theory</topic><topic>Temperature measurement</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dai, Ye</creatorcontrib><creatorcontrib>Li, Yang</creatorcontrib><creatorcontrib>Li, Zhaolong</creatorcontrib><creatorcontrib>Wen, Wanjian</creatorcontrib><creatorcontrib>Zhan, Shiqiang</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>International journal of advanced manufacturing technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dai, Ye</au><au>Li, Yang</au><au>Li, Zhaolong</au><au>Wen, Wanjian</au><au>Zhan, Shiqiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Temperature measurement point optimization and experimental research for bi-rotary milling head of five-axis CNC machine tool</atitle><jtitle>International journal of advanced manufacturing technology</jtitle><stitle>Int J Adv Manuf Technol</stitle><date>2022-07-01</date><risdate>2022</risdate><volume>121</volume><issue>1-2</issue><spage>309</spage><epage>322</epage><pages>309-322</pages><issn>0268-3768</issn><eissn>1433-3015</eissn><abstract>Thermal deformation is the main factor affecting the machining accuracy of the bi-rotary milling head. To accurately determine the temperature-sensitive points of the bi-rotary milling head to suppress thermal deformation, this paper adopts back propagation (BP) neural network sensitivity analysis method with improved connection weights to optimize the temperature measurement points. The analysis results are subjected to randomized mean value processing to reduce the randomness of the initialization of the prediction model. The number of temperature measurement points is reduced from 15 to 4. Taking the 5AS01 direct-drive bi-rotary milling head as an example, a thermal-structural coupling model is established to analyze its thermal characteristics. The capillary copper tube cooling suppression experiment is arranged according to the position of the temperature-sensitive points. 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| subjects | Accuracy Advanced manufacturing technologies Back propagation networks CAE) and Design Capillary tubes Computer-Aided Engineering (CAD Cooling Deformation Engineering Five axis Industrial and Production Engineering Machine tools Manufacturing Mechanical Engineering Media Management Methods Milling (machining) Neural networks Optimization Original Article Prediction models Sensitivity analysis Sensors System theory Temperature measurement |
| title | Temperature measurement point optimization and experimental research for bi-rotary milling head of five-axis CNC machine tool |
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