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Accurate Prediction of CNC Machining Time for Milling Operations Using Neural Networks
The prediction of computer numerical control (CNC) machining time critically impacts productivity. Computer-aided manufacturing (CAM) software typically has machining time prediction capabilities, allowing users to know the machining time for parts during toolpath planning. However, CAM software doe...
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creator | Chen, Xiao-Xing Lee, Wei-Chen |
description | The prediction of computer numerical control (CNC) machining time critically impacts productivity. Computer-aided manufacturing (CAM) software typically has machining time prediction capabilities, allowing users to know the machining time for parts during toolpath planning. However, CAM software does not consider the kinematics of the CNC machines and the control principle of the CNC controllers. Hence, the predicted machining time is often much shorter than the actual one. To address this problem, we developed two neural network-based machining time prediction models for milling operations using MATLAB and TensorFlow. The results show that using the models proposed in this research could achieve prediction errors within 2%, while the CAM software had about 12% error. |
doi_str_mv | 10.1109/ICPS59941.2024.10640035 |
format | conference_proceeding |
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Computer-aided manufacturing (CAM) software typically has machining time prediction capabilities, allowing users to know the machining time for parts during toolpath planning. However, CAM software does not consider the kinematics of the CNC machines and the control principle of the CNC controllers. Hence, the predicted machining time is often much shorter than the actual one. To address this problem, we developed two neural network-based machining time prediction models for milling operations using MATLAB and TensorFlow. The results show that using the models proposed in this research could achieve prediction errors within 2%, while the CAM software had about 12% error.</description><identifier>EISSN: 2769-3899</identifier><identifier>EISBN: 9798350363012</identifier><identifier>DOI: 10.1109/ICPS59941.2024.10640035</identifier><language>eng</language><publisher>IEEE</publisher><subject>Kinematics ; Machining Time ; Milling ; Neural Network ; Neural networks ; Planning ; Predictive models ; Productivity ; Software</subject><ispartof>2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS), 2024, p.1-2</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10640035$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,27924,54554,54931</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10640035$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Chen, Xiao-Xing</creatorcontrib><creatorcontrib>Lee, Wei-Chen</creatorcontrib><title>Accurate Prediction of CNC Machining Time for Milling Operations Using Neural Networks</title><title>2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS)</title><addtitle>ICPS</addtitle><description>The prediction of computer numerical control (CNC) machining time critically impacts productivity. Computer-aided manufacturing (CAM) software typically has machining time prediction capabilities, allowing users to know the machining time for parts during toolpath planning. However, CAM software does not consider the kinematics of the CNC machines and the control principle of the CNC controllers. Hence, the predicted machining time is often much shorter than the actual one. To address this problem, we developed two neural network-based machining time prediction models for milling operations using MATLAB and TensorFlow. The results show that using the models proposed in this research could achieve prediction errors within 2%, while the CAM software had about 12% error.</description><subject>Kinematics</subject><subject>Machining Time</subject><subject>Milling</subject><subject>Neural Network</subject><subject>Neural networks</subject><subject>Planning</subject><subject>Predictive models</subject><subject>Productivity</subject><subject>Software</subject><issn>2769-3899</issn><isbn>9798350363012</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2024</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1kNtKw0AYhFdBsNS8geC-QOK_5-xlCR4KPYGtt2V3s9HVNCm7Eenbm6JeDXzMDMMgdEegIAT0_bzavAitOSkoUF4QkByAiQuUaaVLJoBJBoReoglVUues1PoaZSl9wGijhCgoJ-h15txXNIPHm-jr4IbQd7hvcLWq8NK499CF7g1vw8Hjpo94Gdr2DNZHP4ZGb8K7dAYrP7a0owzfffxMN-iqMW3y2Z9O0e7xYVs954v107yaLfIwzh1yC0IIYygzwCz1RjZWK8UZlZxwKkHW1hmrvKJCKdVoK5lwjimoZSm45myKbn97g_d-f4zhYOJp_38F-wFqf1Il</recordid><startdate>20240512</startdate><enddate>20240512</enddate><creator>Chen, Xiao-Xing</creator><creator>Lee, Wei-Chen</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20240512</creationdate><title>Accurate Prediction of CNC Machining Time for Milling Operations Using Neural Networks</title><author>Chen, Xiao-Xing ; Lee, Wei-Chen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i106t-b0555aa23a03b2ea6fb97743264142606dbcab7e725777f9b635cc370d6854943</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Kinematics</topic><topic>Machining Time</topic><topic>Milling</topic><topic>Neural Network</topic><topic>Neural networks</topic><topic>Planning</topic><topic>Predictive models</topic><topic>Productivity</topic><topic>Software</topic><toplevel>online_resources</toplevel><creatorcontrib>Chen, Xiao-Xing</creatorcontrib><creatorcontrib>Lee, Wei-Chen</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chen, Xiao-Xing</au><au>Lee, Wei-Chen</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Accurate Prediction of CNC Machining Time for Milling Operations Using Neural Networks</atitle><btitle>2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS)</btitle><stitle>ICPS</stitle><date>2024-05-12</date><risdate>2024</risdate><spage>1</spage><epage>2</epage><pages>1-2</pages><eissn>2769-3899</eissn><eisbn>9798350363012</eisbn><abstract>The prediction of computer numerical control (CNC) machining time critically impacts productivity. Computer-aided manufacturing (CAM) software typically has machining time prediction capabilities, allowing users to know the machining time for parts during toolpath planning. However, CAM software does not consider the kinematics of the CNC machines and the control principle of the CNC controllers. Hence, the predicted machining time is often much shorter than the actual one. To address this problem, we developed two neural network-based machining time prediction models for milling operations using MATLAB and TensorFlow. The results show that using the models proposed in this research could achieve prediction errors within 2%, while the CAM software had about 12% error.</abstract><pub>IEEE</pub><doi>10.1109/ICPS59941.2024.10640035</doi><tpages>2</tpages></addata></record> |
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language | eng |
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subjects | Kinematics Machining Time Milling Neural Network Neural networks Planning Predictive models Productivity Software |
title | Accurate Prediction of CNC Machining Time for Milling Operations Using Neural Networks |
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