Control Force Compensation and Wear Monitoring of Variable Stiffness Joints in Drive Machining Process
Aiming at the joint flexibility and wear state existing in the process of driving mechanical parts, this paper first proposes a stiffness and position decoupling control method for variable stiffness joints, which realizes the joint position control and the unity of joint compliance. The joint stiff...
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| Veröffentlicht in: | Mathematical problems in engineering 2021, Vol.2021, p.1-11 |
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| creator | Wu, Yanchen |
| description | Aiming at the joint flexibility and wear state existing in the process of driving mechanical parts, this paper first proposes a stiffness and position decoupling control method for variable stiffness joints, which realizes the joint position control and the unity of joint compliance. The joint stiffness model was obtained by using the static relationship between the Jacobian matrix and the model, and the nonlinear equations composed of the mechanical model and the stiffness model of the variable stiffness device were solved by the optimization method to realize the nonlinear decoupling of the stiffness and position of the variable stiffness joint. Secondly, this paper proposes an online monitoring method of wear state in the machining process based on machine tool information. In this method, OPC-UA communication technology was used to collect and store the information of CNC machine tools online, and the internal process information related to the wear of the machine tools was obtained. Based on such information and the corresponding wear information, a wear state recognition model is established by using a convolutional neural network. The feasibility and effectiveness of the proposed compliance control scheme and the performance of online monitoring of wear condition are analysed and verified by simulation experiments. |
| doi_str_mv | 10.1155/2021/5524323 |
| format | Article |
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The joint stiffness model was obtained by using the static relationship between the Jacobian matrix and the model, and the nonlinear equations composed of the mechanical model and the stiffness model of the variable stiffness device were solved by the optimization method to realize the nonlinear decoupling of the stiffness and position of the variable stiffness joint. Secondly, this paper proposes an online monitoring method of wear state in the machining process based on machine tool information. In this method, OPC-UA communication technology was used to collect and store the information of CNC machine tools online, and the internal process information related to the wear of the machine tools was obtained. Based on such information and the corresponding wear information, a wear state recognition model is established by using a convolutional neural network. The feasibility and effectiveness of the proposed compliance control scheme and the performance of online monitoring of wear condition are analysed and verified by simulation experiments.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2021/5524323</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Artificial neural networks ; Compliance ; Condition monitoring ; Control methods ; Decoupling ; Design ; Gravity ; Jacobi matrix method ; Jacobian matrix ; Machine learning ; Machine tools ; Machining ; Mathematical problems ; Methods ; Neural networks ; Nonlinear equations ; Optimization ; Principal components analysis ; Robots ; Signal processing ; Stiffness ; Tool wear ; Velocity</subject><ispartof>Mathematical problems in engineering, 2021, Vol.2021, p.1-11</ispartof><rights>Copyright © 2021 Yanchen Wu.</rights><rights>Copyright © 2021 Yanchen Wu. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c294t-fbe372f60c204d73d5dca486d0d27fdd7aac30a1d7d0be9f0a6bc869666b43e13</cites><orcidid>0000-0002-2077-5407</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,4010,27900,27901,27902</link.rule.ids></links><search><contributor>Jiang, Yi-Zhang</contributor><contributor>Yi-Zhang Jiang</contributor><creatorcontrib>Wu, Yanchen</creatorcontrib><title>Control Force Compensation and Wear Monitoring of Variable Stiffness Joints in Drive Machining Process</title><title>Mathematical problems in engineering</title><description>Aiming at the joint flexibility and wear state existing in the process of driving mechanical parts, this paper first proposes a stiffness and position decoupling control method for variable stiffness joints, which realizes the joint position control and the unity of joint compliance. The joint stiffness model was obtained by using the static relationship between the Jacobian matrix and the model, and the nonlinear equations composed of the mechanical model and the stiffness model of the variable stiffness device were solved by the optimization method to realize the nonlinear decoupling of the stiffness and position of the variable stiffness joint. Secondly, this paper proposes an online monitoring method of wear state in the machining process based on machine tool information. In this method, OPC-UA communication technology was used to collect and store the information of CNC machine tools online, and the internal process information related to the wear of the machine tools was obtained. Based on such information and the corresponding wear information, a wear state recognition model is established by using a convolutional neural network. The feasibility and effectiveness of the proposed compliance control scheme and the performance of online monitoring of wear condition are analysed and verified by simulation experiments.</description><subject>Artificial neural networks</subject><subject>Compliance</subject><subject>Condition monitoring</subject><subject>Control methods</subject><subject>Decoupling</subject><subject>Design</subject><subject>Gravity</subject><subject>Jacobi matrix method</subject><subject>Jacobian matrix</subject><subject>Machine learning</subject><subject>Machine tools</subject><subject>Machining</subject><subject>Mathematical problems</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Nonlinear equations</subject><subject>Optimization</subject><subject>Principal components analysis</subject><subject>Robots</subject><subject>Signal processing</subject><subject>Stiffness</subject><subject>Tool 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Yanchen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c294t-fbe372f60c204d73d5dca486d0d27fdd7aac30a1d7d0be9f0a6bc869666b43e13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial neural networks</topic><topic>Compliance</topic><topic>Condition monitoring</topic><topic>Control methods</topic><topic>Decoupling</topic><topic>Design</topic><topic>Gravity</topic><topic>Jacobi matrix method</topic><topic>Jacobian matrix</topic><topic>Machine learning</topic><topic>Machine tools</topic><topic>Machining</topic><topic>Mathematical problems</topic><topic>Methods</topic><topic>Neural networks</topic><topic>Nonlinear equations</topic><topic>Optimization</topic><topic>Principal components analysis</topic><topic>Robots</topic><topic>Signal processing</topic><topic>Stiffness</topic><topic>Tool 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China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Yanchen</au><au>Jiang, Yi-Zhang</au><au>Yi-Zhang Jiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Control Force Compensation and Wear Monitoring of Variable Stiffness Joints in Drive Machining Process</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2021</date><risdate>2021</risdate><volume>2021</volume><spage>1</spage><epage>11</epage><pages>1-11</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>Aiming at the joint flexibility and wear state existing in the process of driving mechanical parts, this paper first proposes a stiffness and position decoupling control method for variable stiffness joints, which realizes the joint position control and the unity of joint compliance. The joint stiffness model was obtained by using the static relationship between the Jacobian matrix and the model, and the nonlinear equations composed of the mechanical model and the stiffness model of the variable stiffness device were solved by the optimization method to realize the nonlinear decoupling of the stiffness and position of the variable stiffness joint. Secondly, this paper proposes an online monitoring method of wear state in the machining process based on machine tool information. In this method, OPC-UA communication technology was used to collect and store the information of CNC machine tools online, and the internal process information related to the wear of the machine tools was obtained. Based on such information and the corresponding wear information, a wear state recognition model is established by using a convolutional neural network. 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| subjects | Artificial neural networks Compliance Condition monitoring Control methods Decoupling Design Gravity Jacobi matrix method Jacobian matrix Machine learning Machine tools Machining Mathematical problems Methods Neural networks Nonlinear equations Optimization Principal components analysis Robots Signal processing Stiffness Tool wear Velocity |
| title | Control Force Compensation and Wear Monitoring of Variable Stiffness Joints in Drive Machining Process |
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