Small Modulus Injection Gear Size Inspection Method Based on Super Resolution
Accurate gear parameter inspection technology can effectively improve the quality of gear production. In order to obtain more accurate detection results, the quality of the detection image must be controlled. However, there is a lack of optimization of image accuracy in the existing gear size detect...
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| Veröffentlicht in: | IEEE sensors journal 2024-06, Vol.24 (11), p.18646-18658 |
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| creator | Jiang, Kuosheng Liu, Hao Chang, Yasheng |
| description | Accurate gear parameter inspection technology can effectively improve the quality of gear production. In order to obtain more accurate detection results, the quality of the detection image must be controlled. However, there is a lack of optimization of image accuracy in the existing gear size detection methods. Consequently, this article proposes a super-resolution (SR)-based dimensional detection method for small modulus injection molding gears. The method introduces an improved SR algorithm for small modulus injection molding gears, constructs a size detection system, effectively enhances the quality of the image of the measured gear through image processing, and then calculates the value of the measured gear's precision parameters according to the definition of gear precision parameters. The experimental results show that the standard deviation of the bore diameter and the toothed circle diameter are 0.756 and 1.531 \mu \text{m} respectively, and the repeat errors of the repeated measurements are less than 2 \mu \text{m} . Therefore, the research results of this article provide a new detection method for related industries, which is of positive significance for improving production efficiency and quality. |
| doi_str_mv | 10.1109/JSEN.2024.3390033 |
| format | Article |
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In order to obtain more accurate detection results, the quality of the detection image must be controlled. However, there is a lack of optimization of image accuracy in the existing gear size detection methods. Consequently, this article proposes a super-resolution (SR)-based dimensional detection method for small modulus injection molding gears. The method introduces an improved SR algorithm for small modulus injection molding gears, constructs a size detection system, effectively enhances the quality of the image of the measured gear through image processing, and then calculates the value of the measured gear's precision parameters according to the definition of gear precision parameters. The experimental results show that the standard deviation of the bore diameter and the toothed circle diameter are 0.756 and <inline-formula> <tex-math notation="LaTeX">1.531 \mu \text{m} </tex-math></inline-formula> respectively, and the repeat errors of the repeated measurements are less than <inline-formula> <tex-math notation="LaTeX">2 \mu \text{m} </tex-math></inline-formula>. Therefore, the research results of this article provide a new detection method for related industries, which is of positive significance for improving production efficiency and quality.]]></description><identifier>ISSN: 1530-437X</identifier><identifier>EISSN: 1558-1748</identifier><identifier>DOI: 10.1109/JSEN.2024.3390033</identifier><identifier>CODEN: ISJEAZ</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Deep learning ; Diameters ; Gears ; generating adversarial network (GAN) ; Generative adversarial networks ; Image enhancement ; Image processing ; Image quality ; Injection molding ; Inspection ; Parameters ; size inspection ; small modulus injection gear ; super resolution (SR) ; Superresolution ; Visualization</subject><ispartof>IEEE sensors journal, 2024-06, Vol.24 (11), p.18646-18658</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c246t-e2795a78030b0371c69c86a2566cfbf4630b54165e7ce1484972c1e6a544e4dc3</cites><orcidid>0000-0002-2420-437X ; 0000-0003-0300-1976</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10508630$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10508630$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jiang, Kuosheng</creatorcontrib><creatorcontrib>Liu, Hao</creatorcontrib><creatorcontrib>Chang, Yasheng</creatorcontrib><title>Small Modulus Injection Gear Size Inspection Method Based on Super Resolution</title><title>IEEE sensors journal</title><addtitle>JSEN</addtitle><description><![CDATA[Accurate gear parameter inspection technology can effectively improve the quality of gear production. In order to obtain more accurate detection results, the quality of the detection image must be controlled. However, there is a lack of optimization of image accuracy in the existing gear size detection methods. Consequently, this article proposes a super-resolution (SR)-based dimensional detection method for small modulus injection molding gears. The method introduces an improved SR algorithm for small modulus injection molding gears, constructs a size detection system, effectively enhances the quality of the image of the measured gear through image processing, and then calculates the value of the measured gear's precision parameters according to the definition of gear precision parameters. The experimental results show that the standard deviation of the bore diameter and the toothed circle diameter are 0.756 and <inline-formula> <tex-math notation="LaTeX">1.531 \mu \text{m} </tex-math></inline-formula> respectively, and the repeat errors of the repeated measurements are less than <inline-formula> <tex-math notation="LaTeX">2 \mu \text{m} </tex-math></inline-formula>. Therefore, the research results of this article provide a new detection method for related industries, which is of positive significance for improving production efficiency and quality.]]></description><subject>Algorithms</subject><subject>Deep learning</subject><subject>Diameters</subject><subject>Gears</subject><subject>generating adversarial network (GAN)</subject><subject>Generative adversarial networks</subject><subject>Image enhancement</subject><subject>Image processing</subject><subject>Image quality</subject><subject>Injection molding</subject><subject>Inspection</subject><subject>Parameters</subject><subject>size inspection</subject><subject>small modulus injection gear</subject><subject>super resolution (SR)</subject><subject>Superresolution</subject><subject>Visualization</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkE9Lw0AQxRdRsFY_gOBhwXPqbPZfctRSa6VRMArelu1mgilpE3eTg356E9qDp5l5vDcz_Ai5ZjBjDNK753zxMoshFjPOUwDOT8iESZlETIvkdOw5RILrz3NyEcIWgKVa6gnJ8p2ta5o1RV_3ga72W3Rd1ezpEq2nefWLgxbao5hh99UU9MEGLOgw532Lnr5haOp-NFySs9LWAa-OdUo-Hhfv86do_bpcze_XkYuF6iKMdSqtToDDBrhmTqUuUTaWSrlyUwo16FIwJVE7ZCIRqY4dQ2WlECgKx6fk9rC39c13j6Ez26b3--Gk4aCEhlgNy6eEHVzONyF4LE3rq531P4aBGamZkZoZqZkjtSFzc8hUiPjPLyEZvuJ_UCNnbA</recordid><startdate>20240601</startdate><enddate>20240601</enddate><creator>Jiang, Kuosheng</creator><creator>Liu, Hao</creator><creator>Chang, Yasheng</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-2420-437X</orcidid><orcidid>https://orcid.org/0000-0003-0300-1976</orcidid></search><sort><creationdate>20240601</creationdate><title>Small Modulus Injection Gear Size Inspection Method Based on Super Resolution</title><author>Jiang, Kuosheng ; Liu, Hao ; Chang, Yasheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c246t-e2795a78030b0371c69c86a2566cfbf4630b54165e7ce1484972c1e6a544e4dc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Deep learning</topic><topic>Diameters</topic><topic>Gears</topic><topic>generating adversarial network (GAN)</topic><topic>Generative adversarial networks</topic><topic>Image enhancement</topic><topic>Image processing</topic><topic>Image quality</topic><topic>Injection molding</topic><topic>Inspection</topic><topic>Parameters</topic><topic>size inspection</topic><topic>small modulus injection gear</topic><topic>super resolution (SR)</topic><topic>Superresolution</topic><topic>Visualization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiang, Kuosheng</creatorcontrib><creatorcontrib>Liu, Hao</creatorcontrib><creatorcontrib>Chang, Yasheng</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE sensors journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jiang, Kuosheng</au><au>Liu, Hao</au><au>Chang, Yasheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Small Modulus Injection Gear Size Inspection Method Based on Super Resolution</atitle><jtitle>IEEE sensors journal</jtitle><stitle>JSEN</stitle><date>2024-06-01</date><risdate>2024</risdate><volume>24</volume><issue>11</issue><spage>18646</spage><epage>18658</epage><pages>18646-18658</pages><issn>1530-437X</issn><eissn>1558-1748</eissn><coden>ISJEAZ</coden><abstract><![CDATA[Accurate gear parameter inspection technology can effectively improve the quality of gear production. In order to obtain more accurate detection results, the quality of the detection image must be controlled. However, there is a lack of optimization of image accuracy in the existing gear size detection methods. Consequently, this article proposes a super-resolution (SR)-based dimensional detection method for small modulus injection molding gears. The method introduces an improved SR algorithm for small modulus injection molding gears, constructs a size detection system, effectively enhances the quality of the image of the measured gear through image processing, and then calculates the value of the measured gear's precision parameters according to the definition of gear precision parameters. The experimental results show that the standard deviation of the bore diameter and the toothed circle diameter are 0.756 and <inline-formula> <tex-math notation="LaTeX">1.531 \mu \text{m} </tex-math></inline-formula> respectively, and the repeat errors of the repeated measurements are less than <inline-formula> <tex-math notation="LaTeX">2 \mu \text{m} </tex-math></inline-formula>. Therefore, the research results of this article provide a new detection method for related industries, which is of positive significance for improving production efficiency and quality.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JSEN.2024.3390033</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-2420-437X</orcidid><orcidid>https://orcid.org/0000-0003-0300-1976</orcidid></addata></record> |
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| subjects | Algorithms Deep learning Diameters Gears generating adversarial network (GAN) Generative adversarial networks Image enhancement Image processing Image quality Injection molding Inspection Parameters size inspection small modulus injection gear super resolution (SR) Superresolution Visualization |
| title | Small Modulus Injection Gear Size Inspection Method Based on Super Resolution |
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