Quality monitoring of aluminum alloy DPMIG welding based on broadband mode decomposition and MMC-FCH
•Broadband mode decomposition is proposed for decomposing broadband signals with “sharp corners”.•An associative dictionary library consisting of typical broadband signals and narrowband signals is constructed.•ACROA is adopted for determining the optimized parameter by using the smoothness function...
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Veröffentlicht in: | Measurement : journal of the International Measurement Confederation 2020-07, Vol.158, p.107683, Article 107683 |
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creator | Peng, Yanfeng Li, Zepei He, Kuanfang Liu, Yanfei Lu, Qinghua Li, Qingxian Liu, Liangjiang Luo, Ruiqiong |
description | •Broadband mode decomposition is proposed for decomposing broadband signals with “sharp corners”.•An associative dictionary library consisting of typical broadband signals and narrowband signals is constructed.•ACROA is adopted for determining the optimized parameter by using the smoothness function as the objective function.•BMD can effectively monitor the quality of aluminum alloy DPMIG welding combined with MMC-FCH.
In double pulse metal inert gas (DPMIG) welding, the input broadband electrical signals are often affected by strong noise, which will decrease the quality monitoring accuracy. Therefore, a suitable method should be applied to extract features from the signals. However, due to the Gibbs phenomenon and the interpolation of extreme points, former methods such as variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) will generate unavoidable error. Therefore, broadband mode decomposition (BMD) method is newly proposed in this paper by constructing an associative dictionary library consisting of typical broadband and narrowband signals. Therefore, the drawbacks of the former methods can be avoided by searching in the dictionary. Analysis results indicate that by combining with flexible convex hulls (MMC-FCH), BMD is more accurate in extracting broadband components. Meanwhile, the mean accuracy of quality monitoring can be increased from 92.19% (VMD) and 93.75% (EEMD) to 100% by applying BMD. |
doi_str_mv | 10.1016/j.measurement.2020.107683 |
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In double pulse metal inert gas (DPMIG) welding, the input broadband electrical signals are often affected by strong noise, which will decrease the quality monitoring accuracy. Therefore, a suitable method should be applied to extract features from the signals. However, due to the Gibbs phenomenon and the interpolation of extreme points, former methods such as variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) will generate unavoidable error. Therefore, broadband mode decomposition (BMD) method is newly proposed in this paper by constructing an associative dictionary library consisting of typical broadband and narrowband signals. Therefore, the drawbacks of the former methods can be avoided by searching in the dictionary. Analysis results indicate that by combining with flexible convex hulls (MMC-FCH), BMD is more accurate in extracting broadband components. Meanwhile, the mean accuracy of quality monitoring can be increased from 92.19% (VMD) and 93.75% (EEMD) to 100% by applying BMD.</description><identifier>ISSN: 0263-2241</identifier><identifier>EISSN: 1873-412X</identifier><identifier>DOI: 10.1016/j.measurement.2020.107683</identifier><language>eng</language><publisher>London: Elsevier Ltd</publisher><subject>Aluminum alloys ; Aluminum base alloys ; Broadband ; Broadband mode decomposition ; Convexity ; Dictionaries ; Empirical analysis ; Feature extraction ; Flexible convex hulls ; Gibbs phenomenon ; Hulls ; Interpolation ; Monitoring ; Monitoring systems ; Multiscale fuzzy entropy ; Narrowband ; Quality monitoring ; Rare gases ; Welding</subject><ispartof>Measurement : journal of the International Measurement Confederation, 2020-07, Vol.158, p.107683, Article 107683</ispartof><rights>2020 Elsevier Ltd</rights><rights>Copyright Elsevier Science Ltd. Jul 1, 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c349t-54f552cd8b02778d244023377c87e1ba9e5cfc200d4dafe3805f0514237d509e3</citedby><cites>FETCH-LOGICAL-c349t-54f552cd8b02778d244023377c87e1ba9e5cfc200d4dafe3805f0514237d509e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.measurement.2020.107683$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Peng, Yanfeng</creatorcontrib><creatorcontrib>Li, Zepei</creatorcontrib><creatorcontrib>He, Kuanfang</creatorcontrib><creatorcontrib>Liu, Yanfei</creatorcontrib><creatorcontrib>Lu, Qinghua</creatorcontrib><creatorcontrib>Li, Qingxian</creatorcontrib><creatorcontrib>Liu, Liangjiang</creatorcontrib><creatorcontrib>Luo, Ruiqiong</creatorcontrib><title>Quality monitoring of aluminum alloy DPMIG welding based on broadband mode decomposition and MMC-FCH</title><title>Measurement : journal of the International Measurement Confederation</title><description>•Broadband mode decomposition is proposed for decomposing broadband signals with “sharp corners”.•An associative dictionary library consisting of typical broadband signals and narrowband signals is constructed.•ACROA is adopted for determining the optimized parameter by using the smoothness function as the objective function.•BMD can effectively monitor the quality of aluminum alloy DPMIG welding combined with MMC-FCH.
In double pulse metal inert gas (DPMIG) welding, the input broadband electrical signals are often affected by strong noise, which will decrease the quality monitoring accuracy. Therefore, a suitable method should be applied to extract features from the signals. However, due to the Gibbs phenomenon and the interpolation of extreme points, former methods such as variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) will generate unavoidable error. Therefore, broadband mode decomposition (BMD) method is newly proposed in this paper by constructing an associative dictionary library consisting of typical broadband and narrowband signals. Therefore, the drawbacks of the former methods can be avoided by searching in the dictionary. Analysis results indicate that by combining with flexible convex hulls (MMC-FCH), BMD is more accurate in extracting broadband components. Meanwhile, the mean accuracy of quality monitoring can be increased from 92.19% (VMD) and 93.75% (EEMD) to 100% by applying BMD.</description><subject>Aluminum alloys</subject><subject>Aluminum base alloys</subject><subject>Broadband</subject><subject>Broadband mode decomposition</subject><subject>Convexity</subject><subject>Dictionaries</subject><subject>Empirical analysis</subject><subject>Feature extraction</subject><subject>Flexible convex hulls</subject><subject>Gibbs phenomenon</subject><subject>Hulls</subject><subject>Interpolation</subject><subject>Monitoring</subject><subject>Monitoring systems</subject><subject>Multiscale fuzzy entropy</subject><subject>Narrowband</subject><subject>Quality monitoring</subject><subject>Rare gases</subject><subject>Welding</subject><issn>0263-2241</issn><issn>1873-412X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNqNUEtLxDAQDqLguvofKp675tE26VGq7i7sooKCt5AmU0lpmzVplf33ttSDR08zfK9hPoSuCV4RTLLbetWCCoOHFrp-RTGdcJ4JdoIWRHAWJ4S-n6IFphmLKU3IOboIocYYZyzPFsi8DKqx_TFqXWd75233EbkqUs3Q2m5ox6Vxx-j-eb9dR9_QmIkvVQATuS4qvVOmVJ0Z3QYiA9q1Bxdsb0dygvf7In4sNpforFJNgKvfuURvjw-vxSbePa23xd0u1izJ-zhNqjSl2ogSU86FoUmCKWOca8GBlCqHVFeaYmwSoypgAqcVTklCGTcpzoEt0c2ce_Duc4DQy9oNvhtPyjGLCMFTikdVPqu0dyF4qOTB21b5oyRYTqXKWv4pVU6lyrnU0VvMXhjf-LLgZdAWOg3GetC9NM7-I-UHBMuFuw</recordid><startdate>20200701</startdate><enddate>20200701</enddate><creator>Peng, Yanfeng</creator><creator>Li, Zepei</creator><creator>He, Kuanfang</creator><creator>Liu, Yanfei</creator><creator>Lu, Qinghua</creator><creator>Li, Qingxian</creator><creator>Liu, Liangjiang</creator><creator>Luo, Ruiqiong</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20200701</creationdate><title>Quality monitoring of aluminum alloy DPMIG welding based on broadband mode decomposition and MMC-FCH</title><author>Peng, Yanfeng ; Li, Zepei ; He, Kuanfang ; Liu, Yanfei ; Lu, Qinghua ; Li, Qingxian ; Liu, Liangjiang ; Luo, Ruiqiong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c349t-54f552cd8b02778d244023377c87e1ba9e5cfc200d4dafe3805f0514237d509e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Aluminum alloys</topic><topic>Aluminum base alloys</topic><topic>Broadband</topic><topic>Broadband mode decomposition</topic><topic>Convexity</topic><topic>Dictionaries</topic><topic>Empirical analysis</topic><topic>Feature extraction</topic><topic>Flexible convex hulls</topic><topic>Gibbs phenomenon</topic><topic>Hulls</topic><topic>Interpolation</topic><topic>Monitoring</topic><topic>Monitoring systems</topic><topic>Multiscale fuzzy entropy</topic><topic>Narrowband</topic><topic>Quality monitoring</topic><topic>Rare gases</topic><topic>Welding</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Peng, Yanfeng</creatorcontrib><creatorcontrib>Li, Zepei</creatorcontrib><creatorcontrib>He, Kuanfang</creatorcontrib><creatorcontrib>Liu, Yanfei</creatorcontrib><creatorcontrib>Lu, Qinghua</creatorcontrib><creatorcontrib>Li, Qingxian</creatorcontrib><creatorcontrib>Liu, Liangjiang</creatorcontrib><creatorcontrib>Luo, Ruiqiong</creatorcontrib><collection>CrossRef</collection><jtitle>Measurement : journal of the International Measurement Confederation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Peng, Yanfeng</au><au>Li, Zepei</au><au>He, Kuanfang</au><au>Liu, Yanfei</au><au>Lu, Qinghua</au><au>Li, Qingxian</au><au>Liu, Liangjiang</au><au>Luo, Ruiqiong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quality monitoring of aluminum alloy DPMIG welding based on broadband mode decomposition and MMC-FCH</atitle><jtitle>Measurement : journal of the International Measurement Confederation</jtitle><date>2020-07-01</date><risdate>2020</risdate><volume>158</volume><spage>107683</spage><pages>107683-</pages><artnum>107683</artnum><issn>0263-2241</issn><eissn>1873-412X</eissn><abstract>•Broadband mode decomposition is proposed for decomposing broadband signals with “sharp corners”.•An associative dictionary library consisting of typical broadband signals and narrowband signals is constructed.•ACROA is adopted for determining the optimized parameter by using the smoothness function as the objective function.•BMD can effectively monitor the quality of aluminum alloy DPMIG welding combined with MMC-FCH.
In double pulse metal inert gas (DPMIG) welding, the input broadband electrical signals are often affected by strong noise, which will decrease the quality monitoring accuracy. Therefore, a suitable method should be applied to extract features from the signals. However, due to the Gibbs phenomenon and the interpolation of extreme points, former methods such as variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) will generate unavoidable error. Therefore, broadband mode decomposition (BMD) method is newly proposed in this paper by constructing an associative dictionary library consisting of typical broadband and narrowband signals. Therefore, the drawbacks of the former methods can be avoided by searching in the dictionary. Analysis results indicate that by combining with flexible convex hulls (MMC-FCH), BMD is more accurate in extracting broadband components. Meanwhile, the mean accuracy of quality monitoring can be increased from 92.19% (VMD) and 93.75% (EEMD) to 100% by applying BMD.</abstract><cop>London</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.measurement.2020.107683</doi></addata></record> |
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subjects | Aluminum alloys Aluminum base alloys Broadband Broadband mode decomposition Convexity Dictionaries Empirical analysis Feature extraction Flexible convex hulls Gibbs phenomenon Hulls Interpolation Monitoring Monitoring systems Multiscale fuzzy entropy Narrowband Quality monitoring Rare gases Welding |
title | Quality monitoring of aluminum alloy DPMIG welding based on broadband mode decomposition and MMC-FCH |
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