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dc.contributor.authorIeamsaard, Jirarat
dc.contributor.authorMuneesawang, Paisarn
dc.contributor.authorSandnes, Frode Eika
dc.date.accessioned2018-10-10T06:58:55Z
dc.date.accessioned2018-10-23T07:33:10Z
dc.date.available2018-10-10T06:58:55Z
dc.date.available2018-10-23T07:33:10Z
dc.date.issued2018-02-23
dc.identifier.citationIeamsaard J, Muneesawang P, Sandnes FE. Automatic Optical Inspection of Solder Ball Burn Defects on Head Gimbal Assembly. Journal of Failure Analysis and Prevention. 2018;18(2):435-444en
dc.identifier.issn1547-7029
dc.identifier.urihttps://hdl.handle.net/10642/6278
dc.description.abstractThe detection of low quality solder joint quality in hard disk drive (HDD) manufacturing is a time consuming, error-prone and costly process that is often performed manually. This paper thus proposes two automated optical solder jet ball joint defect inspection methods for head gimbal assembly (HGA) production. The first method uses a Support Vector Machine (SVM) for fault detection and the second method uses vertical edge detection to identify solder ball and pad burning defects. The methods were tested with 5,530 HGA images, and their performance was compared to a Bayesian-based method. Experimental results show that the vertical edge detection method gave the best results, with an under reject rate of 0.75% and an over reject rate of 1.88%. The accuracy of the vertical edge detection method was 98.2%, which is higher than the accuracy of 89.9% for the Bayesian-based method, and 84.6% for the SVM-based method.en
dc.language.isoenen
dc.publisherSpringer Verlagen
dc.relation.ispartofseriesJournal of Failure Analysis and Prevention;Volume 18, Issue 2
dc.rightsThe final publication is available at Springer via http://dx.doi.org/ 10.1007/s11668-018-0426-4en
dc.subjectOptical inspectionsen
dc.subjectSolder jet ball joint defectsen
dc.subjectVertical edge detectionsen
dc.subjectHard disk-drive manufacturingen
dc.titleAutomatic Optical Inspection of Solder Ball Burn Defects on Head Gimbal Assemblyen
dc.typeJournal articleen
dc.typePeer revieweden
dc.date.updated2018-10-10T06:58:55Z
dc.description.versionacceptedVersionen
dc.identifier.doihttp://dx.doi.org/10.1007/s11668-018-0426-4
dc.identifier.cristinID1596906
dc.source.issn1547-7029
dc.source.issn1864-1245
dc.relation.journalJournal of Failure Analysis and Prevention


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