Show simple item record

dc.contributor.advisorSteele, John P. H.
dc.contributor.advisorMoore, Kevin L., 1960-
dc.contributor.authorAyoade, Adewole A.
dc.date.accessioned2019-07-08T21:07:39Z
dc.date.accessioned2022-02-03T13:17:22Z
dc.date.available2020-07-05T21:07:39Z
dc.date.available2022-02-03T13:17:22Z
dc.date.issued2019
dc.identifierAyoade_mines_0052E_11758.pdf
dc.identifierT 8748
dc.identifier.urihttps://hdl.handle.net/11124/173093
dc.descriptionIncludes bibliographical references.
dc.description2019 Summer.
dc.description.abstractWe have come a long way in the automation of robotic welding. But with the decline in qualified welders, there has been a steadily growing demand for robotic solutions with capabilities that match that of human welders. Among these capabilities, a major missing element is the ability of the robotic systems to extract actionable information about the quality of their welds, in real time. While this is a challenging problem, we have been able to show that we can extract this information through-the-arc that captures the controlled dynamics leading to defect occurrence and at the same time identify the variation in the process leading to that defect. Our methodology involves a unique approach to gathering synchronized measurements of the welding process mapped to an objective evaluation of the resulting weld bead at a resolution of 1 mm. Demonstrating a system that can achieve this was at the heart of this work, after which we gathered measurements on voltage, current, weld bead profile, visual images, welding torch pose and radiographic images. These measurements were gathered under experiments involving induced porosity and geometric defects under process variations such as the presence of primer and oil, loss of shielding gas, erratic wire feeding, out of joint and the presence of gap between plates. From these measurements, we created a dataset and identified discriminative features that are indicative of weld quality deviations from process variations. Particularly, we proposed a specialized feature extraction methodology, "Bag-of-Pulses" as informed by the domain expertise knowledge based on "Bag-of-Words" approach. In this feature extraction methodology, we were able to represent the data concisely with new building blocks that retain the characteristic nature of the signal source (i.e., pulsed GMAW) such that we can feed them into a classification algorithm to identify defects and make diagnosis.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.rightsCopyright of the original work is retained by the author.
dc.subjectgas metal arc welding
dc.subjectonline weld quality assessment
dc.subjecttime series analysis
dc.subjectmachine learning and data mining
dc.subjectdefect detection and diagnosis
dc.subjectrobotics
dc.titleLearning and online prediction of weld quality in robotic GMAW
dc.typeText
dc.contributor.committeememberWakin, Michael B.
dc.contributor.committeememberVincent, Tyrone
dc.contributor.committeememberZhang, Xiaoli
dcterms.embargo.terms2020-07-05
dcterms.embargo.expires2020-07-05
thesis.degree.nameDoctor of Philosophy (Ph.D.)
thesis.degree.levelDoctoral
thesis.degree.disciplineElectrical Engineering and Computer Sciences
thesis.degree.grantorColorado School of Mines
dc.rights.accessEmbargo Expires: 07/05/2020


Files in this item

Thumbnail
Name:
Ayoade_mines_0052E_11758.pdf
Size:
8.384Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record