Apparatus And Method For In-Manufacturing Evaluation Of Structural And Material Properties Of Fasteners Using Machine Learning
An apparatus and method for detecting structural and material defects in a fastener driven during a manufacturing process includes a driving tool capable of recording an angle-torque trace during the driving of the fastener and a machine learning engine operably connected to the driving tool for ana...
Saved in:
Main Authors | , |
---|---|
Format | Patent |
Language | English |
Published |
14.04.2022
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | An apparatus and method for detecting structural and material defects in a fastener driven during a manufacturing process includes a driving tool capable of recording an angle-torque trace during the driving of the fastener and a machine learning engine operably connected to the driving tool for analyzing the recorded angle-torque trace. The machine learning engine can be provided with a number of sample angle-torque traces from sample fasteners and can self-determine a stored trace including tolerances for acceptable angle-torque trace data from the samples in an unsupervised learning process or protocol without the need for defined anomalous and non-anomalous samples being provided to the machine learning engine. Using the self-defined stored trace and acceptable tolerances, the machine learning engine can analyze attributes of subsequently recorded angle-torque traces to ascertain whether the attributes of the recorded angle-torque traces indicate anomalies within the fastener identified by the recorded trace. |
---|---|
AbstractList | An apparatus and method for detecting structural and material defects in a fastener driven during a manufacturing process includes a driving tool capable of recording an angle-torque trace during the driving of the fastener and a machine learning engine operably connected to the driving tool for analyzing the recorded angle-torque trace. The machine learning engine can be provided with a number of sample angle-torque traces from sample fasteners and can self-determine a stored trace including tolerances for acceptable angle-torque trace data from the samples in an unsupervised learning process or protocol without the need for defined anomalous and non-anomalous samples being provided to the machine learning engine. Using the self-defined stored trace and acceptable tolerances, the machine learning engine can analyze attributes of subsequently recorded angle-torque traces to ascertain whether the attributes of the recorded angle-torque traces indicate anomalies within the fastener identified by the recorded trace. |
Author | Beacham, JR., Jimmie A Jia, Tao |
Author_xml | – fullname: Beacham, JR., Jimmie A – fullname: Jia, Tao |
BookMark | eNqNjL0KwkAQhFNo4d87LFgLXhTBMohBwaCg1mFJNnoQ9o69PUuf3QR9AKthZr6ZcTJgxzRK3pn3KKgxQMY1FKRPV0PuBI68KJBjg5VGsfyA_QvbiGodw7mBq0rsG2y_Q1QS25mLOE-ilkJP5RiUmCTAPfQfBVZPywQnQuEumCbDBttAs59Oknm-v-0OC_KupOCx6tZa3q_pMk2NMevtJjOr_6gPm7ZKMw |
ContentType | Patent |
DBID | EVB |
DatabaseName | esp@cenet |
DatabaseTitleList | |
Database_xml | – sequence: 1 dbid: EVB name: esp@cenet url: http://worldwide.espacenet.com/singleLineSearch?locale=en_EP sourceTypes: Open Access Repository |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Medicine Chemistry Sciences Physics |
ExternalDocumentID | US2022111496A1 |
GroupedDBID | EVB |
ID | FETCH-epo_espacenet_US2022111496A13 |
IEDL.DBID | EVB |
IngestDate | Fri Jul 19 14:43:18 EDT 2024 |
IsOpenAccess | true |
IsPeerReviewed | false |
IsScholarly | false |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-epo_espacenet_US2022111496A13 |
Notes | Application Number: US202017070618 |
OpenAccessLink | https://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220414&DB=EPODOC&CC=US&NR=2022111496A1 |
ParticipantIDs | epo_espacenet_US2022111496A1 |
PublicationCentury | 2000 |
PublicationDate | 20220414 |
PublicationDateYYYYMMDD | 2022-04-14 |
PublicationDate_xml | – month: 04 year: 2022 text: 20220414 day: 14 |
PublicationDecade | 2020 |
PublicationYear | 2022 |
RelatedCompanies | GE Precision Healthcare LLC |
RelatedCompanies_xml | – name: GE Precision Healthcare LLC |
Score | 3.399129 |
Snippet | An apparatus and method for detecting structural and material defects in a fastener driven during a manufacturing process includes a driving tool capable of... |
SourceID | epo |
SourceType | Open Access Repository |
SubjectTerms | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HAND TOOLS MANIPULATORS PERFORMING OPERATIONS PHYSICS PORTABLE POWER-DRIVEN TOOLS TOOLS OR BENCH DEVICES NOT OTHERWISE PROVIDED FOR, FORFASTENING, CONNECTING, DISENGAGING OR HOLDING TRANSPORTING |
Title | Apparatus And Method For In-Manufacturing Evaluation Of Structural And Material Properties Of Fasteners Using Machine Learning |
URI | https://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220414&DB=EPODOC&locale=&CC=US&NR=2022111496A1 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3dT8IwEL8Q_HxT1PiBpolmb4tjK8M9EAP7CJoMiIDhjWzdZkxMIWzEN_92r-dAnnhse2ty7a73a3v3K8CDaDXiLEtsHd2D0LnZSvTIaho6IgvHyND6IiJJCvt2b8Jfp81pBb7WuTDEE_pN5IhoUQLtvaD1evF_iOVRbGX-GH9i1fw5GLc9rdwdm6bBG1zzum1_OPAGrua67clI679RG5o1d-wO7pX2FJBWTPv-e1flpSy2nUpwAvtD7E8Wp1BJZQ2O3PXbazU4DMsr7xocUIymyLGytMP8DH4QPCrO7lXOOjJhIT0DzYL5kr1IPYzkSuUrUAIi8zd03myQsRHRxSqqjb8Po4L-QDZUZ_JLRa6qpIII514iMGQUUYBiKuIyZSUZ68c53Af-2O3pqNJsM4KzyWhbf-sCqnIu00tgpmE5iYkTJhCIZFYUC4QcgjscZc0n27mC-q6ernc338CxKqrLlwavQxVVTG_RhxfxHQ39L3Afn-w |
link.rule.ids | 230,309,786,891,25594,76903 |
linkProvider | European Patent Office |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3dT8JADG8IfuCbosYP1Es0e1sc2zHcAzGwsYAyIAKGN7JPY2IOwkZ882-3VwfyxOu1u6TX9dretb8DeAjr1SBJIlNF9xCqXK9Hqm_UNBUjC0tL0Pp8Akny-mZnwl-mtWkBvta9MIQT-k3giGhRIdp7Rvv14v8Qy6HayvQx-MSh-bM7bjhKnh3rusarXHFajfZw4AxsxbYbk5HSfyMamjW3zCbmSnt1TAopWXpvyb6UxbZTcY9hf4jziewECrEoQ8lev71WhkMvv_IuwwHVaIYpDuZ2mJ7CDwaPErN7lbKmiJhHz0Azd75kXaF6vljJfgVqQGTtDZw3GyRsRHCxEmrj70M_oz-QDeWZ_FKCq0ou10fdCwwMGVUUIJusuIxZDsb6cQb3bntsd1QUabZZwdlktC2_cQ5FMRfxBTBdM6xIR4WFGIgkhh-EGHKE3OLIqz-Z1iVUds10tZt8B6XO2OvNet3-6zUcSZK8iKnyChRR3PgG_XkW3JIafgEQj6LW |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Apatent&rft.title=Apparatus+And+Method+For+In-Manufacturing+Evaluation+Of+Structural+And+Material+Properties+Of+Fasteners+Using+Machine+Learning&rft.inventor=Beacham%2C+JR.%2C+Jimmie+A&rft.inventor=Jia%2C+Tao&rft.date=2022-04-14&rft.externalDBID=A1&rft.externalDocID=US2022111496A1 |