AI-Enabled Quality Control: Detecting Defects in Manufacturing Processes with Convolutional Neural Networks
This paper aims to establish an improvement upon previous approaches to defect detection, namely, the CNN model and how it is more effective than edge detection and histogram-based methods for manufacturing defects. Overall, the model that used a set of 10,000 images as the training basis presented...
Uloženo v:
| Vydáno v: | International Conference on Computing, Communication and Automation (Online) s. 1 - 6 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
22.11.2024
|
| Témata: | |
| ISSN: | 2642-7354 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | This paper aims to establish an improvement upon previous approaches to defect detection, namely, the CNN model and how it is more effective than edge detection and histogram-based methods for manufacturing defects. Overall, the model that used a set of 10,000 images as the training basis presented an accuracy rate of 98. 5%, precision of 98. 7%, recall of 98. 65%, accuracy of 97. 85%, precision of 97. 22%, recall of 3 %, and an F1-score of 98. Only 5%, which is much better than using traditional methods such as multiple linear regressions. The outlined flexibility in relation to the given form of defects and manufacturing environment proves the model's applicability and efficiency for the purpose of improving quality assurance, which makes it possible asserting its effectiveness. As much as this new advanced AI technology has some limitations especially when it has to do with data demands and computation, the potential that comes with this technology is phenomenal, especially when it comes to defect detection and increased operational efficiency. Based on these observations, the technical suggestions for future improvements are as follows: transfer learning approach, and edge computing to support the model in keeping up optimized learning continually in different manufacturing scenarios. |
|---|---|
| AbstractList | This paper aims to establish an improvement upon previous approaches to defect detection, namely, the CNN model and how it is more effective than edge detection and histogram-based methods for manufacturing defects. Overall, the model that used a set of 10,000 images as the training basis presented an accuracy rate of 98. 5%, precision of 98. 7%, recall of 98. 65%, accuracy of 97. 85%, precision of 97. 22%, recall of 3 %, and an F1-score of 98. Only 5%, which is much better than using traditional methods such as multiple linear regressions. The outlined flexibility in relation to the given form of defects and manufacturing environment proves the model's applicability and efficiency for the purpose of improving quality assurance, which makes it possible asserting its effectiveness. As much as this new advanced AI technology has some limitations especially when it has to do with data demands and computation, the potential that comes with this technology is phenomenal, especially when it comes to defect detection and increased operational efficiency. Based on these observations, the technical suggestions for future improvements are as follows: transfer learning approach, and edge computing to support the model in keeping up optimized learning continually in different manufacturing scenarios. |
| Author | S.Rajarajeswari Vishwakarma, Ravi Vinoth, R. Adnan, Myasar Mundher Prasadarao, J Kaur, Simranjit |
| Author_xml | – sequence: 1 surname: S.Rajarajeswari fullname: S.Rajarajeswari email: s.rajarajeswari_maths@psvpec.in organization: Padmavathy Engineering College,Prince Shri Venkateshwara,Chennai,India – sequence: 2 givenname: J surname: Prasadarao fullname: Prasadarao, J email: John211286@gmail.com organization: Aditya Engineering College,Department of Computer Science & Engineering,Surampalem,India – sequence: 3 givenname: Myasar Mundher surname: Adnan fullname: Adnan, Myasar Mundher email: Maiser.monther@iunajaf.edu.iq organization: The Islamic University,College of Technical Engineering,Department of Computers Techniques Engineering,Najaf,Iraq – sequence: 4 givenname: Simranjit surname: Kaur fullname: Kaur, Simranjit email: Simranjit2661.research@cgcjhanjeri.in organization: Chandigarh Group of Colleges,Chandigarh Engineering College,Department of Computer Application,Mohali,Punjab,India – sequence: 5 givenname: Ravi surname: Vishwakarma fullname: Vishwakarma, Ravi email: ravi.research@iesuniversity.ac.in organization: IES University,IES Institute of Technology and Management,Department of Mechanical Engineering,Bhopal,M.P.,India – sequence: 6 givenname: R. surname: Vinoth fullname: Vinoth, R. email: vinothcse@rmkcet.ac.in organization: RMK College of Engineering and Technology,Department of Computer Science and Engineering,Chennai,India |
| BookMark | eNo1UM1OAyEYRKOJtfYNPOADbOWDXVi8NWvVJq0_Se8NUFDsyhpgbfr2tv6cZjKTmWTmHJ2ELliEroCMAYi8njWTpllwABBjSmh5UCsJII_QSApZs4owLqRgx2hAeUkLwaryDI1SeieEMKhlLdkAbSazYhqUbu0av_Sq9XmHmy7k2LU3-NZma7IPr3vm9ixhH_BChd4pk_t4MJ5jZ2xKNuGtz2-H6FfX9tl3QbX40fbxB_K2i5t0gU6dapMd_eEQLe-my-ahmD_d7-fMCy9ZLpzTRrla8IoyxTkwrcEYqByApmQtCJEMhKHEWO2EEsIKV3JqNdNM1qDYEF3-1npr7eoz-g8Vd6v_e9g3-chd7A |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ICACCM61117.2024.11059119 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISBN | 9798350367973 |
| EISSN | 2642-7354 |
| EndPage | 6 |
| ExternalDocumentID | 11059119 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IF 6IL 6IN AAJGR AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK OCL RIE RIL |
| ID | FETCH-LOGICAL-i93t-ffbcaf876523a6613bb1cc15f11b20d7009317c20cebf7a77e7f462eb3b3981a3 |
| IEDL.DBID | RIE |
| IngestDate | Wed Aug 06 17:58:33 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i93t-ffbcaf876523a6613bb1cc15f11b20d7009317c20cebf7a77e7f462eb3b3981a3 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_11059119 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-Nov.-22 |
| PublicationDateYYYYMMDD | 2024-11-22 |
| PublicationDate_xml | – month: 11 year: 2024 text: 2024-Nov.-22 day: 22 |
| PublicationDecade | 2020 |
| PublicationTitle | International Conference on Computing, Communication and Automation (Online) |
| PublicationTitleAbbrev | ICACCM |
| PublicationYear | 2024 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0003189893 |
| Score | 1.8901273 |
| Snippet | This paper aims to establish an improvement upon previous approaches to defect detection, namely, the CNN model and how it is more effective than edge... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Accuracy Chipper Text Policy-Attribute Based Encryption Algorithm Cloud Computing Computational modeling Convolutional neural networks Defect detection Encryption Encryption Time Image edge detection Manufacturing Manufacturing processes Multilevel authentication Novel Lightweight Cryptography Method Technology Quality assurance Transfer learning |
| Title | AI-Enabled Quality Control: Detecting Defects in Manufacturing Processes with Convolutional Neural Networks |
| URI | https://ieeexplore.ieee.org/document/11059119 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEA5aRPTiq-KbCF7TbrLZJOutrC0WbOmhSG8lr4WibKXdFvz3Jtlt1YMHTwkhgZBMMpnJfN8A8MCkEwqqLRI0IYhiHSMhhUKpu_tULBOmQpaI1xc-HIrJJB3VYPWAhbHWhuAz2_LV8Jdv5nrlXWVt7B8D2JN87nLOKrDW1qHihDN1yncf3Nc8mu1-1smyAXOnmTtDkNDWZvyvTCpBkfSO_jmFY9D8huTB0VbZnIAdW5yCwx9sgmfgrdNH3QCFMrCixviEWRWJ_gifrP8tcP1cLURwwFkBB7JYeWRDgCrCGjNgl9A7Z_3QdS2X8h16Eo9QhKjxZROMe91x9ozqXApolsYlynOlZe5uPmd3SqeSY6Ww1jjJMVYkMtw7NjDXJNJW5VxybnlOGXGWtopTgWV8DhrFvLAXAIrIulcYx6kwObWUCZMmRkpmFCVGsugSNP2yTT8qtozpZsWu_mi_Bgd-czy-j5Ab0CgXK3sL9vS6nC0Xd2GPvwCj4KiI |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEA5SxcfFV8W3EbymbrLZJOutrC0ttqWHIr2VvBaKspW-wH9vkm6rHjx42rAkISSTTGYy3zcAPDDphIJqiwRNCKJYx0hIoVDqzj4Vy4SpkCXitcN7PTEcpv0SrB6wMNbaEHxma74Y3vLNRC-8q-wR-8sA9iSf2z51VgnX2rhUnHimTv3ugvuSSfOxndWzrMvcfubOFCS0tu7hVy6VoEqah_8cxBGofoPyYH-jbo7Bli1OwMEPPsFT8FZvo0YAQxm4Isf4hNkqFv0JPlv_XuDquVKI4YDjAnZlsfDYhgBWhCVqwM6gd8_6pstSMuU79DQe4RPixmdVMGg2BlkLldkU0DiN5yjPlZa5O_uc5SmdUo6VwlrjJMdYkchw79rAXJNIW5VzybnlOWXE2doqTgWW8RmoFJPCngMoIuvuYRynwuTUUiZMmhgpmVGUGMmiC1D10zb6WPFljNYzdvnH_zuw1xp0O6NOu_dyBfb9Qnm0HyHXoDKfLuwN2NHL-Xg2vQ3r_QWX_qvR |
| 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%3Abook&rft.genre=proceeding&rft.title=International+Conference+on+Computing%2C+Communication+and+Automation+%28Online%29&rft.atitle=AI-Enabled+Quality+Control%3A+Detecting+Defects+in+Manufacturing+Processes+with+Convolutional+Neural+Networks&rft.au=S.Rajarajeswari&rft.au=Prasadarao%2C+J&rft.au=Adnan%2C+Myasar+Mundher&rft.au=Kaur%2C+Simranjit&rft.date=2024-11-22&rft.pub=IEEE&rft.eissn=2642-7354&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FICACCM61117.2024.11059119&rft.externalDocID=11059119 |