Machine vision-based gradient-boosted tree and support vector regression for tool life prediction in turning

Basit Öğe Kaydı
dc.authorscopusid57222198238
dc.authorscopusid55987845500
dc.authorscopusid56916167500
dc.authorscopusid57206785879
dc.authorscopusid55642814700
dc.authorscopusid56925609000
dc.authorscopusid57743372300
dc.contributor.authorBagga, Prashant J.
dc.contributor.authorPatel, K.M.
dc.contributor.authorMakhesana, M.A.
dc.contributor.authorŞirin, Şenol
dc.contributor.authorKhanna, N.
dc.contributor.authorKrolczyk, G.M.
dc.contributor.authorPala, A.D.
dc.date.accessioned2023-07-26T11:50:54Z
dc.date.available2023-07-26T11:50:54Z
dc.date.issued2023
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractOne of the essential elements of automated and intelligent machining processes is accurately predicting tool life. It also helps in achieving the goal of producing quality products with reduced production costs. This work proposes a computer vision-based tool wear monitoring and tool life prediction system using machine learning methods. Gradient-boosted trees and support vector machine (SVM) techniques are used to predict tool life. The experimental investigation on the CNC machine is conducted to study the applicability of the proposed tool wear monitoring system. Experiments are performed using workpiece material made of alloy steel and PVD-coated cutting inserts, and flank wear is monitored. An imaging system consisting of an industrial camera, lens, and LED ring light is mounted on the machine to capture tool wear zone images. Images are then processed by algorithms developed in MATLAB®. Boosted tree methods and the SVM methodology have 96% and 97% prediction accuracy, respectively. Validation tests are carried out to determine the accuracy of proposed models. It is observed that the prediction accuracy of boosted three and SVM is good, with a maximum error of 5.89% and 7.56%, respectively. The outcome of the study established that the developed system can monitor the tool wear with good accuracy and can be adopted in industries to optimize the utilization of tool inserts. © 2023, The Author(s).en_US
dc.description.sponsorshipNirma University, NUen_US
dc.description.sponsorshipThe authors would like to acknowledge the resources and support provided by Nirma University in the form of the Minor Research Project grant to carry out the research work.en_US
dc.identifier.doi10.1007/s00170-023-11137-2
dc.identifier.issn0268-3768
dc.identifier.scopus2-s2.0-85148946451en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s00170-023-11137-2
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12453
dc.identifier.wosWOS:000940340200005en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorŞirin, Şenol
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofInternational Journal of Advanced Manufacturing Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectCutting tool wearen_US
dc.subjectGradient-boosted treeen_US
dc.subjectMachine visionen_US
dc.subjectSupport vector machineen_US
dc.subjectTool lifeen_US
dc.subjectTurningen_US
dc.subjectAlloy steelen_US
dc.subjectComputer control systemsen_US
dc.subjectComputer visionen_US
dc.subjectForecastingen_US
dc.subjectSupport vector machinesen_US
dc.subjectTurningen_US
dc.subjectWear of materialsen_US
dc.subjectCutting tool wearen_US
dc.subjectGradient-boosted treeen_US
dc.subjectLife predictionsen_US
dc.subjectMachine-visionen_US
dc.subjectPrediction accuracyen_US
dc.subjectSupport vectors machineen_US
dc.subjectTool lifeen_US
dc.subjectTool wearen_US
dc.subjectTool wear monitoringen_US
dc.subjectVision baseden_US
dc.subjectCutting toolsen_US
dc.titleMachine vision-based gradient-boosted tree and support vector regression for tool life prediction in turningen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
12453.pdf
Boyut:
1.7 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu