Publication:
Dissociation artificial neural network for tool wear estimation in CNC milling

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dc.citedby9
dc.contributor.authorWong S.Y.en_US
dc.contributor.authorChuah J.H.en_US
dc.contributor.authorYap H.J.en_US
dc.contributor.authorTan C.F.en_US
dc.contributor.authorid57216689998en_US
dc.contributor.authorid50161306600en_US
dc.contributor.authorid57951575600en_US
dc.contributor.authorid35788387200en_US
dc.date.accessioned2024-10-14T03:18:58Z
dc.date.available2024-10-14T03:18:58Z
dc.date.issued2023
dc.description.abstractTool wear in CNC milling is a gradual process which significantly affects product quality. Left unmonitored, it could increase risks of tool breakage, leading to losses due to scrap and equipment damage. A modular neural network (MNN), the dissociation artificial neural network (Dis-ANN), was proposed in this paper for tool wear prediction. The Dis-ANN consists of a modular structure constructed out of parallel ANN modules (referred to as the dissociation unit), connected to an intermediary. The output of each ANN module is dependent on input feature vectors formed from the concatenation of both previous and current feature values, allowing each module to account for feature trends. Dis-ANN was validated using the Slot Milling Dataset (collected in the University of Malaya workshop) and the 2010 PHM Data Challenge Dataset. The Slot Milling Dataset contains data in the form of images of machined workpiece surfaces and acoustic signals during milling. In order to account for uneven lighting in each workpiece surface image, image features were extracted by processing grey-level co-occurrence matrix�based texture descriptors of different non-overlapping sections within the same image. For model validation using the 2010 PHM Data Challenge Dataset, Dis-ANN was validated using features extracted from the dataset under two conditions � with and without the addition of random feature noise. Results showed Dis-ANN was better at learning complex non-linear relationships while showing higher robustness to input feature noise compared to linear regression, support vector regression, and monolithic ANN. Furthermore, the modular design of Dis-ANN facilitated model architecture optimization to minimize network redundancy. � 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s00170-022-10737-8
dc.identifier.epage901
dc.identifier.issue1-Feb
dc.identifier.scopus2-s2.0-85145500444
dc.identifier.spage887
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85145500444&doi=10.1007%2fs00170-022-10737-8&partnerID=40&md5=2f8082951c30e7b0244e3394041d06de
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34308
dc.identifier.volume125
dc.pagecount14
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Advanced Manufacturing Technology
dc.subjectCNC milling
dc.subjectModular neural network
dc.subjectRemaining useful life
dc.subjectTool condition monitoring
dc.subjectTool wear
dc.subjectCondition monitoring
dc.subjectCutting tools
dc.subjectDissociation
dc.subjectMilling (machining)
dc.subjectNeural networks
dc.subjectWear of materials
dc.subjectCNC-milling
dc.subjectData challenges
dc.subjectInput features
dc.subjectModular neural networks
dc.subjectProducts quality
dc.subjectRemaining useful lives
dc.subjectTool condition monitoring
dc.subjectTool wear
dc.subjectTool wear estimations
dc.subjectWorkpiece
dc.subjectTextures
dc.titleDissociation artificial neural network for tool wear estimation in CNC millingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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