An embedded TensorFlow lite model for classification of chip images with respect to chip morphology depending on varying feed

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dc.contributor.authorÖzçevik Y.
dc.contributor.authorSönmez F.
dc.date.accessioned2024-07-22T08:01:56Z
dc.date.available2024-07-22T08:01:56Z
dc.date.issued2024
dc.description.abstractTurning is one of the fundamental machining processes used to produce superior machine parts. It is critical to manage the machining conditions to maintain the desired properties of the final product. Chip morphology and chip control are crucial factors to be monitored. In particular, the selection of an appropriate feed has one of the most significant effects. On the other hand, machine learning is an advanced approach that is continuously evolving and helping many industries. Moreover, mobile applications with learning models have been deployed in the field, recently. Taking these motivations into account, in this study, we propose a practical mobile application that includes an embedded learning model to provide chip classification based on chip morphology. For this purpose, a dataset of chips with different morphological properties is obtained and manually labeled according to ISO 3685 standards by using 20 different feeds on AISI 4140 material. Accordingly, TensorFlow Lite is used to train a learning model, and the model is embedded into a real-time Android mobile application. Eventually, the final software is evaluated through experiments conducted on the dataset and in the field, respectively. According to the evaluation results, it can be stated that the learning model is able to predict chip morphology with a test accuracy of 85.4%. Moreover, the findings obtained from the real-time mobile application satisfy the success rate by practical usage. As a result, it can be concluded that such attempts can be utilized in the turning process to adjust the relevant feed conditions. © The Author(s) 2024.
dc.identifier.DOI-ID10.1007/s10845-023-02320-z
dc.identifier.issn09565515
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/11659
dc.language.isoEnglish
dc.publisherSpringer
dc.rightsAll Open Access; Hybrid Gold Open Access
dc.subjectImage classification
dc.subjectLearning systems
dc.subjectMachine learning
dc.subjectMobile computing
dc.subjectMorphology
dc.subjectChip image
dc.subjectChip morphologies
dc.subjectLearning models
dc.subjectMachine-learning
dc.subjectMachining conditions
dc.subjectMachining Process
dc.subjectMobile applications
dc.subjectProperty
dc.subjectReal- time
dc.subjectTensorflow lite
dc.subjectTurning
dc.titleAn embedded TensorFlow lite model for classification of chip images with respect to chip morphology depending on varying feed
dc.typeArticle

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