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

Yusuf Özçevik () and Fikret Sönmez
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Yusuf Özçevik: Manisa Celal Bayar University
Fikret Sönmez: Manisa Celal Bayar University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 3, No 6, 1623-1635

Abstract: Abstract Turning 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.

Keywords: Turning; Machine learning; Chip morphology; TensorFlow lite; Mobile application (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-023-02320-z

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