A computer vision system for saw blade condition monitoring

Jourdan, Nicolas; Biegel, Tobias; Knauthe, Volker; Buelow, Max von; Guthe, Stefan; Metternich, Joachim

doi:10.1016/j.procir.2021.11.186

Cited by 8 publications

(1 citation statement)

References 20 publications

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“…Recent papers have included significant work in imagebased tool condition monitoring [22,23]. Deep learning for tool condition monitoring has been well articulated in [24].…”

Section: The Proposed Methodsmentioning

confidence: 99%

Sensor fusion and the application of artificial intelligence to identify tool wear in turning operations

Al-Azmi

Al‐Habaibeh

Abbas

2023

Int J Adv Manuf Technol

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This paper aims to develop an effective sensor fusion model for turning processes for the detection of tool wear. Fusion of sensors’ data combined with novelty detection algorithm and learning vector quantisation (LVQ) neural networks is used to detect tool wear and present diagnostic and prognostic information. To reduce the number of sensors required in the monitoring system and support sensor fusion, the ASPS approach (Automated Sensor and Signal Processing Selection System) is used to select the most appropriate sensors and signal processing methods for the design of the condition monitoring system. The experimental results show that the proposed approach has demonstrated its efficacy in the implementation of an effective solution for the monitoring tool wear in turning. The results prove that the fusion of sensitive sensory characteristic features and the use of AI methods have been successful for the detection and prediction of the tool wear in turning processes and show the capability of the proposed approach to reduce the complexity of the design of condition monitoring systems and the development of a sensor fusion system using a self-learning method.

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“…Recent papers have included significant work in imagebased tool condition monitoring [22,23]. Deep learning for tool condition monitoring has been well articulated in [24].…”

Section: The Proposed Methodsmentioning

confidence: 99%

Sensor fusion and the application of artificial intelligence to identify tool wear in turning operations

Al-Azmi

Al‐Habaibeh

Abbas

2023

Int J Adv Manuf Technol

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Overview of the development of wear in bi-metal band saw blades

Fu,

Liu,

Chen

et al. 2023

Int J Adv Manuf Technol

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Digital-based production: introduction and fields of application

Metternich¹,

Kreß²

2023

Prod. Eng. Res. Devel.

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This special issue on “digital-based production” gives an overview about the current research on the integration of digital technologies into production processes and their fields of application. It covers topics from Industry 4.0, artificial intelligence and data analytics to the Industrial Internet of Things and Cyber-Physical Production Systems. This issue offers valuable insights for those interested in improving production efficiency, quality, and sustainability through digital technologies. In this foreword, we describe promising application fields of digital-based production and classify the submitted articles accordingly.

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A computer vision system for saw blade condition monitoring

Cited by 8 publications

References 20 publications

Sensor fusion and the application of artificial intelligence to identify tool wear in turning operations

Sensor fusion and the application of artificial intelligence to identify tool wear in turning operations

Overview of the development of wear in bi-metal band saw blades

Digital-based production: introduction and fields of application

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