On-machine detection of face milling cutter damage based on machine vision

Qu, Jiaxu; Yue, Caixu; Zhou, Jiaqi; Xia, Wei; Liu, Xianli; Liang, Steven Y.

doi:10.1007/s00170-024-13818-y

Int J Adv Manuf Technol

2024

DOI: 10.1007/s00170-024-13818-y

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On-machine detection of face milling cutter damage based on machine vision

Jiaxu Qu,

Caixu Yue,

Jiaqi Zhou

et al.

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2024

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Cited by 2 publications

References 28 publications

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Sustainable Machining: A Case Study on Face Milling of AISI 1045 Steel Using a Multi-Objective Optimization Approach

Roshan,

Sumesh,

Balaji

et al. 2024

Int J Interact Des Manuf

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Sustainable Machining: A Case Study on Face Milling of AISI 1045 Steel Using a Multi-Objective Optimization Approach

Roshan,

Sumesh,

Balaji

et al. 2024

Int J Interact Des Manuf

View full text Add to dashboard Cite

A Machine Vision-Based Measurement Method for the Concentricity of Automotive Brake Piston Components

Ge,

Li,

Zhao

et al. 2024

Symmetry

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The concentricity error of automotive brake piston components critically affects the stability and reliability of the brake system. Traditional contact-based concentricity measurement methods are inefficient. In order to address the issue of low detection efficiency, this paper proposes a non-contact concentricity measurement method based on the combination of machine vision and image processing technology. In this approach, an industrial camera is employed to capture images of the measured workpiece’s end face from the top of the spring. The edge contours are extracted through the implementation of image preprocessing algorithms, which are then followed by the calculation of the outer circle center and the fitting of the inner circle center. Finally, the concentricity error is calculated based on the coordinates of the inner and outer circle centers. The experimental results demonstrate that, in comparison to a coordinate measuring machine (CMM), this method exhibits a maximum error of only 0.0393 mm and an average measurement time of 3.9 s. This technology markedly enhances the efficiency of measurement and fulfills the industry’s requirement for automated inspection. The experiments confirmed the feasibility and effectiveness of this method in practical engineering applications, providing reliable technical support for the online inspection of automotive brake piston components. Moreover, this methodology can be extended to assess concentricity in other complex stepped shaft parts.

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On-machine detection of face milling cutter damage based on machine vision

Cited by 2 publications

References 28 publications

Sustainable Machining: A Case Study on Face Milling of AISI 1045 Steel Using a Multi-Objective Optimization Approach

Sustainable Machining: A Case Study on Face Milling of AISI 1045 Steel Using a Multi-Objective Optimization Approach

A Machine Vision-Based Measurement Method for the Concentricity of Automotive Brake Piston Components

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