Machine vision based condition monitoring and fault diagnosis of machine tools using information from machined surface texture: A review

Liu, Yuekai; Guo, Liang; Gao, Hongli; You, Zhichao; Ye, Yunguang; Zhang, Bin

doi:10.1016/j.ymssp.2021.108068

Cited by 96 publications

(29 citation statements)

References 193 publications

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“…e vision sensor can obtain original images for MVS processing. Visual sensors analyze the captured images through comparison with the reference image in memory [11,12].…”

Section: Calculation Model Of Player's Ball Receivingmentioning

confidence: 99%

The Selecting Optimal Ball-Receiving Body Parts Using Pose Sequence Analysis and Sports Biomechanics

2022

Mobile Information Systems

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The purpose is to explore a personalized and targeted training mode in football player training. Firstly, this work introduces the principle and advantages of machine vision sensing. Secondly, from the biomechanical point of view, the influence of the acceleration of several joints and the joint angle on the ball receiving effect is analyzed. Furthermore, the football player’s in-game receiving image is collected using machine vision technology, and the pose image data are preprocessed to construct a data set. Then, a new model is constructed and trained using Haar-like feature (HLF) and (Adaptive Boosting (Adaboost). Finally, the recognition model of the football receiving pose is tested, and the recognition effect is compared with the mainstream recognition model. The results show that the recognition parameter of the traditional method based on the Halcon recognition pose system is 5.12 at 20 times and then begins to decline. In contrast, the identification parameters based on the Industrial Robot Vision System Development (IRVSD) platform are much higher than those based on Halcon. It slightly decreases when the training times are 60 and then gradually increases. However, the recognition parameters based on the proposed machine vision have been far higher than those of the two traditional methods and maintained at about 10. This is because the proposed method extracts the foul image features, establishes the pose sequence potential function, and analyzes it in more detail, thus improving the recognition accuracy. The player’s pose recognition model based on HLF and AdaBoost algorithm can identify and evaluate the ball-receiving pose, thus guiding the receiving improvement. The finding shows that the proposed recognition technology can recognize and evaluate the players’ ball-receiving image, providing a new direction for applying artificial intelligence technology in sports.

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“…e vision sensor can obtain original images for MVS processing. Visual sensors analyze the captured images through comparison with the reference image in memory [11,12].…”

Section: Calculation Model Of Player's Ball Receivingmentioning

confidence: 99%

The Selecting Optimal Ball-Receiving Body Parts Using Pose Sequence Analysis and Sports Biomechanics

2022

Mobile Information Systems

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“…For examining the quality of the machined surfaces, e.g., the roughness of the surface, the technical descriptions are hard to be assured using a simple one-step process. Also, the regular initial judgment of the quality of the machined part is based on empirical rules by manually observing the machining time and noise of the processing method [124]. So, in comparison with the traditional methods for quality examination, the machine Vision is capable of evaluating the roughness quality of the surface with a higher speed and is able to detect the irregularities without scraping the surface [125].…”

Section: Figure 38mentioning

confidence: 99%

Surface Characteristics Measurement Using Computer Vision: A Review

Hashmi¹,

Mali²,

Meena³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured. For any components to execute their intended functions and operations, surface quality is considered equally significant to dimensional quality. Surface Roughness (Ra) is a widely recognized measure to evaluate and investigate the surface quality of machined parts. Various conventional methods and approaches to measure the surface roughness are not feasible and appropriate in industries claiming 100% inspection and examination because of the time and efforts involved in performing the measurement. However, Machine vision has emerged as the innovative approach to executing the surface roughness measurement. It can provide economic, automated, quick, and reliable solutions. This paper discusses the characterization of the surface texture of surfaces of traditional or non-traditional manufactured parts through a computer/machine vision approach and assessment of the surface characteristics, i.e., surface roughness, waviness, flatness, surface texture, etc., machine vision parameters. This paper will also discuss multiple machine vision techniques for different manufacturing processes to perform the surface characterization measurement.

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“…The machined surface images could provide rich geometrical characteristics for diagnosing the soundness of the state of cutting tools [ 18 ]. The massive and unstructured raw image data brings new opportunities and challenges to vison-based tool condition monitoring.…”

Section: Introductionmentioning

confidence: 99%

Tool Wear Monitoring in Milling Based on Fine-Grained Image Classification of Machined Surface Images

Yang

Duan

et al. 2022

Sensors

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Cutting tool wear state assessment during the manufacturing process is extremely significant. The primary purpose of this study is to monitor tool wear to ensure timely tool change and avoid excessive tool wear or sudden tool breakage, which causes workpiece waste and could even damage the machine. Therefore, an intelligent system, that is efficient and precise, needs to be designed for addressing these problems. In our study, an end-to-end improved fine-grained image classification method is employed for workpiece surface-based tool wear monitoring, which is named efficient channel attention destruction and construction learning (ECADCL). The proposed method uses a feature extraction module to extract features from the input image and its corrupted images, and adversarial learning is used to avoid learning noise from corrupted images while extracting semantic features by reconstructing the corrupted images. Finally, a decision module predicts the label based on the learned features. Moreover, the feature extraction module combines a local cross-channel interaction attention mechanism without dimensionality reduction to characterize representative information. A milling dataset is conducted based on the machined surface images for monitoring tool wear conditions. The experimental results indicated that the proposed system can effectively assess the wear state of the tool.

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Machine vision based condition monitoring and fault diagnosis of machine tools using information from machined surface texture: A review

Cited by 96 publications

References 193 publications

The Selecting Optimal Ball-Receiving Body Parts Using Pose Sequence Analysis and Sports Biomechanics

The Selecting Optimal Ball-Receiving Body Parts Using Pose Sequence Analysis and Sports Biomechanics

Surface Characteristics Measurement Using Computer Vision: A Review

Tool Wear Monitoring in Milling Based on Fine-Grained Image Classification of Machined Surface Images

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