MS-SSPCANet: A powerful deep learning framework for tool wear prediction

Duan, Jian; Hu, Cheng; Zhan, Xueying; Zhou, Hongdi; Liao, Guanglan; Shi, Tielin

doi:10.1016/j.rcim.2022.102391

Cited by 38 publications

(9 citation statements)

References 47 publications

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“…Non-contact measurement technology mainly uses computer vision to detect the yarn roll. It can be divided into monocular vision method, stereo vision method, and deep learning method [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ].…”

Section: Related Workmentioning

confidence: 99%

The Detection of Yarn Roll’s Margin in Complex Background

Wang

Shi

et al. 2023

Sensors

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Online detection of yarn roll’s margin is one of the key issues in textile automation, which is related to the speed and scheduling of bobbin (empty yarn roll) replacement. The actual industrial site is characterized by uneven lighting, restricted shooting angles, diverse yarn colors and cylinder yarn types, and complex backgrounds. Due to the above characteristics, the neural network detection error is large, and the contour detection extraction edge accuracy is low. In this paper, an improved neural network algorithm is proposed, and the improved Yolo algorithm and the contour detection algorithm are integrated. First, the image is entered in the Yolo model to detect each yarn roll and its dimensions; second, the contour and dimensions of each yarn roll are accurately detected based on Yolo; third, the diameter of the yarn rolls detected by Yolo and the contour detection algorithm are fused, and then the length of the yarn rolls and the edges of the yarn rolls are calculated as measurements; finally, in order to completely eliminate the error detection, the yarn consumption speed is used to estimate the residual yarn volume and the measured and estimated values are fused using a Kalman filter. This method overcomes the effects of complex backgrounds and illumination while being applicable to different types of yarn rolls. It is experimentally verified that the average measurement error of the cylinder yarn diameter is less than 8.6 mm, and the measurement error of the cylinder yarn length does not exceed 3 cm.

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Section: Related Workmentioning

confidence: 99%

The Detection of Yarn Roll’s Margin in Complex Background

Wang

Shi

et al. 2023

Sensors

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“…Considering signals collected from manufacturing site most are nonstationary, Short-Time Fourier transform (STFT) is widely used to get time-frequency image [31,32], which reflects the distribution of signal power along the time and frequency axis. A time-frequency matrix ( , )…”

Section: 13tfdviuidtdxiivoitodtaveditodtotod-oidquddo T Ivphmentioning

confidence: 99%

Tool condition monitoring for cavity milling based on bispectrum analysis and Bayesian optimized SVM

Li¹,

Wang

Hu³

et al. 2023

Preprint

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Tool wear status seriously affects the dimensional accuracy and surface quality of the machined parts. Therefore, tool condition monitoring (TCM) is essential in the milling process of aerospace structural parts due to the use of difficult-to-cut materials and complex cutting trajectories. The Higher order spectrum (HOS) was first employed to analyze the vibration signals, and then bispectral features extracted from de-noised signals were used to characterize the tool wear status. The improved Pearson’s correlation coefficient was used for feature selection to reduce the influence of periodic components on feature selection process. Furthermore, a novel objective function was proposed to guide the hyperparameters optimization process of support vector machine based on Bayesian optimization algorithm, in which the effect of imbalanced data on the recognition rates was considered. To demonstrate the effectiveness of the proposed method, a structural part milling experiment was performed on a vertical machining center and vibration signals of spindle were collected. Based on this, an online TCM model was established. The present study suggests that the proposed TCM system is accurate and robust.

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“…Data analysis relies on an intelligent method to recognize or detect specific states. Deep learning techniques are known for their powerful learning capability, and are currently the most popular approach in many applications [301][302][303][304]. However, deep learning-based methods require large datasets, which presents a challenge for the DDT system.…”

Section: B Unsupervised Data Analysismentioning

confidence: 99%

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

Lou

Xing

et al. 2022

IEEE Trans. Intell. Veh.

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Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design of the automated vehicle, whereas the digitization of the human driver, who plays an important role in driving, is largely ignored. Furthermore, previous driver-related tasks are limited to specific scenarios and have limited applicability. Thus, a novel concept of a driver digital twin (DDT) is proposed in this study to bridge the gap between existing automated driving systems and fully digitized ones and aid in the development of a complete driving human cyber-physical system (H-CPS). This concept is essential for constructing a harmonious human-centric intelligent driving system that considers the proactivity and sensitivity of the human driver. The primary characteristics of the DDT include multimodal state fusion, personalized modeling, and time variance. Compared with the original DT, the proposed DDT emphasizes on internal personality and capability with respect to the external physiological-level state. This study systematically illustrates the DDT and outlines its key enabling aspects. The related technologies are comprehensively reviewed and discussed with a view to improving them by leveraging the DDT. In addition, the potential applications and unsettled challenges are considered. This study aims to provide fundamental theoretical support to researchers in determining the future scope of the DDT system.

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MS-SSPCANet: A powerful deep learning framework for tool wear prediction

Cited by 38 publications

References 47 publications

The Detection of Yarn Roll’s Margin in Complex Background

The Detection of Yarn Roll’s Margin in Complex Background

Tool condition monitoring for cavity milling based on bispectrum analysis and Bayesian optimized SVM

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

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