Deep learning-based monitoring of surface residual stress and efficient sensing of AE for laser shock peening

Zhang, Zhifen; Qin, Rui; Lin, Geng; Du, Zhengyao; Li, Zhiwen; Lin, Yuan-Yao; He, Weifeng

doi:10.1016/j.jmatprotec.2022.117515

Cited by 24 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A quality assessment is then made by comparing the processed signal to a standard signal. [152][153][154] PVDF film can also be used for real-time monitoring. In this approach, a PVDF piezoelectric film is placed on the back side of the workpiece.…”

Section: Stability Of Absorbing and Confinement Overlaysmentioning

confidence: 99%

A Critical Review of Laser Shock Peening of Aircraft Engine Components

Huang

Zou³

et al. 2023

Adv Eng Mater

View full text Add to dashboard Cite

Many aviation accidents are caused by the failure of aircraft engine components, and engine blades are especially vulnerable to high‐cycle fatigue fracture in severe working environments as well as to impact damage caused by foreign objects. To address this problem, the United States took the lead and has been successful in implementing laser shock peening (LSP) as a surface treatment for aircraft engine components to enhance their fatigue performance. This review provides an overview of the development of LSP for use in treating aircraft engine components over the past three decades, with a brief introduction to the development of high‐energy pulsed lasers for LSP. A particular focus of this review is on the limitations and challenges associated with the application of LSP for treating critical aircraft engine components. It is hoped that this review serves as a reference for future research and development that can lead to better performance of these components.

show abstract

Section: Stability Of Absorbing and Confinement Overlaysmentioning

confidence: 99%

A Critical Review of Laser Shock Peening of Aircraft Engine Components

Huang

Zou³

et al. 2023

Adv Eng Mater

View full text Add to dashboard Cite

show abstract

“…Hence, many researchers have turned to employ deep learning algorithms to address issues in additive manufacturing, including design optimization, real-time monitoring, performance prediction, and energy management [13][14][15][16]. Some researchers collect large amounts of data through simulations or experiments that can be used to train deep learning models that predict the mechanical properties of the parts [17,18]. These research place too much emphasis on using the powerful computing power of deep learning models to summarize potential laws based on big data and make predictions in subsequent applications.…”

Section: Introductionmentioning

confidence: 99%

Detecting defects in fused deposition modeling based on improved YOLO v4

Xu,

Zhang,

et al. 2023

Mater. Res. Express

View full text Add to dashboard Cite

Fused deposition modeling comes with many conveniences for the manufacturing industry, but many defects tend to appear in actual production due to the problems of the FDM mechanism itself. Although some deep learning-based object detection models show excellent performance in detecting defects in the additive manufacturing process, their detection efficiency is relatively low, and they are prone to drawbacks in the face of large numbers of defects. In this paper, an improved model based on the YOLO v4 network structure is developed. We lightweight the model and modify its loss function to achieve better performance. Experimental results show that the improved model, MobileNetV2-YOLO v4, achieves a mAP of 98.96% and an FPS of 50.8 after training, which obtains higher detection accuracy and faster detection speed than the original YOLO v4 algorithm model. Through testing, this improved model can accurately identify the location and information of target defects, which has great potential for real-time detection in the additive manufacturing process.

show abstract

“…For example, Zhang et al [ 25 ] proposed a dynamic model that combines CNN and LSTM networks to account for the detected AE signals, which can accurately monitor the surface quality during laser shock peening. Shi et al [ 26 ] developed a novel deep learning algorithm (BiConvLSTM) that combines CNN and LSTM networks for planetary gearbox fault diagnosis under different operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Laser Welding Penetration Monitoring Based on Time-Frequency Characterization of Acoustic Emission and CNN-LSTM Hybrid Network

Luo

Zhang

et al. 2023

Materials

View full text Add to dashboard Cite

In-process penetration monitoring of the pulsed laser welding process remains a great challenge for achieving uniform and reproducible products due to the highly complex nature of the keyhole dynamics within the intense laser-metal interactions. The main purpose of this study is to investigate the feasibility of acoustic emission (AE) measurement for penetration monitoring based on acoustic wave characteristics and deep learning. Firstly, a series of laser welding experiments on aluminum alloys were conducted using high-speed photography and AE techniques. This allowed us to in-situ visualize the complete keyhole dynamics and elucidate the generation mechanism of acoustic waves originating from pressure fluctuations at the keyhole wall. Then, an adaptive time-frequency technique namely VMD (Variational Mode Decomposition) was proposed to characterize the acoustic energy distribution among the nine subsignals with low-frequency and high-frequency components under different welding penetrations. Lastly, a novel hybrid model combing CNN (Convolutional Neural Network) and LSTM (Long Short Term Memory) was designed to deeply mine the spatial and temporal acoustic features from the extracted frequency components. Extensive experiments demonstrate that our proposed approach yields a remarkable classification performance with a test accuracy of 99.8% and a standard deviation of 0.21, which obtains a high recognition rate. This work is a new paradigm in the digitization and intelligence of the laser welding process and contributes to an alternative way of developing an efficient end-to-end penetration monitoring system.

show abstract

Deep learning-based monitoring of surface residual stress and efficient sensing of AE for laser shock peening

Cited by 24 publications

References 29 publications

A Critical Review of Laser Shock Peening of Aircraft Engine Components

A Critical Review of Laser Shock Peening of Aircraft Engine Components

Detecting defects in fused deposition modeling based on improved YOLO v4

Laser Welding Penetration Monitoring Based on Time-Frequency Characterization of Acoustic Emission and CNN-LSTM Hybrid Network

Contact Info

Product

Resources

About