Noncontact Surface Roughness Estimation Using 2D Complex Wavelet Enhanced ResNet for Intelligent Evaluation of Milled Metal Surface Quality

Sun, Weifang; Yao, Bin; Chen, Binqiang; He, Yong; Xiao, Cao; Zhou, Tianxiang; Liu, Huigang

doi:10.3390/app8030381

Cited by 36 publications

(24 citation statements)

References 40 publications

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“…In recent years, CNN has been widely used in object recognition, detection, and scene understanding [22]. CNN makes it possible to design an end-to-end deep network for identification of shaft orbits.…”

Section: Identification Methods Of Fine-grained Shaft Orbits Based On mentioning

confidence: 99%

Fine-grained fault recognition method for shaft orbit of rotary machine based on convolutional neural network

Wu¹,

Feng²,

Sun³

et al. 2019

J. vibroeng.

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In the fault diagnosis of the shaft orbit of rotating machinery, there are few prejudgments about the severity of the faults, which is very important for fault repair. Therefore, a fine-grained recognition method is proposed to detect different severity faults by shaft orbit. Since different shaft orbits represent different type and different severity of faults, the convolutional neural network (CNN) is applied for identifying the shaft orbits to recognize the type and severity of the fault. The recognition rate of proposed fine-grained fault identification method is 97.96 % on the simulated shaft orbit database, and it takes only 0.31 milliseconds for the recognition of single sample. Experimental result indicated that the classification performance of the proposed method are better than the traditional machine learning models. Moreover, the method is applied for the identification of the measured shaft orbits of rotor with different degree of imbalance faults, and the testing accuracy of the experiments in measured shaft orbits is 97.14 %, which has verified the effectiveness of the proposed fine-grained fault recognition method.

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Section: Identification Methods Of Fine-grained Shaft Orbits Based On mentioning

confidence: 99%

Fine-grained fault recognition method for shaft orbit of rotary machine based on convolutional neural network

Wu¹,

Feng²,

Sun³

et al. 2019

J. vibroeng.

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show abstract

“…Recently, methods that incorporate the process of feature extraction from images into the ML pipeline, have been developed to predict surface roughness characteristics in industrial [36] and infrastructure engineering [37]. These models are based on recent developments in Deep Learning that led to a new class of neural networks known as convolutional neural networks [38], which allow to analyze high dimensional sensor and further process-related data more efficiently.…”

Section: B Surface Roughness Measurementmentioning

confidence: 99%

Improving Build Quality in Laser Powder Bed Fusion Using High Dynamic Range Imaging and Model-Based Reinforcement Learning

et al. 2021

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In laser-based additive manufacturing (AM) of metal parts from powder bed, information about actual part quality obtained during build is essential for cost-efficient production and high product quality. Reliable and effective monitoring strategies for laser powder bed fusion (LPBF) therefore remain in high demand and are the subject of current research. To address this demand, a novel analysis approach using high dynamic range (HDR) optical imaging in combination with convolutional neural networks (CNN) is proposed for spatially resolved and layer-wise prediction of the surface roughness of LPBF parts. In a further step, the predicted surface roughness maps are used as a feedback signal for a reinforcement learning technique that employs a dynamics model to subsequently identify optimal process parameters under varying and uncertain conditions. The proposed approach ultimately combines the estimation of the local surface roughness based on image texture and model-based reinforcement learning to an in-situ optimization framework for LPBF processes. In addition, the relationship between the layer surface roughness of the part and the overall part density is discussed on the basis of experimental data, which also indicate the applicability of the proposed method in industrial environments. This preliminary study is a first step towards highly adaptive and intelligent machines in the field of automated laser powder bed fusion with the primary goals of reducing production costs and improving the environmental fingerprint as well as print quality.

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“…Roughness measuring methods include laser reflectivity, contact stylus tracing, tactile profile measurement, focus variation, fringe projection technique and confocal laser scanning microscope [31,32]. Sun et al presented a novel method based on convolutional neural networks (CNN) for making intelligent surface roughness identifications and achieved high-precision surface roughness estimation [33]. Patel et al introduced a SRAS system capable of detecting surface ultrasound waves on the rough-surface of an as-deposited SLM sample [34].…”

Section: Morphology Observation By 3d Optical Profilermentioning

confidence: 99%

Investigation of Laser Polishing of Four Selective Laser Melting Alloy Samples

Zhang

et al. 2020

Applied Sciences

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Selective laser melting (SLM) is a layer by layer process of melting and solidifying of metal powders. The surface quality of the previous layer directly affects the uniformity of the next layer. If the surface roughness value of the previous layer is large, there is the possibility of not being able to complete the layering process such that the entire process has to be abandoned. At least, it may result in long term durability problem and the inhomogeneity, may even make the processed structure not be able to be predicted. In the present study, the ability of a fiber laser to in-situ polish the rough surfaces of four typical additive-manufactured alloys, namely, Ti6Al4V, AlSi10Mg, 316L and IN718 was demonstrated. The results revealed that the surface roughness of the as-received alloys could be reduced to about 3 μm through the application of the laser-polishing process, and the initial surfaces had roughness values of 8.80–16.64 μm. Meanwhile, for a given energy density, a higher laser power produced a laser-polishing effect that was often more obvious, with the surface roughness decreasing with an increase in the laser power. Further, the polishing strategy will be optimized by simulation in our following study.

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Noncontact Surface Roughness Estimation Using 2D Complex Wavelet Enhanced ResNet for Intelligent Evaluation of Milled Metal Surface Quality

Cited by 36 publications

References 40 publications

Fine-grained fault recognition method for shaft orbit of rotary machine based on convolutional neural network

Fine-grained fault recognition method for shaft orbit of rotary machine based on convolutional neural network

Improving Build Quality in Laser Powder Bed Fusion Using High Dynamic Range Imaging and Model-Based Reinforcement Learning

Investigation of Laser Polishing of Four Selective Laser Melting Alloy Samples

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