Research

13 pages, 3325 KiB

Open AccessArticle

A Lightweight Neural Network Based on GAF and ECA for Bearing Fault Diagnosis

by Xiaojiao Gu, Yuntao Xie, Yang Tian and Tianshun Liu

Metals 2023, 13(4), 822; https://doi.org/10.3390/met13040822 - 21 Apr 2023

Cited by 3 | Viewed by 1108

A lightweight neural network fault diagnosis method based on Gramian angular field (GAF) feature map construction and efficient channel attention (ECA) optimization is presented herein to address the problem of the complex structure of traditional neural networks in bearing fault diagnosis. Firstly, a [...] Read more.

A lightweight neural network fault diagnosis method based on Gramian angular field (GAF) feature map construction and efficient channel attention (ECA) optimization is presented herein to address the problem of the complex structure of traditional neural networks in bearing fault diagnosis. Firstly, a GAF is used to encode vibration signals into a temporal image. Secondly, the double-layer separation residual convolution neural network (DRCNN) is used to learn advanced features of the sample. The multi-branch structure is used as the receiving domain. ECA learns the correlation between feature channels. The extracted feature channels are adaptively weighted by adding a small additional computational cost. Finally, the method is tested and evaluated using wind turbine bearing data. The experimental results show that, compared with the traditional neural network, the DRCNN model based on GAF achieves higher diagnostic accuracy with less parameter calculation. Full article

(This article belongs to the Special Issue Quality Prediction and Control Technology Design for Intelligent Manufacturing)

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16 pages, 31830 KiB

Open AccessArticle

Effect of Scanning Strategies on the Microstructure and Mechanical Properties of Ti-22Al-25Nb Alloy Fabricated through Selective Laser Melting

by Yaqun Liu, Zhongde Shan, Xujing Yang, Haowen Jiao and Weiying Huang

Metals 2023, 13(3), 634; https://doi.org/10.3390/met13030634 - 22 Mar 2023

Cited by 1 | Viewed by 1211

Abstract

In this study, Ti-22Al-25Nb intermetallic compound alloys are fabricated through selective laser melting (SLM) at four scanning speeds (600, 700, 800, and 900 mm/s). The microstructure and mechanical properties of the selective laser melting fabricated alloys are systematically evaluated. The results indicate that [...] Read more.

In this study, Ti-22Al-25Nb intermetallic compound alloys are fabricated through selective laser melting (SLM) at four scanning speeds (600, 700, 800, and 900 mm/s). The microstructure and mechanical properties of the selective laser melting fabricated alloys are systematically evaluated. The results indicate that scanning speed significantly affects microstructure characteristics (e.g., relative density, grain size, texture density, and the precipitation of secondary phases). The variation laws of the relative density, grain size, and texture density are likewise affected by scanning speed. The relative density, grain size, and texture density increase and then decrease with the increase in scanning speed. The alloy fabricated with the lowest scanning speed (600 mm/s) exhibits the maximum relative density, grain size, and texture density. By contrast, the alloy with the highest scanning speed (900 mm/s) exhibits the minimum relative density, grain size, and texture density. Furthermore, the precipitations of the O phase and Ti₃Al phase are primarily distributed in regions with a high strain concentration near the pool boundary. The alloy fabricated with a 600 mm/s scanning speed simultaneously achieves the highest strength and elongation, which is closely correlated with the uniform distribution of secondary phases. Full article

(This article belongs to the Special Issue Quality Prediction and Control Technology Design for Intelligent Manufacturing)

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10 pages, 1743 KiB

Open AccessArticle

Quantified Approach for Evaluation of Geometry Visibility of Optical-Based Process Monitoring System for Laser Powder Bed Fusion

by Song Zhang, Frank Adjei-Kyeremeh, Hui Wang, Moritz Kolter, Iris Raffeis, Johannes Henrich Schleifenbaum and Andreas Bührig-Polaczek

Metals 2023, 13(1), 13; https://doi.org/10.3390/met13010013 - 21 Dec 2022

Cited by 1 | Viewed by 916

Abstract

The long-term sustainability of the Additive Manufacturing (AM) industry not only depends on the ability to produce parts with reproducible quality and properties to a large extent but also on the standardization of the production processes. In that regard, online process monitoring and [...] Read more.

The long-term sustainability of the Additive Manufacturing (AM) industry not only depends on the ability to produce parts with reproducible quality and properties to a large extent but also on the standardization of the production processes. In that regard, online process monitoring and detection of defective parts during production become inevitable. Optical-based process monitoring techniques are popular; however, most work has been mainly focused on capturing images of print abnormalities without taking other influencing factors, such as camera and part position, chamber illumination, and print geometry on the resolution of the captured images, into account. In this work, we present a scenario to evaluate and quantify the performance of an optical-based monitoring system in a Laser Powder Bed Fusion (LPBF) machine using the F1 score, considering factors such as scan vector orientation, part geometry (size) and position in a built chamber with a fixed camera position. The quantified results confirm that the F1 score can be used as a reliable means of evaluating the performance of optical-based monitoring systems in the LPBF process for the purposes of standardization. The biggest line width of the test artifact (1000 µm) had the highest F1 score range of 0.714–0.876 compared to the smallest (200 µm) with a 0.158–0.649 F1 score. Full article

(This article belongs to the Special Issue Quality Prediction and Control Technology Design for Intelligent Manufacturing)

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22 pages, 6349 KiB

Open AccessArticle

A New Prediction Method for the Preload Drag Force of Linear Motion Rolling Bearing

by Lu Liu, Hu Chen, Zhuang Li, Wan-Ping Li, Yi Liang, Hu-Tian Feng and Chang-Guang Zhou

Metals 2022, 12(12), 2139; https://doi.org/10.3390/met12122139 - 13 Dec 2022

Viewed by 1009

Abstract

Existing studies focusing on the prediction of the preload drag force of linear motion rolling bearing (LMRB) are mainly based on mathematical modeling and vibration signal analysis. Very few studies have attempted to predict the preload drag force of LMRB on the basis [...] Read more.

Existing studies focusing on the prediction of the preload drag force of linear motion rolling bearing (LMRB) are mainly based on mathematical modeling and vibration signal analysis. Very few studies have attempted to predict the preload drag force of LMRB on the basis of the raceway morphology. A 50 km running test was performed on a LMRB to study the correlation between the preload drag force of the LMRB and the change in raceway morphology. The preload drag force variation was measured in six regions using a surface profiler on a preload drag force test bench. The variational law for raceway morphology was characterized using the surface roughness Ra, maximum peak-to-valley height Rt, fractal dimension D, and recurrence rate Rr. The correlations between these four parameters (Ra, Rt, D, and Rr) and the preload drag force were 0.645, 0.657, 0.718, and 0.722, respectively, based on the gray correlation method. Hence, Rr is recognized as the optimal characterization parameter. Through the Gaussian process regression model, a preload drag force prediction model was established. Using the recurrence rate Rr as the input parameter to develop the prediction model, the accuracies of the prediction results of the three sets are 93.75%, 98.5% and 98.8%, respectively. These results provide a new method for the monitoring and prediction of the degradation of the preload drag force of a LMRB based on rolling track topography. Full article

(This article belongs to the Special Issue Quality Prediction and Control Technology Design for Intelligent Manufacturing)

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12 pages, 35098 KiB

Open AccessFeature PaperArticle

Online Monitoring and Control of Butt-Welded Joint Penetration during GMAW

by Xingwang Xu, Yiming Wang, Jing Han, Jun Lu and Zhuang Zhao

Metals 2022, 12(12), 2009; https://doi.org/10.3390/met12122009 - 23 Nov 2022

Viewed by 1054

Abstract

Butt welding is an important link to ensure welding quality, and the penetration state of the weld is the main criterion to achieve this. Online monitoring and control of the penetration state of welded joints is an important measure to ensure welding quality. [...] Read more.

Butt welding is an important link to ensure welding quality, and the penetration state of the weld is the main criterion to achieve this. Online monitoring and control of the penetration state of welded joints is an important measure to ensure welding quality. The molten pool image is monitored by a visual sensor in the gas metal arc welding (GMAW) process, and the bottom molten pool width is predicted by the regression network model. Combined with the real-time control method, the welding current is changed to monitor and control the bottom weld width in real time. Butt-welding experiments with different groove angles verified that the proposed method could achieve satisfactory control accuracy and generalization ability. For butt-welding experiments with constant groove angles of 30° and 45°, the MAE of the controlled backside melt width to the target values was 0.2603 mm and 0.2620 mm. Therefore, it provides a feasible method for the online control of weld penetration. Full article

(This article belongs to the Special Issue Quality Prediction and Control Technology Design for Intelligent Manufacturing)

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38 pages, 11767 KiB

Open AccessArticle

Optimal Design of Three-Stress Accelerated Degradation Test Plan for Motorized Spindle with Poor Prior Information

by Hongxun Zhao, Zhaojun Yang, Chuanhai Chen, Zhifeng Liu, Wei Luo and Chunlei Hua

Metals 2022, 12(11), 1996; https://doi.org/10.3390/met12111996 - 21 Nov 2022

Viewed by 1124

Abstract

Accurate optimal design for the test plan with limited prior information is impossible since the optimal design method of a three-stress accelerated degradation test plan for a motorized spindle is based on the determination of model parameters. In order to optimize the test [...] Read more.

Accurate optimal design for the test plan with limited prior information is impossible since the optimal design method of a three-stress accelerated degradation test plan for a motorized spindle is based on the determination of model parameters. In order to optimize the test plan with poor prior information, a “dynamic” optimal design method is proposed in this article. Firstly, a three-stress accelerated degradation model with a stress coupling term is established based on the correlation of the degradation rate of the motorized spindle, and the parameters in the model are regarded as variables to represent the deviation between the prior information and the true value of the motorized spindle when the prior information is poor. Then, based on the information theory and the sequential design method, an optimal design method of the three-stress accelerated degradation test plan of the motorized spindle with the information entropy as the objective function is proposed to realize the “dynamic” optimization of the test plan. Finally, the usability of the proposed method is verified by taking a Chinese model spindle as an example, and the validity of the method is verified by checking the model accuracy of the accelerated degradation model of the motorized spindle after the test. Full article

(This article belongs to the Special Issue Quality Prediction and Control Technology Design for Intelligent Manufacturing)

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12 pages, 3756 KiB

Open AccessArticle

Thermodynamic Analysis and Experimental Study of Masked Corrosion Protection of 304 Stainless Steel Processed with Nanosecond Pulsed Laser

by Shuming Wang, Han Tong, Dong Wang and Xiaohai Li

Metals 2022, 12(5), 749; https://doi.org/10.3390/met12050749 - 27 Apr 2022

Cited by 2 | Viewed by 1402

Abstract

A three-dimensional finite element model of nanosecond pulsed laser processing is developed, given the variation of thermal physical parameters with temperature during the laser processing of metallic materials. The effect of process parameters on the temperature field is analyzed by simulating the temperature [...] Read more.

A three-dimensional finite element model of nanosecond pulsed laser processing is developed, given the variation of thermal physical parameters with temperature during the laser processing of metallic materials. The effect of process parameters on the temperature field is analyzed by simulating the temperature field of 304 stainless steel processed by nanosecond lasers. Temperature is the most sensitive to repetition frequency. The effects of power, spot diameter, scanning speed, and scan line spacing on temperature decrease successively. The quantitative analysis of the relationship between processing parameters and temperature provides a basis for the corrosion-resistant mask processing parameters on the surface of 304 stainless steel. The applicable laser processing parameters are given according to the results of the orthogonal simulation experiments; the masks and experimental studies on corrosion resistance are carried out. Experimental results show that the corrosion potential of the mask increased by a maximum of 326 mV and the corrosion current decreased by a maximum of 479 nA/cm² in the passivation electrolyte. Localized electrolysis of the material surface is carried out using the mask provided by the corrosion-resistant surface, and thus the micro-patterns of more complex shapes are processed. This study offers a new path for the micro electrolytic processing mask process. Full article

(This article belongs to the Special Issue Quality Prediction and Control Technology Design for Intelligent Manufacturing)

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16 pages, 6010 KiB

Open AccessArticle

Predictive Modeling of Thermally Assisted Machining and Simulation Based on RSM after WAAM

by Hongyu Tian, Zhenyang Lu and Shujun Chen

Metals 2022, 12(4), 691; https://doi.org/10.3390/met12040691 - 18 Apr 2022

Cited by 3 | Viewed by 1661

Abstract

The WAAM (Wire Arc Additive Manufacturing) process is well-respected because of its low cost and high deposition efficiency; nevertheless, the process has the limitations of high heat input and low forming accuracy. Hybrid manufacturing processes employing both additive and subtractive processes can effectively [...] Read more.

The WAAM (Wire Arc Additive Manufacturing) process is well-respected because of its low cost and high deposition efficiency; nevertheless, the process has the limitations of high heat input and low forming accuracy. Hybrid manufacturing processes employing both additive and subtractive processes can effectively reduce shape error. The predictive modeling of surface roughness in thermally assisted machining is described in this paper on the basis of three important parameters: feed per tooth, spindle speed, and workpiece temperature. The predictive model indicates that temperature has a very significant influence on the surface quality. An experimental study on thermally assisted machining was performed to obtain the variation law of cutting surface quality with temperature in order to determine the optimal process interval of subtractive processes. Through finite element simulation of thermally assisted machining, the influence law of external main cutting force and the internal mean stress of the cutting material were determined. Full article

(This article belongs to the Special Issue Quality Prediction and Control Technology Design for Intelligent Manufacturing)

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