Study on the surface integrity in ultrasonic vibration and magnetic field complex assisted low speed WEDM under different magnetic field coupling

Wang, Yan; Miao, Jiantao; Chen, Siyu; Jin, Ruiqi; Xiong, Wei; Chen, Yizhang

doi:10.1016/j.jmapro.2023.01.083

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…The complex interaction of these factors will not only reduce the stability of the surface formation process but also further constrain the improvement of the surface quality of the workpiece. Relevant literature [9][10][11][12] also released that the surface will directly influence the performance of the workpiece (e.g. wear resistance, fit quality, fatigue strength, corrosion resistance, contact stiffness).…”

Section: Introductionmentioning

confidence: 99%

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Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Man,

Zhou,

Sun

et al. 2024

Surf. Topogr.: Metrol. Prop.

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Surface roughness is of great significance in maintaining mechanical performance and improving the reliability of the equipment. However, fast surface roughness evaluations that are sufficiently stable and efficient for engineering in-situ use have not yet been realized. To address this issue, an image-driven roughness intelligent method is proposed in this research. By evaluating the texture similarity intelligently between the testing image and the reference image, the surface roughness of the testing image can be acquired. Firstly, with a proposed adaptive texture extraction method, the texture feature of an image can be extracted even under a complex background. Secondly, by establishing the graph structure of the texture grayscale features, the similarity between different images is evaluated. Finally, by establishing a sparrow-optimized support vector machine regression method, the mapping relationship between the similarity and the surface roughness can be acquired. The experimental results indicate that the proposed method for intelligent evaluation of roughness has superior prediction performance (the average relative prediction error of Ra and Rz are 8.8156% and 8.0571%, respectively). Therefore, this work provides a useful tool for non-contact detection of workpiece surface roughness.

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Section: Introductionmentioning

confidence: 99%

“…Parameter determinationIn order to realize the adaptive extraction of the optimal sub-bands, three variables in equation(9) are important for the extraction of the feature extraction. Among them, maximizing the correlation coefficient R ensures that the sub-bands are as similar as possible to the original image (with a range of [0,1]).…”

mentioning

confidence: 99%

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Man,

Zhou,

Sun

et al. 2024

Surf. Topogr.: Metrol. Prop.

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show abstract

“…Employing a teaching-learning-based optimization (TLBO) algorithm integrated with response surface methodology (RSM), they achieved optimal machining conditions characterized by low roughness values and high removal rates. In addressing the inefficiencies and surface integrity challenges inherent in traditional WEDM for aerospace delicate structures crafted from superalloy, Wang et al [17] proposed an innovative approach integrating ultrasonic-assisted vibration (USV) and magnetic field (MF) assistance into the WEDM process. Through meticulous analysis of processing parameters, including pulse discharge parameters and magnetic field strength, their study demonstrated significant improvements in the removal rate of nickel-based shape memory alloys while concurrently enhancing the surface integrity of the workpiece.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigations on WEDM performance characteristics of FGH99 powder metallurgy superalloy

Qian,

Zhao,

et al. 2024

Preprint

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In response to challenges posed by conventional machining methods, an exploratory experimental investigation was conducted to assess the effectiveness of wire electric discharge machining (WEDM) in fabricating turbine disc slots from powder metallurgy superalloy FGH99. The present study conducted a single-factor experiment on WEDM using FGH99 alloys, with the aim of investigating the impact of different WEDM parameters on the material removal rate, surface roughness, and surface morphology. Additionally, the analysis encompassed the characterization of both the heat-affected zone and plastic deformation zone on the workpiece surface. Results show that there is a positive correlation between surface roughness and variables such as pulse width, pulse gap, servo voltage, and wire tension. Conversely, wire speed exhibits an inverse relationship with surface roughness. Notably, the impact of pulse width on surface roughness surpasses that of other parameters. Metallographic examination reveals that under the selected process parameters, while ensuring the efficiency of material removal, superior machining quality was achieved. The absence of conspicuous recast layers on the workpiece surface, with an average thickness of the plastic deformation layer measured at 3.97 µm, indicating favorable processing outcomes. The aforementioned findings provide valuable experimental insights into the WEDM process of FGH99 alloys and offer foundational engineering data for the fabrication of turbine disc slots made from this material.

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Performance Evaluation with Metallurgical Aspects on Groove Formation during Magnetic Field-Assisted Electrical Discharge Turning of EN24 Steel Alloy

Varshney,

Singh

2024

J. of Materi Eng and Perform

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Study on the surface integrity in ultrasonic vibration and magnetic field complex assisted low speed WEDM under different magnetic field coupling

Cited by 8 publications

References 16 publications

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Experimental investigations on WEDM performance characteristics of FGH99 powder metallurgy superalloy

Performance Evaluation with Metallurgical Aspects on Groove Formation during Magnetic Field-Assisted Electrical Discharge Turning of EN24 Steel Alloy

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