A new method for online monitoring when grinding Ti-6Al-4V alloy

Nguyen, Duc-Toan; Yin, Shu; Nguyen, Tan N.; Son, Phung Xuan; Duc, Le Anh

doi:10.1080/10426914.2018.1532587

Cited by 17 publications

(6 citation statements)

References 44 publications

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“…Li 等 [118] 根据砂轮轮廓的特点, 构建了基于轮廓搜索算法的轮廓模板匹配方法, 获得完整的砂轮轮廓并进行测量. Zhao 等 [119][120][121] (1) 气压/液压测量法气压测量法原理如图 9(a)所示 [122] , 工作时恒定气压的压缩空气由喷嘴吹向砂轮表面, 当砂轮磨损时砂轮表面与喷嘴之间的气隙将发生变化, 通过测试压缩空气头的静态特性来标定压力与气隙的关系.…”

Section: F O R R E V I E W O N L Yunclassified

“…为适应有磨削液存在的加工环境, 可以采用液压测量法, 通过液压信号对砂轮磨损量进行检测. Duytrinh [122] 利用气压测量法对砂轮磨损量进行了测量. Thang 等 [123] 通过测量探头与砂轮工作面间隙压缩空气的压力进而确定砂轮的径向磨损量.…”

Section: 通过测量气压大小进而得到砂轮的磨损量也可采用恒压力型热线管(风速测定管)测量砂轮表面气流unclassified

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Research on grinding wheel wear measurement methods: Current status and future perspectives

LI,

BAO,

WANG

et al. 2024

Sci. Sin.-Tech.

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Section: F O R R E V I E W O N L Yunclassified

Section: 通过测量气压大小进而得到砂轮的磨损量也可采用恒压力型热线管(风速测定管)测量砂轮表面气流unclassified

Research on grinding wheel wear measurement methods: Current status and future perspectives

LI,

BAO,

WANG

et al. 2024

Sci. Sin.-Tech.

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“…Furthermore, a Gaussian regression function had a difficulty in explaining the machining mechanisms. To solve these most difficult problems, DuyTrinh et al 100 proposed an online system to monitor the wheel wear in process and predict the surface roughness. Their experiments proved that this approach could accurately forecast the surface roughness by measuring the wheel wear.…”

Section: Prediction Of Surface Roughnessmentioning

confidence: 99%

Surface roughness of machined wood and advanced engineering materials and its prediction: A review

Zhong

2021

Advances in Mechanical Engineering

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This article discusses the surface roughness of wood and advanced engineering materials after machining and its prediction. The topics are surface roughness of precision-machined advanced engineering materials, machining of WC and Inconel, rapidly solidified Al alloys, surface roughness of wood materials, and prediction of surface roughness. Findings include that ductile streaks on silicon and glass surfaces ground or lapped with inexpensive machines largely reduced the polishing time to obtain the required surface roughness. Abrasive jet machining could remove the patterns from recycled wafers and improve the surface roughness. The roughness of WC-Co coatings was significantly improved by using the method of fast regime fluidized bed machining. As beryllium is a toxic element, the rapidly solidified Al alloy may be a good insert material to replace BeCu. Higher bonding strengths resulted from rougher surfaces of wood samples. Wood samples had reduced bonding strengths after soaking in water. Optimum artificial neural networks (ANNs) with necessary inputs could accurately predict the roughness values. ANNs trained using particle swarm optimization and genetic algorithms could predict surface roughness better than typical ANNs. Minimum quantity lubrication is a hot research topic to minimize the amount of the fluid for cost and environmental considerations.

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“…probe wear, low measurement efficiency, and material surface damage). Common non-contact surface roughness measurements mainly involve laser reflectance method [22], non-contact laser probe method, scanning electron microscopy method [23], compressed air method [24], and laser scanning confocal method [25]. However, those approaches generally require complicated measuring equipment or mechanisms, which limits their popularization in industrial applications.…”

Section: Introductionmentioning

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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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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A new method for online monitoring when grinding Ti-6Al-4V alloy

Cited by 17 publications

References 44 publications

Research on grinding wheel wear measurement methods: Current status and future perspectives

Research on grinding wheel wear measurement methods: Current status and future perspectives

Surface roughness of machined wood and advanced engineering materials and its prediction: A review

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

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