In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning

Pandiyan, Vigneashwara; Murugan, Pushparaja; Tjahjowidodo, Tegoeh; Caesarendra, Wahyu; Manyar, Omey M.; Then, David

doi:10.1016/j.rcim.2019.01.006

Cited by 72 publications

(26 citation statements)

References 26 publications

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“…An automatic robotic grinding system based on reverse engineering has been realized [ 11 ]); however, its trajectory is based only on the surface shape of the product and the burr on the object is not considered. Pandiyan [ 12 ] proposed a method based on deep learning techniques for the detection of the weld seam and its removal; however, this method has a high rate of misclassification between the weld seam states and the background.…”

Section: Introductionmentioning

confidence: 99%

A Robotic grinding station based on an industrial manipulator and vision system

2021

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Due to ever increasing precision and automation demands in robotic grinding, the automatic and robust robotic grinding workstation has become a research hot-spot. This work proposes a grinding workstation constituting of machine vision and an industrial manipulator to solve the difficulty of positioning rough metal cast objects and automatic grinding. Faced with the complex characteristics of industrial environment, such as weak contrast, light nonuniformity and scarcity, a coarse-to-fine two-step localization strategy was used for obtaining the object position. The deep neural network and template matching method were employed for determining the object position precisely in the presence of ambient light. Subsequently, edge extraction and contour fitting techniques were used to measure the position of the contour of the object and to locate the main burr on its surface after eliminating the influence of burr. The grid method was employed for detecting the main burrs, and the offline grinding trajectory of the industrial manipulator was planned with the guidance of the coordinate transformation method. The system greatly improves the automaticity through the entire process of loading, grinding and unloading. It can determine the object position and target the robotic grinding trajectory by the shape of the burr on the surface of an object. The measurements indicate that this system can work stably and efficiently, and the experimental results demonstrate the high accuracy and high efficiency of the proposed method. Meanwhile, it could well overcome the influence of the materials of grinding work pieces, scratch and rust.

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Section: Introductionmentioning

confidence: 99%

A Robotic grinding station based on an industrial manipulator and vision system

2021

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“…Contact conditions and surface roughness predictions are proposed in [26,27]. Visual-based inspection after grinding is presented using a deep learning method in [28]. Similar to the above research status of robotic grinding, as far as the author knows, most of the monitoring methods are for surface grinding.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the special characteristics of the welding seam, the traditional monitoring method based on mathematical models is not applicable, so only statistical learning methods can be used. Although the method of deep learning has been applied to weld seam inspection [28], for the research in this paper, sample acquisition is difficult and laborious. This is because each sample acquisition requires robotic scanning of welds, weld analysis, robot grinding, and evaluation of grinding results.…”

Section: Introductionmentioning

confidence: 99%

System Design and Monitoring Method of Robot Grinding for Friction Stir Weld Seam

et al. 2020

Applied Sciences

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In the grinding process of friction stir weld seams, excessive grinding will cause damage to the base metal and bring significant economic losses. In this paper, the authors design a robotic system for grinding the weld seam and present a monitoring method of excessive grinding. The designed system consists of an industrial robot, a line scanner for measuring the weld seam and a force-controlled grinding tool. Since the result of the measurement of the weld seam is a point cloud, the extraction method of the weld seam point cloud based on graph-cut is proposed in this paper. The extracted features are used as prior knowledge of the monitoring algorithm. On the other hand, by combining the features from the point cloud and force-position information during the processing, a monitoring method for excessive grinding based on PSO-SVM is proposed and verified by experiments. The experiments demonstrate that the proposed method can identify excessive grinding, and the accuracy of recognition is 91.5%.

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“…Experiments showed that the method can be used as a precision processing method for the blade disc. Pandiyan [8] developed a machine learning belt grinding system based on deep learning, which can monitor and predict the new height distribution of welding seams and the endpoint of belt grinding in extra real-time height. Wang and Yan et al [9,10] studied robot belt grinding technology from two aspects-the robot belt grinding model and surface quality-which laid a theoretical foundation for robot belt grinding technology.…”

Section: Introductionmentioning

confidence: 99%

Bionic Structure on Complex Surface with Belt Grinding for Electron Beam Welding Seam of Titanium Alloy

Xiao¹,

Zhang²,

He³

et al. 2020

Applied Sciences

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Electron beam welding (EBW) is widely used to weld titanium alloy parts such as aero-engine casing and blades. The surface quality after EBW has a significant influence on the aero-engine performance of those parts. We propose a surface treatment method with grinding on a titanium alloy electron beam weld. We analyze the influence of grinding parameters on the characteristics of the grinding surface. The experiment shows the applicability of ground surface by belt grinding on EBW and its impact on aero-engine performance. After belt grinding, both the welded surface and the surface connected with the substrate are smooth. The extra height of the seam was less than 0.2 mm, and the surface roughness (Ra) of the weld after grinding can be less than 0.98 μm. The microstructure of the weld after grinding was analyzed. Two types of bionic shapes were obtained, a sawtooth shape with a width of 40 μm and a height of 10 μm and a wavy shape with a width of 20 μm and a height of 3 μm. From the analysis above, the bionic surface can be obtained by grinding on the weld with an abrasive belt.

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In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning

Cited by 72 publications

References 26 publications

A Robotic grinding station based on an industrial manipulator and vision system

A Robotic grinding station based on an industrial manipulator and vision system

System Design and Monitoring Method of Robot Grinding for Friction Stir Weld Seam

Bionic Structure on Complex Surface with Belt Grinding for Electron Beam Welding Seam of Titanium Alloy

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