Weld seam profile detection and feature point extraction for multi-pass route planning based on visual attention model

He, Yinshui; Xu, Yanling; Chen, Yuxi; Chen, Huabin; Chen, Shanben

doi:10.1016/j.rcim.2015.04.005

Cited by 93 publications

(25 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There is a case in which interference data points survive after some preprocessing algorithms have been applied to images at some sampling time particularly when thresholding is carried out as the final step (the welding system refers to Refs. [21,22]). Figure 1 illustrates the acquisition process of the image with laser stripes, namely, WSPs.…”

Section: Issue Derivationmentioning

confidence: 99%

“…In our previous research, we presented a great number of methods based on visual attention mechanism to extract WSPs from strong arc background for butt and fillet joints, such as visual saliency [4,21] and visual attention models [22]. With these methods WSPs can be highlighted from the uneven background with strong arc regions.…”

Section: Introductionmentioning

confidence: 99%

“…Clustering algorithms using the designated Euclidean distance threshold are employed in Refs. [4,[21][22][23] to discern the data points belonging to the WSP in the binary image, which produces many clusters, and the length of the cluster is used to differentiate the segments that belong to the WSP from interference clusters.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Discerning Weld Seam Profiles from Strong Arc Background for the Robotic Automated Welding Process via Visual Attention Features

Zhang

et al. 2020

Chin. J. Mech. Eng.

Self Cite

View full text Add to dashboard Cite

In the robotic welding process with thick steel plates, laser vision sensors are widely used to profile the weld seam to implement automatic seam tracking. The weld seam profile extraction (WSPE) result is a crucial step for identifying the feature points of the extracted profile to guide the welding torch in real time. The visual information processing system may collapse when interference data points in the image survive during the phase of feature point identification, which results in low tracking accuracy and poor welding quality. This paper presents a visual attention featurebased method to extract the weld seam profile (WSP) from the strong arc background using clustering results. First, a binary image is obtained through the preprocessing stage. Second, all data points with a gray value 255 are clustered with the nearest neighborhood clustering algorithm. Third, a strategy is developed to discern one cluster belonging to the WSP from the appointed candidate clusters in each loop, and a scheme is proposed to extract the entire WSP using visual continuity. Compared with the previous methods the proposed method in this paper can extract more useful details of the WSP and has better stability in terms of removing the interference data. Considerable WSPE tests with butt joints and T-joints show the anti-interference ability of the proposed method, which contributes to smoothing the welding process and shows its practical value in robotic automated welding with thick steel plates.

show abstract

Section: Issue Derivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Discerning Weld Seam Profiles from Strong Arc Background for the Robotic Automated Welding Process via Visual Attention Features

Zhang

et al. 2020

Chin. J. Mech. Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nevertheless, MATLAB (Rodríguez O et al, 2012) is on the top for image processing and analysis tasks being used in to detect weld defects (Chokkalingham, et al, 2012;He, Xu, Chen and Chen, 2016;Reisgen et al, 2014;ummenhofer and Medgenberg, 2009) to study the metal transfer through droplet detection and build 3D determining concrete features of the weld profile (Sreedhar, Krishnamurthy, Balasubramanjam, Raghupathy and Ravisankar, 2012;Wang et al, 2010;Ramos et al, 2013;).…”

Section: Softwarementioning

confidence: 99%

Revisión De Técnicas De Sistemas De Visión Artificial Para La Inspección De Procesos De Soldadura Tipo Gmaw

García

Plata

Quintero

2017

RCTA

View full text Add to dashboard Cite

Resumen: El proceso de soldadura GMAW es ampliamente estudiado debido a su alta productividad y bajo costo. En este trabajo se han revisado las investigaciones orientadas a la inspección del proceso de GMAW a través de sistemas de visión artificial con el objetivo de establecer los principales elementos utilizados en estos sistemas destacando dos categorías: métodos computacionales (software y algoritmos generales), materiales y modelos matemáticos (métodos estadísticos y numéricos). Estas categorías se traslapan en el estudio y se han utilizado para evaluar el costo en términos de recursos humanos y recursos económicos. Las investigaciones revisadas se desarrollaron en la última década, con la excepción de algunas investigaciones que desempeñaron un papel principal en el desarrollo de los sistemas de inspección de los procesos GMAW. Finalmente, se han destacado los posibles campos de investigación para aquellos que intentan explorar sistemas de visión artificial para inspección de procesos GMAW.Palabras clave: GMAW, soldadura, visión artificial, inspección. Abstract:The GMAW welding process is widely studied due to its high productivity and low cost. In this paper we have reviewed the research oriented to the inspection of the GMAW process through artificial vision systems to classify them and establish the main elements used in these systems highlighting three categories: technology (hardware), computational methods (software and general algorithms) And mathematical models (statistical and numerical methods). These categories overlap in the document and have been used to assess the cost in terms of human resources and economic resources. The reviewed research was developed over the last decade, with the exception of some research that played a major role in the development of GMAW process inspection systems. Finally, potential research fields for those attempting to explore artificial visual systems for inspection of GMAW processes have been highlighted.

show abstract

“…Therefore, a variety of methods is introduced to solve this problem. Polynomial tting is a convenient and ef cient way that has the ability to handle less complicated data, and its main advantage is the reasonable exibility, which is free from the in uence of measuring areas 3) . The method of lter is another prevailing approach for roughness evaluations; it mainly consists of Gaussian and wavelet lters.…”

Section: Introductionmentioning

confidence: 99%

Surface Morphology Analysis Using Fractal Theory in Micro Electrical Discharge Machining

et al. 2017

View full text Add to dashboard Cite

Surface morphology obtained using micro electrical discharge machining (micro EDM) not only provides important information, such as wear and lubrication properties, but also shows many micro peaks and craters, which are very different from that machined using traditional methods. Hence, surface morphology evaluations in micro EDM processes using traditional evaluation parameters are not appropriate. In this study, a novel evaluation method, which includes a wavelet lter and the fractal theory, was proposed and analyzed in detail to evaluate the surface morphology in order to achieve the required precision and concision. First, a novel evaluation method was proposed, and the effectiveness of the method was investigated. Second, a series of relevant experiments based on titanium alloys and high-speed steels were conducted to validate the effectiveness of the new method, the analysis results were presented. Third, the fractal trend in the surface morphology was summarized based on the titanium alloy and high-speed steel materials; the rules governing the relationship between the fractal dimension and the surface morphology were proposed. Finally, the phenomena observed in the analysis process were investigated and discussed in detail.

show abstract

Weld seam profile detection and feature point extraction for multi-pass route planning based on visual attention model

Cited by 93 publications

References 28 publications

Discerning Weld Seam Profiles from Strong Arc Background for the Robotic Automated Welding Process via Visual Attention Features

Discerning Weld Seam Profiles from Strong Arc Background for the Robotic Automated Welding Process via Visual Attention Features

Revisión De Técnicas De Sistemas De Visión Artificial Para La Inspección De Procesos De Soldadura Tipo Gmaw

Surface Morphology Analysis Using Fractal Theory in Micro Electrical Discharge Machining

Contact Info

Product

Resources

About