An improved 3D imaging system for dimensional quality inspection of rolled products in the metal industry

Molleda, Julio; Usamentiaga, Rubén; García, Daniel; Bulnes, Francisco G.; Espina, Adrián; Dieye, Bassiru; Smith, Lyndon

doi:10.1016/j.compind.2013.05.002

Cited by 47 publications

(20 citation statements)

References 49 publications

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“…In the first case, the optical hardware is more expensive, since two or three laser stripes must be generated. This research group developed a new flatness sensor, where the effects of the vibrations introduced in the surface reconstruction of the rolled product are removed by software [64]. In this system, a 2,560 × 2,048 pixel-resolution, CCD-matrix camera is used, and two approaches are proposed for detecting and extracting the line pattern projected on the product surface.…”

Section: Flatness Sensors For the Steelmaking Industry In Spainmentioning

confidence: 99%

On-Line Flatness Measurement in the Steelmaking Industry

Molleda

Usamentiaga

García

2013

Sensors

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Shape is a key characteristic to determine the quality of outgoing flat-rolled products in the steel industry. It is greatly influenced by flatness, a feature to describe how the surface of a rolled product approaches a plane. Flatness is of the utmost importance in steelmaking, since it is used by most downstream processes and customers for the acceptance or rejection of rolled products. Flatness sensors compute flatness measurements based on comparing the length of several longitudinal fibers of the surface of the product under inspection. Two main different approaches are commonly used. On the one hand, most mechanical sensors measure the tensile stress across the width of the rolled product, while manufacturing and estimating the fiber lengths from this stress. On the other hand, optical sensors measure the length of the fibers by means of light patterns projected onto the product surface. In this paper, we review the techniques and the main sensors used in the steelmaking industry to measure and quantify flatness defects in steel plates, sheets and strips. Most of these techniques and sensors can be used in other industries involving rolling mills or continuous production lines, such as aluminum, copper and paper, to name a few. Encompassed in the special issue, State-of-the-Art Sensors Technology in Spain 2013, this paper also reviews the most important flatness sensors designed and developed for the steelmaking industry in Spain.

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Section: Flatness Sensors For the Steelmaking Industry In Spainmentioning

confidence: 99%

On-Line Flatness Measurement in the Steelmaking Industry

Molleda

Usamentiaga

García

2013

Sensors

Self Cite

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“…The main advantages of this 3D reconstruction method are its accuracy and resilience to changes in illumination [3]. For this reason, this is the most widely used method in industrial environments [4].…”

Section: Introductionmentioning

confidence: 99%

Robust registration for removing vibrations in 3D reconstruction of web material

Usamentiaga

García

2015

Optics and Lasers in Engineering

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“…Image information supporting production lines or solving special tasks independently and thoroughly are getting more and more involved into the industrial processes of many manufacturers. Nowadays, tasks like completeness checks (Haniff et al, 2011), inspections (Molleda et al, 2013), (pre-)selections of construction elements (Rodrigues et al, 2012) or quality tests (Gunasekaran, 1996) are already fulfilled using the potential of robust machine vision systems operating in real time. The required parameters are directly determined by the MVS and then transferred for further processing or evaluation.…”

Section: Related Workmentioning

confidence: 99%

Determination of Steering Wheel Angles During Car Alignment by Image Analysis Methods

Mueller

Voegtle

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Optical systems for automatic visual inspections are of increasing importance in the field of automation in the industrial domain. A new application is the determination of steering wheel angles during wheel track setting of the final inspection of car manufacturing. The camera has to be positioned outside the car to avoid interruptions of the processes and therefore, oblique images of the steering wheel must be acquired. Three different approaches of computer vision are considered in this paper, i.e. a 2D shape-based matching (by means of a plane to plane rectification of the oblique images and detection of a shape model with a particular rotation), a 3D shape-based matching approach (by means of a series of different perspectives of the spatial shape of the steering wheel derived from a CAD design model) and a point-to-point matching (by means of the extraction of significant elements (e.g. multifunctional buttons) of a steering wheel and a pairwise connection of these points to straight lines). The HALCON system (HALCON, 2016) was used for all software developments and necessary adaptions. As reference a mechanical balance with an accuracy of 0.1° was used. The quality assessment was based on two different approaches, a laboratory test and a test during production process. In the laboratory a standard deviation of ±0.035° (2D shape-based matching), ±0.12° (3D approach) and ±0.029° (point-to-point matching) could be obtained. The field test of 291 measurements (27 cars with varying poses and angles of the steering wheel) results in a detection rate of 100% and ±0.48° (2D matching) and ±0.24° (point-to-point matching). Both methods also fulfil the request of real time processing (three measurements per second).

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An improved 3D imaging system for dimensional quality inspection of rolled products in the metal industry

Cited by 47 publications

References 49 publications

On-Line Flatness Measurement in the Steelmaking Industry

On-Line Flatness Measurement in the Steelmaking Industry

Robust registration for removing vibrations in 3D reconstruction of web material

Determination of Steering Wheel Angles During Car Alignment by Image Analysis Methods

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