SUSAN 3D operator, principal saliency degrees and directions extraction and a brief study on the robustness to noise

Walter, Nicolas; Aubreton, Olivier; Fougerolle, Yohan; Laligant, Olivier

doi:10.1109/icip.2009.5414078

Cited by 10 publications

(7 citation statements)

References 5 publications

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“…The inclination of the members is detected precisely and locally along the full contour of the structure, while other methods may require introducing a series of auxiliary constraints to efficiently detect not self-supported edges and singular parts (Guo et al 2014). Its extension to 3D problems is also straightforward, as it can be checked in the work by Walter et al (2009aWalter et al ( , 2009b.…”

Section: Introductionmentioning

confidence: 99%

A new overhang constraint for topology optimization of self-supporting structures in additive manufacturing

Garaigordobil

Ansola

Santamaría

et al. 2018

Struct Multidisc Optim

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This work falls within the scope of computer-aided optimal design, and aims to integrate the topology optimization procedures and recent additive manufacturing technologies (AM). The elimination of scaffold supports at the topology optimization stage has been recognized and pursued by many authors recently. The present paper focuses on implementing a novel and specific overhang constraint that is introduced inside the topology optimization problem formulation along with the regular volume constraint. The proposed procedure joins the design and manufacturing processes into a integrated workflow where any component can directly be manufactured with no requirement of any sacrificial support material right after the topology optimization process. The overhang constraint presented in this work is defined by the maximum allowable inclination angle, where the inclination of any member is computed by the Smallest Univalue Segment Assimilating Nucleus (SUSAN), an edge detection algorithm developed in the field of image analysis and processing. Numerical results on some benchmark examples, along with the numerical performances of the proposed method, are introduced to demonstrate the capacities of the presented approach.

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Section: Introductionmentioning

confidence: 99%

A new overhang constraint for topology optimization of self-supporting structures in additive manufacturing

Garaigordobil

Ansola

Santamaría

et al. 2018

Struct Multidisc Optim

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“…The cutting speeds and the tool materials used in each experiment are shown in Table 4. It is important to say that the first two speeds used (350 and 420 m/min) are close to the speeds recommended for the milling with carbide tools and the third and fourth speeds (680 and 850 m/min) are recommended for the milling with ceramic (Si 3 N 4 ) tools (Walter, 2009). Each experiment was carried out three times.…”

Section: Materials Equipments and Experimental Proceduresmentioning

confidence: 94%

Performance of carbide and ceramic tools in the milling of compact graphite iron - CGI

Gabaldo¹,

Diniz²,

Andrade³

et al. 2010

J. Braz. Soc. Mech. Sci. & Eng.

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Grey cast iron (GCI) is the most common material used in diesel engineCompact graphite iron is a material with intermediary properties between grey cast iron (GCI) and ductile cast iron (Sahm et al., 2002). Like in the GCI, the graphite particles in the CGI are flat, long, randomly oriented and interconnected. In the ductile iron they are in form of nodules (Warrick et al., 1999). However, as it can be seen in Fig. 1(b), the "worms" (as the graphite particles in the CGI are called) have something in common with the nodules of the ductile iron ( Fig. 1(a)), since both are much smaller than the graphite lamellas of the GCI (Fig. 1(c)). On the other hand, the morphology of CGI graphites is compact and with rounded tips, what makes the nucleation and growth of cracks more difficult than in the GCI. The sharp and lamellar graphites of the GCI with smooth surfaces (Fig. 1(c)) make this material more fragile than the other two (Löhe, 2005).The CGI alloys have good properties of mechanical strength, ductility, toughness, thermal shocks, damping and heat conductivity (Mocellin et al., 2004). These properties are better for the CGI part functioning, but make its machinability worse than the machinability of GCI.Compared with GCI, the CGI presents some advantages like reduction of the wall thickness of the parts for a same load, reduction of the safety factor due to a smaller variation of the cast properties, reduction of the fragile fractures during manufacturing, assembly and service due to its higher ductility and strength (Dawson, 1999).Compared with ductile cast iron, it can be said that with CGI it is easier to produce complex cast parts and the residual stresses are smaller due to the higher heat conductivity (what also helps to increase cutting tool life). Moreover, it presents smaller Young modulus and better machinability (Guesser, 2004). Milling of CGI engine blocksBesides the worm graphite particles, a certain percentage of spheroidal graphite nodules is also present in the CGI. As the nodularity increases, the material strenght also increases making the forces needed to cut the material higher. Moreover, with the nodularity growth, the heat conductivity decreases, decreasing machinabilty even more (Dawson, 2002).A factor that makes the engine blocks even more difficult to be machined is the great number of interruptions on the surface. Special care has to be taken in the mill cutter choice, because very positive tools reduce cutting force, but may generate tool life relatively lower when compared to the cutters with negative geometry, due to their small impact resistance.As the tool wears, workpiece surface roughness and flatness error increase. Besides tool wear, other important factors to obtain J. of the Braz. Soc. of Mech. Sci. & Eng.

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“…The edge and/or corner based algorithms, especially the Harris 3D and SUSAN [10] detectors, had the best results. Fig.…”

Section: Algorithms and Methodsmentioning

confidence: 99%

Preceding Vehicle Tracking in Stereo Images via 3D Feature Matching

Weingerl¹,

Kubinger²,

Engelhardt‐Nowitzki³

2016

DAAAM Proceedings

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The reliable detection of the preceding vehicle position is a very important component of most semi-autonomous cars. However, most vehicle detection systems rely on expensive sensors or need a big amount of training images. In this work a vehicle tracking system, which is using images from a stereo camera system, was developed. In the first part a state of the art stereo matching algorithm was chosen based on the operating environment of the vehicle. The depth information from the stereo algorithm is then transformed into a point cloud. In the second part of this work an object detection module, which detects the preceding vehicle in the point cloud, was developed. The algorithm is based on 3D feature detection and matching. The 3D pose of the tracked vehicle is determined directly via the position of the detected vehicle in the point cloud. The module was tested with several test sequences and it has good tracking results in off-road situations with trucks as tracked vehicles.

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SUSAN 3D operator, principal saliency degrees and directions extraction and a brief study on the robustness to noise

Cited by 10 publications

References 5 publications

A new overhang constraint for topology optimization of self-supporting structures in additive manufacturing

A new overhang constraint for topology optimization of self-supporting structures in additive manufacturing

Performance of carbide and ceramic tools in the milling of compact graphite iron - CGI

Preceding Vehicle Tracking in Stereo Images via 3D Feature Matching

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