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Defects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.

Combining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.

Computer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.

This paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.

Surface defects are often present on the surface of continuous casting slabs and rolled products. A lot of surface defects of hot rolled products are inherited from initial defects on continuous casting slabs. This work aims to trace the original surface defect during the whole heavy rail rolling and avoid black line surface defect that appears on the surface of heavy rail finial product.

Artificial round hole-shaped surface defects on the surface of continuous casting slab during the hot rolling of 60 kg/m heavy rail are analyzed experimentally and by means of explicit dynamic finite element method (FEM) and modified model rebuilding method.

The calculated results of surface defect locations of heavy rail finial product are in good agreement with the experimental ones. It is shown that the explicit dynamic FEM and modified model rebuilding method can be used effectively to predict the flow behavior of surface defects in the hot rolling of 60 kg/m heavy rail.

The three-dimensional finite element model for whole heavy rail rolling is built using explicit dynamic code and modified model rebuilding method. Flow behavior of black lines is studied in the 60-kg/m heavy rail rolling. The simulation results of six typical points are in good agreement with the experimental results.

The purpose of this paper is to develop a robot vision system for surface defect detection of 3D objects. It aims at the ill‐defined qualitative items such as stains and scratches.

A robot vision system for surface defect detection may counter: high surface reflection at some viewing angles; and no reference markers in any sensed images for matching. A filtering process is used to separate the illumination and reflection components of an image. An automatic marker‐selection process and a template‐matching method are then proposed for image registration and anomaly detection in reflection‐free images.

Tests were performed on a variety of hand‐held electronic devices such as cellular phones. Experimental results show that the proposed system can reliably avoid reflection surfaces and effectively identify small local defects on the surfaces in different viewing angles.

The results have practical implications for industrial objects with arbitrary surfaces.

Traditional visual inspection systems mainly work for two‐dimensional planar surfaces such as printed circuit boards and wafers. The proposed system can find the viewing angles with minimum surface reflection and detect small local defects under image misalignment for three‐dimensional objects.

Effective rail surface defects detection method is the basic guarantee to manufacture high-quality rail. However, the existed visual inspection methods have disadvantages such as poor ability to locate the rail surface region and high sensitivity to uneven reflection. This study aims to propose a bionic rail surface defect detection method to obtain the high detection accuracy of rail surface defects under uneven reflection environments.

Through this bionic rail surface defect detection algorithm, the positioning and correction of the rail surface region can be computed from maximum run-length smearing (MRLS) and background difference. A saliency image can be generated to simulate the human visual system through some features including local grayscale, local contrast and edge corner effect. Finally, the meanshift algorithm and adaptive threshold are developed to cluster and segment the saliency image.

On the constructed rail defect data set, the bionic rail surface defect detection algorithm shows good recognition ability on the surface defects of the rail. Pixel- and defect-level index in the experimental results demonstrate that the detection algorithm is better than three advanced rail defect detection algorithms and five saliency models.

The bionic rail surface defect detection algorithm in the production process is proposed. Particularly, a method based on MRLS is introduced to extract the rail surface region and a multifeature saliency fusion model is presented to identify rail surface defects.

The purpose of this paper is to determine the ultra-high cycle fatigue behavior and ultra-slow crack propagation behavior of selective laser melting (SLM) AlSi7Mg alloy under as-built conditions.

Constant amplitude and two-step variable amplitude fatigue tests were carried out using ultrasonic fatigue equipment. The fracture surface of the failure specimen was quantitatively analyzed by scanning electron microscope (SEM).

The results show that the competition of surface and interior crack initiation modes leads to a duplex S–N curve. Both manufacturing defects (such as the lack of fusion) and inclusions can act as initially fatal fatigue microcracks, and the fatigue sensitivity level decreases with the location, size and type of the maximum defects.

The research results play a certain role in understanding the ultra-high cycle fatigue behavior of additive manufacturing aluminum alloys. It can provide reference for improving the process parameters of SLM technology.

Combined defects in ball bearings may be caused during the use or manufacturing process, which can significantly affect their vibration characteristics. The previous defect models in the literature can only describe single defects such as the surface waviness and localized defect. This paper aims to propose an in-depth understanding of radial vibrations of a ball bearing with the combined defect.

A dynamic model for a ball bearing with the combined defect including the surface waviness and localized defect on its races is proposed. The effects of the combined defect sizes on the radial bearing vibrations are investigated. The results from the proposed model considering the combined defect are compared with the available results from the previous methods considering the single defects.

The acceleration amplitude is significantly affected by the surface waviness, localized defect and the combined defect on its races. The effect of the combined defect on the acceleration amplitude is larger than that of the single defect. The amplitude and peak frequency of the spectrum of acceleration for the combined defect increases with the defect sizes. The RMS value of the accelerations for the combined defect increases with the combined defect sizes.

Consequently, the proposed model can predict more accurate and in-depth understanding of the radial vibrations caused by the combined defect in the ball bearing.

AN exceedingly careful control of the surface of aero‐engine parts has beyond doubt become an excellent habit both with manufacturers and those who are to use the engine. A crack on a new part, or one which will cause a fatigue failure in work—these are the defects looked for by inspectors during manufacture, overhaul, or repairs. Cracks are very frequent causes of accidents and this fear often underlies the rejection of parts which are only suspect but which might work quite well until normal wear and tear would cause them to exceed permissible tolerances. In many cases, electro‐magnetic examination or etching reveal defects on the surface of engine parts which cannot be defined: in such cases, for the sake of certainty the part is rejected on the ground that it is cracked or made from faulty material.

Commercial solid freeform fabrication (SFF) systems, which have been developed for fabrication of wax and polymer parts for form and fit and secondary applications, such as moulds for casting, etc., require further improvements for use in direct processing of structural ceramic and metal parts. Defects, both surface as well as internal, are undesirable in SFF processed ceramic and metal parts for structural and functional applications. Process improvements are needed before any SFF technique can successfully be commercialized for structural ceramic and metal processing. Describes process improvements made in new SFF techniques, called fused deposition of ceramics (FDC) and metals (FDMet), for fabrication of structural and functional ceramic and metal parts. They are based on an existing SFF technique, fused deposition modelling (FDM) and use commercial FDM systems. The current state of SFF technology and commercial FDM systems results in parts with several surface and internal defects which, if not eliminated, severely limit the structural properties of ceramic and metal parts thus produced. Describes systematically, in detail, the nature of these defects and their origins. Discusses several novel strategies for elimination of most of these defects. Shows how some of these strategies have successfully been implemented to result in ceramic parts with structural properties comparable to those obtained in conventionally processed ceramics.

Streak defect and dynamic harmonic excitation (DHE) loading play important roles in machine operating conditions. The purpose of this paper was to assess the effects of streak defect and DHE loading on the tribological properties of surface-contact friction pairs, for example the differential gear end-face on the washer, via experimental investigation.

Streak defect was artificially introduced into the washer surface, which was loaded with DHE loads produced by a spring-connecting weight system. The wear scar of the washers and the monitored friction force signals were respectively scanned using scanning electron microscope (SEM) and analyzed using wavelet simulation.

The friction force curves, SEM images and discrete wavelet transform results indicate that DHE loading tends to increase friction force, to accelerate plowing damages and result in side-flow of material and plastic deformation on the surfaces of the washer. Whereas, streak oil-channel textures on washer specimen can be machined to modify the lubrication condition in the running-in stage so as to improve the tribological properties of the sliding pairs which were even subjected to DHE loading.

On the basis of this thesis research, the effect of streak defect and DHE loading on tribological performance of surface-contact sliding pairs is discussed. The results of wear form and friction state with the effect of streak defect and DHE loading facilitate to optimize the operating condition of mechanical parts.

The traditional methods have difficulty to inspection various types of copper strips defects as inclusions, pits and delamination defects under uneven illumination. Therefore, this paper aims to propose an optimal real Gabor filter model for inspection; however, improper selection of Gabor parameters will cause the boundary between the defect and the background image to be not very clear. This will make the defect and the background cannot be completely separated.

The authors proposed an optimal Real Gabor filter model for inspection of copper surface defects under uneven illumination. This proposed method only requires a single filter by calculating the specific convolution energy of the Gabor filter with the image. The Real Gabor filter’s parameter is optimized by particle swarm optimization (PSO), which objective fitness function is maximization of the Gabor filter’s energy average divided by the energy standard deviation, the objective makes a distinction between the defect and normal area.

The authors have verified the effect with different iterations of parameter optimization using PSO, the effects with different control constant of energy and neighborhood window size of real Gabor filter, the experimental results on a number of metal surface have shown the proposed method achieved a well performance in defect recognition of metal surface.

The authors propose a defect detection method based on particle swarm optimization for single Gabor filter parameters optimization. This proposed method only requires a single filter and finds the best parameters of the Gabor filter. By calculating the specific convolution energy of the Gabor filter and the image, to obtain the best Gabor filter parameters and to highlight the defects, the particle swarm optimization algorithm’s fitness objective function is maximize the Gabor filter's average energy divided by the energy standard deviation.