Email: markrainsun( at )gmail( dot )com Here are some listed. (PDF)A Brief Introduction To Fluid Mechanics, 5th Edition INSTRUCTOR SOLUTIONS MANUAL.

Steel is the material of choice for a large number and very diverse industrial applications. Surface qualities along with other properties are the most important quality parameters, particularly for flat-rolled steel products. Traditional manual surface inspection procedures are awfully inadequate to ensure guaranteed quality-free surface. To ensure stringent requirements of customers, automated vision-based steel surface inspection techniques have been found to be very effective and popular during the last two decades. Considering its importance, this paper attempts to make the first formal review of state-of-art of vision-based defect detection and classification of steel surfaces as they are produced from steel mills. It is observed that majority of research work has been undertaken for cold steel strip surfaces which is most sensitive to customers' requirements.

Work on surface defect detection of hot strips and bars/rods has also shown signs of increase during the last 10 years. The review covers overall aspects of automatic steel surface defect detection and classification systems using vision-based techniques. Attentions have also been drawn to reported success rates along with issues related to real-time operational aspects.

Steel is probably the most important of all metals in terms of its quantum and variety of use. Steel has contributed immensely towards the development of industrial society. In fact, consumption of steel is considered to be one of the yardsticks to judge the developmental status of a country. As per World Steel Association, production of crude steel during 2013 was 1,582 million tons (Mt), which is more than production figure of all other metals put together. Today, there are more than 3,500 grades of steel available out of which trade in flat steel products accounts for about 50%.An integrated iron and steel making plant produces liquid iron in blast furnace with iron ore, coke, sinter and flux as input.

Liquid iron is converted to liquid steel with specified constituent by primary and secondary steel making processes. Liquid steel is continuously cast into slabs and billets. Slabs are of rectangular cross-section with dimension of a typical slab being 1,600-mm-wide, 250-mm-thick and 12,000-mm-long. Billets are normally of square cross-section of about 150 × 150 mm and about 12,000-mm-long.

Slabs are subsequently rolled into hot strips and then to cold strips. Billets are rolled into structural of various dimensions. A simplified flow chart of steel making processes is shown in Figure. Figure 1 Steel making flow chart. Importance of surface quality of steel products, particularly that of cold-rolled steel assumed importance since 1980s primarily due to demands from automotive car makers. In course of time, hot strip surface quality, and in recent times, surface quality of structural products like rods/bars have assumed significant importance.

Traditionally, surface quality of flat steel products, which are in coil form, is judged manually by cutting about 30 m of a random coil in a batch and inspected by an expert. Typically, in manual inspection, the inspected surface is about 0.05% of the total steel surface produced. In cold rolling mill complex, operators are sometimes stationed to inspect the finished product online for any defect. However, due to high line speed, fatigue and other adverse factors, inspection process is hardly satisfactory. Thus, the manual inspection process is not sufficient to guarantee defect-free surface of steel products with reasonable degree of confidence and naturally, need for automated surface inspection grew. In a significant development [ ], nine steel companies and three aluminium companies in US started a research project in early 1980s on vision-based steel surface inspection in collaboration with two commercial organisations.

A prototype system was built and tested in several steel plants during 1987. At the same time, European companies also started working. Thus, from later half of 1980s, systematic research work on surface inspection of steel products started.

Today, vision-based automated surface inspection systems (ASIS) are produced by many reputed companies. Since 2006, an annual International Surface Inspection Summit (ISIS) is organised by a consortium of manufacturers and others. Technology of vision-based automatic inspection of steel products, even though not 100% accurate has matured. This paper attempts to find out the status of development of vision-based ASIS for steel surfaces through review of published literature during the last two and a half decades.

Complexities of steel surface inspection automation Real-time inspection of steel surfaces faces a number of challenges. The difficulties may be enumerated as follows: Hazardous site. The place for installation of inspection equipment (illumination system, camera and some signal processing equipment), particularly, for hot rolling mills is very hazardous.

Presence of high ambient temperature, dust, oil, water droplet and vapour is very common. Additionally, the illumination system and the cameras require protection against shock and vibration. Further, heavy equipment is moved in and out of site during daily, weekly and annual maintenance. All above factors necessitate the use of appropriate physical and environmental protective measures for site equipment. Operating speed.

During regular production, operating speed of the surface to be inspected is generally high. For flat steel products, speed at the end of rolling, where the inspection equipment has to operate, is typically 20 m/s. For long products, particularly wire rods, speed could be as high as 225 miles/h (100 m/s) [ ]. Real-time operation at such high speed requires special image processing equipment and software with small execution time. Varieties of surface defects in different steel products are reported to be very high [ ].

For example, Verlag Stahleisen [ ] have categorised surface defects of hot-rolled products in nine main classes and 29 subclasses. These defects are not governed by any standard. Thus, their characteristics and classification vary from mill to mill and from operator to operator. Further, manifestation of these defects changes due to variations in production process.

Large number of cameras. For flat steel products, two sets of inspection systems - one for top and another for bottom surface - are needed. Each of these sets in turn generally consists of 3 to 4 cameras to cover the entire width of the strip.

For long steel products, multiple cameras are to be located peripherally to ensure coverage of entire surface. For example, for a round product, at least three cameras are used while use of five cameras has been reported in the literature [ ]. Thus, gathering of images and their real-time processing is a daunting task. Prior literature review Over the years, a number of review papers [ – ] on various aspects of surface defect detection have been reported. Various aspects and methods for texture analysis have been reviewed in [, ]. Two comparatively recent review papers are [, ]. Advances in surface defect detection using texture analysis techniques have been dealt with by Xie [ ] covering applications in mainly textiles, tiles and wood.

Kumar [ ] has covered very comprehensively research work done in fabric surface defect detection and provided some valuable conclusions. Review papers particularly on texture defects and defects in fabrics also mention steel surface as a category where identified techniques can be applied.

It is worth mentioning that as early as in 1982, 11 papers were listed under ‘Inspection in Metal Processing Industry’ category in a review by Chin and Harlow [ ]. Gonzalez and Woods [ ] provide an excellent theoretical background to all aspects of image processing, whereas theoretical basis for neural network-based classification is adequately covered by Haykins [ ]. However, the authors could not locate any review of research work done in the field of steel surface defect detection and classification. Therefore, in this paper, attempt has been made to consolidate the published literature from academia, steel industry and manufacturers on the topic of automatic defect detection and classification of steel surfaces. Availability of research publications on automated vision-based steel surface inspection Availability of the published literature on steel surface inspection mostly consists of research work done at various academic institutions, steel plants/steel plant research units and surface inspection equipment manufacturers. A number of research works have been published jointly by academic/research institutes and steel plants indicating good collaborative partnership. During the last 10 years, a significant percentage of published work on steel surface inspection systems came from China.

This is commensurate with China's dominant presence in steel manufacturing. Some papers have been published with reported research work mainly on defect classification aspects implemented in commercially procured systems. While overall systems and their benefits are well documented by reputed manufacturers, details of defect detection and classification techniques are not elaborated, probably due to issues regarding intellectual property rights. Categories of steel surfaces. Types of steel surfaces studied for defect detection/classification are: slab, billet, plate, hot strip, cold strip, rod/bar. They cover a large proportion of applications of steel as a material.

Cold strips, and off late, rod/bars have received more attention of researchers. This is mainly explained by the fact that large proportions of these products are finished product and quality requirements of customers have become more stringent over time.Broadly, steel surfaces can be categorised in flat and long products (Figure ). Figure 3 Basic hardware structure of ASIS. 7.1 Image acquisition To obtain satisfactory surface image quality, it is important to illuminate the surface adequately and uniformly. In fact, high quality of illumination reduces computational burden of image processing. Two types of illumination techniques can be used for metallic surfaces: intensity imaging and range imaging.

[ – ] have discussed various aspects of illumination systems for metallic surfaces. Research on imaging systems for cold strips has been well documented in [ ]. Range imaging provides height information thereby making 3D defects prominent. Range imaging is not competitive to intensity imaging.

In general, use of range imaging is not common in steel surface defect studies. Intensity imaging is primarily of two types: bright field and dark field. In bright field illumination, the sensor captures most of the directly reflected light. The surface appears bright, whereas the defect features appear darker. In dark field illumination, the angle of the incident light rays to the surface normal vector is very large. This results in a dark appearance of the surface, but some defects appear bright in the image. Dark field view requires more intense lighting.

Requirement of about eight times compared to bright field lighting has been reported [ ]. Unfortunately, all surface defects do not show up either in bright field or in dark field alone. Algebra Lineal Grossman Pdf 5 Edicion. There are many examples of the use of two sets of cameras covering both the fields of view [ – ]. Use of 20 charge-coupled device (CCD) area scan cameras which are used to capture surface image of both sides of hot-rolled strips using both bright field and dark field modes have been reported in an iron and steel plant of China [ ]. However, considering maintenance issues and system complexity, most of the systems place the cameras in between the bright field and dark field locations.

7.2 Source of light. The light source is required to provide uniform ripple-free light as far as possible. While ripple-free illumination calls for special arrangement of light power supply [ ], providing uniform intensity is not possible due to the use of more than one light source in majority of the cases. Figure shows the variation of incident light intensity on to a steel surface using two xeon lights [ ]. Types of light source which are used in general are: wide spectrum tungsten, fluorescent tubes, halogen, xeon and LED. Figure 4 Illumination pattern[ ].

7.3 Type of camera In general, high-resolution CCD cameras are used. Use of both line scan and area scan cameras has been reported in the literature. Line scan cameras have been widely used as it is easier to realise a strong and even illumination to the surface area to be inspected. The disadvantage with the line scan cameras is that they do not generate a complete image at once and requires an external hardware to build up images from multiple line scans [ ]. Most of the automatic surface inspection system manufacturers use line scan camera.

For area scan cameras, the usage of transport encoder is optional and the inspection resolution in both directions is independent of the object (web) speed. However, while using area scan camera, special attention is needed to ensure even illumination of the total area under scan to the extent possible. High-resolution video cameras are also used as complimentary systems [ ]. 7.4 Camera and image resolution Camera resolution. Line scan camera resolution is generally 1,024(cross web) × 1(down web) and 2,048 × 1 pixels. Yazdchi et al. [ ] reported the use of 4,096 × 1 pixel camera.

Manufacturers normally use 1,024/2,048/4,096 × 1 pixels. For area scan: 600 × 400 pixels have been reported by [ ]. In [ ], 4,096 × 1,000 pixels have been used for slab. Image resolution.

Various dimensions of image resolutions have been reported [,,,, ]. Cross web resolutions vary from 0.17 mm to about 1 mm while reported down-web resolutions vary from 0.25 to 1.25 mm. 7.5 Image processing computer hardware Images captured by a CCD camera are transferred to some form of fast, parallel processing system dedicated to the camera and located close to it [ ].

The parallel processing system ensures real-time operation by processing bulk image data and selecting and storing regions of interest (RoIs). The parallel processing system could be a part of the camera itself, or a FPGA processor or a general purpose processor with special hardware. This part of the system is vitally important both from real-time operation as well as accuracy of defect detection and classification. Thereafter, a server with a large backup memory is used for further processing and for operator's interface. List of defect detection and classification methods.

Method Reference Type of steel surface Supervised classifier K-nearest neighbour (KNN) [ ] Cold strip [ ] Misc NN-BP [,, ] Hot strip [,,,,,,, ] Cold strip [ ] Rod/bar SVM [,,,, ] Slab, plate, billet [, ] Hot strip [,,,, ] Cold strip [,,, ], Rod/bar [ ] Misc Max-pooling convolutional NN [ ] Cold strip Discriminant function [, ] Cold strip Fuzzy logic-based classifier [,,, ] Cold strip Unsupervised classifier Self-organising map (SOM) [ ] Hot strip [, ] Cold strip Learning vector quantiser (LVQ) [ ] Hot strip [ ] Cold strip 9. Comparative evaluation of defect detection systems. In Table, some of the typical vision-based defect detection systems presented in the literature are highlighted. Attention is drawn to broad methods followed, types of defects, sample size and resolution of images used for study and reported detection accuracy. Speed of steel object and reported suitability for real-time operation are also mentioned.

In a number of studies, detection of a single defect is achieved after elimination of pseudo defects using a classifier ([,, ] etc.). These are shown here instead of under classification table (Table ). Paper Method Type of defects Sample size Features Detection accuracy (%) Resolution (across × along) Speed of steel object (m/s) Real-time operation Remark Detection Classification [ ] - slab Gabor filter, two-level thresholding, edge pair detection SVM Scratch 7,110 cases 7 histogram, gradient 94.08 Classification w.r.t.

Pseudo defect [ ] - slab Gabor filter, adaptive double-thresholding Feature-based logic Pinhole 1,764 images 4 morphological features 87.1 0.57 × 0.5 mm Classification w.r.t. Pseudo defect [ ] - billet DWT, morphological Feature difference Corner crack 1,568 regions 4 Morphological 97.6 Classification w.r.t.

Pseudo defect [ ] - billet Wavelet reconstruction, double threshold SVM Corner crack 220 images 12 Histogram, morphological 97.8 0.25 mm along web 2 Suitable Classification w.r.t. Pseudo defect [ ] - plate Gabor filter, adaptive thresholding SVM Seam crack 10,459 images 12 geometric, gray 84.83 0.5 mm Classification w.r.t.

Pseudo defect [ ] - plate UDWT (undecimated WT), morphology Crack, scratch 563 images 90.23 [ ] - hot strip Background difference, region growing Scar, scratches, pits, cracks. 8,037 defects >90 0.5 × 0.5 mm 10 Suitable Bright and dark mode cameras used [ ] - cold strip Morphological, curvelet Linear discriminant analysis 800 images 13 curvelet +7 morphological 98.5 1× 1 mm Pixel-level classification as defect [ ] - cold strip Background difference, gray-level distribution Wrinkles, inclusion, weld, holes and serrated edges 150 images 99.3 [ ] - cold strip Multivariate discriminant function Wrinkles 40 images 91 Suitable [ ] - cold strip Hough transform-hole, scratch. Double thresholding-coil break. Renyi entropy- rust Hole, scratch, Coil break and rust 93 + 157 synthesized images 78 to 90.4 [ ] - rod/bar Edge preserving filter, double threshold Crack, spot, dark line 175 defects (73 images) 95.42 18.5 Suitable [ ] - rod/bar Local annular contrast Pits, overfill, scratch 408 images 93.88 to 100 4.6 Suitable [ ] - rod/bar Sobel edge detector, snake projection, DWT T2 control chart Seam 400 subimages 7 to 9 97.5 Approximately 18 Suitable Classification w.r.t. Pseudo defect [ ] - rod/bar Gradient filter, double thresholding, SVM Vertical scratch 2,444 images 42 geometric, gray level 96.9 0.3 mm 18.5 Five cameras Classification w.r.t. Pseudo defect [ ] - rod/bar Gradient filter, region growing, SVM-RBF Seam 1,226 images Geometric, gray level 94.4 100 Suitable Classification w.r.t. Pseudo defect [ ] - rod/bar UDWT-(Haar), double threshold SVM Scratch 2,080 data 14 geometric, gray level 91.83 0.5 mm 18 Five cameras Classification w.r.t.

Camtasia Studio 8 Keygen Download here. Pseudo defect [ ] - rod/bar UDWT (Haar), DFT Periodic defects 6 coils 100 0.5 mm along web 18 Suitable Classification w.r.t. Pseudo defect [ ] - rod/bar Special horizontal, vertical, diagonal edge filters Seam, scratch, roll mark, overfill 663 images 12 geometric, gray level 85.82 t.