Steel Strip Surface Research Articles

Thermo-chemical Fluid Flow Simulation in Hot-Dip Galvanizing: The Evaluation of Dross Build-Up Formation

This paper presents a new computational framework to investigate the driving force for the formation of intermetallic dross particles in the zinc bath and dross build-up on the bath hardware. This is a major problem in continuous hot-dip galvanizing lines. The Computational Fluid Dynamics (CFD) model calculates the turbulent thermo-chemical flow conditions within the liquid melt. A detailed modeling of the steel strip–liquid interface enhances this approach, by means of a conjugated heat transfer calculation and a spatially resolved, temperature- and concentration-dependent iron dissolution and aluminum uptake. The heart of the computational framework is a thermodynamic model, which assesses the driving force for the formation or dissolution of dross particles in the bath and dross build-up on stationary and rotating equipment. The simulation results are validated with temperature and species depth profile measurements. The applicability of the CFD model is shown by investigating the locally resolved aluminum uptake and iron dissolution at the steel-strip surface, and the multi-physics conditions in the region near the roll. The novel approach of evaluating the thermodynamic driving force enables the assessment of the formation of dross build-up at the roll surface.

Influence of Cold Rolling Process on Mini Spangle Formation of 55%Al-Zn Alloy Coating Steel Sheet

According to that mini spangle is the most common defect affecting the appearance quality of hot-dip 55%Al-Zn alloy coated steel sheet, industrial experiments and statistical analysis were done to investigate the influence of cold rolling process on the formation of mini spangle. The results show that, with the decrease of rolling oil concentration, the increase of rolling time, and the increase of rolling pass, the probability of mini-spangle formation increases. Due to the different equipment conditions, the probability of mini-spangle formation on the upper and lower surfaces of steel strip is different. The reason of mini-spangle formation lies in the presence of carboxylates (R-COO-Fe) result from the residual emulsion on the surface of cold rolled steel strip. The carboxylates may interfere with the interfacial reaction between the steel substrate and Al-Zn bath, and result in more convex Fe5Si2Al20 phases formed on the surface of intermetallic compound layer. The Fe5Si2Al20 phases may provide more heterogeneous nucleation sites for the formation of Al-rich dendrites and lead to the formation of mini spangle.

Influence of different inlet pressures on the flow field and heat transfer characteristics of impinging jets

In this paper, the V type slit nozzle in a quick-frozen machine is used as the research object. Under the same air supply conditions, only the pressure entering the impinging jet region is changed. The impact on the flow field and heat transfer characteristics of the impinging jet is observed. The results show that with the increase of the inlet pressure, the velocity of the exit of the V-slit nozzle will change greatly. In this pressure range, the exit velocity of the nozzle is relatively uniform, and frozen products will not be blown off. Therefore, it is beneficial to frozen food safely and effectively. Along the direction of cross flow, the Nusselt number of the steel strip surface of the V slit nozzle will change when the pressure changes, and it will increase with the increase of pressure. Therefore, the heat transfer characteristics of the V type slit nozzle on the surface of the steel strip are stronger and stronger under the increase of the inlet pressure, and the uniformity is also very good.

Multi-class classification method for strip steel surface defects based on support vector machine with adjustable hyper-sphere

Focusing on strip steel surface defects classification, a novel support vector machine with adjustable hyper-sphere (AHSVM) is formulated. Meanwhile, a new multi-class classification method is proposed. Originated from support vector data description, AHSVM adopts hyper-sphere to solve classification problem. AHSVM can obey two principles: the margin maximization and inner-class dispersion minimization. Moreover, the hyper-sphere of AHSVM is adjustable, which makes the final classification hyper-sphere optimal for training dataset. On the other hand, AHSVM is combined with binary tree to solve multi-class classification for steel surface defects. A scheme of samples pruning in mapped feature space is provided, which can reduce the number of training samples under the premise of classification accuracy, resulting in the improvements of classification speed. Finally, some testing experiments are done for eight types of strip steel surface defects. Experimental results show that multi-class AHSVM classifier exhibits satisfactory results in classification accuracy and efficiency.

Effects of Different Orifice Structure on Heat Transfer Characteristics of Steel Strip Surface in an Air Impinging Freezer

Effects of Different Orifice Structure on Heat Transfer Characteristics of Steel Strip Surface in an Air Impinging Freezer

The Role of Nano-TiO2 Lubricating Fluid on the Hot Rolled Surface and Metallographic Structure of SS41 Steel

In this paper, nano-TiO2lubricating fluid was chosen as an advanced rolling lubricant to investigate its effect on the hot rolled surface and metallographic structure of SS41 steel strips. The tribological performances of nano-TiO2 lubricating fluid were measured by a four-ball tribotester. The hot rolling experiments under different lubrication conditions were carried out by a four-high rolling mill. The surface morphology, oxide scales and metallographic structure after hot rolling were observed using a confocal laser scanning microscope and scanning electron microscope (SEM), respectively. The composition of surface attachments was analyzed with X-ray photoelectron spectroscopy (XPS). The results indicate that the nano-TiO2 lubricating fluid has a better tribological performance. The surface defects on the hot rolled surface could be decreased. The phase composition of the surface still appears as a mixture of ferrite and pearlite. The surface of steel strips is not micro-alloyed with titanium as predicted. Additionally, the grain size of rolled steel strips which were lubricated with the nano-TiO2lubricating fluid decreased by nearly 50%, compared with traditional lubricating fluid. Furthermore, it was found that the thickness of the oxide layers on the surface reduced, whilst the Rockwell hardness of the oxide layers was enhanced as nano-TiO2 lubricating fluid was applied.

Bare Spot Defect on a Hot Dip Galvanized DP Sheet Strip

Bare spots defect of galvanealed (GA) in High Strength Dual Phase (DP) steel strip with 1.5 % Mn contain was studied in detail. The surface morphologies of spot defects before and after partial and complete removal of the Zn layer, as well as the interface between the outermost coating layer and the sheet substrate were analyzed by optical microscope (OM), scanning electron microscope (SEM), energy dispersive spectrometer (EDS), X-ray Photoelectron spectroscopy (XPS), glow discharge optical emission spectrometry (GDOES) and laser scanning confocal microscope(LSCM). It was found that the bare spots defect was composed of a large number of pits with different sizes and depths. There were a lot of Fe - Zn alloy particles distributed in the pits, and many MnO formed on the surface, no effective Fe2Al5 inhibition layer formed. The results showed that the main reasons for the bare spots defect of the GA in High Strength DP steel strip are as follows: a silicon oxide film forms on the substrate during annealing prior to hot dipping because of Mn gathered on the surface of steel strip, thus strip surface wettability with liquid zinc is deteriorated and preventing the formation of the Fe2Al5 inhibition layer during hot dipping. In this paper, the dew point control process is introduced creatively, by increasing the dew point and the hydrogen content in the furnace area, and the bare spot defects on the surface of the high strength duplex steel galvanized sheet are solved.

Strip Steel Surface Defects Recognition Based on SOCP Optimized Multiple Kernel RVM

Strip steel surface defect recognition is a pattern recognition problem with wide applications. Previous works on strip surface defect recognition mainly focus on feature selection and dimension reduction. There are also approaches on real-time systems that mainly exploit the autocorrection within some given picture. However, the instances cannot be used in practical applications because of a bad recognition rate and low efficiency. In this paper, we study the intelligent algorithm of strip steel surface defect recognition, where the goal is to improve the accuracy and save running time. This problem is very important in various applications, especially the process testing of steel manufacturing. We propose an approach called the second-order cone programming (SOCP) optimized multiple kernel relevance vector machine (MKRVM), which can recognize strip surface defects much better than other methods. The method includes the model parameter estimation, training, and optimization of the model based on SOCP and the classification test. We compare our approach with existing methods on strip surface defect recognition. The results demonstrate that our proposed approach can improve the recognition accuracy and reduce the time costs of the strip surface defect.

Real-time Detection of Steel Strip Surface Defects Based on Improved YOLO Detection Network

The surface defects of steel strip have diverse and complex features, and surface defects caused by different production lines tend to have different characteristics. Therefore, the detection algorithms for the surface defects of steel strip should have good generalization performance. Aiming at detecting surface defects of steel strip, we established a dataset of six types of surface defects on cold-rolled steel strip and augmented it in order to reduce over-fitting. We improved the You Only Look Once (YOLO) network and made it all convolutional. Our improved network, which consists of 27 convolution layers, provides an end-to-end solution for the surface defects detection of steel strip. We evaluated the six types of defects with our network and reached performance of 97.55% mAP and 95.86% recall rate. Besides, our network achieves 99% detection rate with speed of 83 FPS, which provides methodological support for real-time surface defects detection of steel strip. It can also predict the location and size information of defect regions, which is of great significance for evaluating the quality of an entire steel strip production line.

Comparison of Corrosion Rate on Paint Coated and Uncoated SS400 Steel

To prevent corrosion in metal, especially carbon steel, always need efforts which considered expensive one. But, compared with costs and losts when the corrosion attack is not properly managed then the cost of efforts is much lower. The most popular method in preventing corrosion attack is by coating of metal which also has decorative objective. In this research, four coating materials were applied to the surface of low carbon steel strips (SS400) and as control uncoated SS400 steel strips were also employed. The steel strips then were dipped into electrolite solution consisted of 30% of consentration of H2SO4, and NaCl soluted in river water for 15 (fifteen) days. After the presetted time was elapsed, the steel strips then examined for lost of mass. It was found that the highest lost of mass was for unpolished uncoated one dipped in H2SO4 with corrosion rate of 4,566.06 mpy. The lowest lost was for paint coated one dipped in NaCl-river water solution with corroion rate of 0.64 mpy.

Application research on semi-definite programming optimized support vector machines

ABSTRACTThe kernel function optimization is the key issues to address when using the support vector machine (SVM) algorithm. To solve the parameter selection for the SVM, a semi-definite programming optimized SVM (SDP-SVM) algorithm is proposed in this paper. The steps of the algorithm are described, and the optimization of the kernel function is shown using an SDP method. The SDP method is used to find the best parameter of SVM. The heart_scale data in the University of California Irvine database are then simulated using the SDP-SVM model. The experimental results shows that the generalization capability and the classification accuracy of the SDP-SVM algorithm have been greatly improved. A variety of strip-steel surface defect images from actual production are classified using the SDP-SVM algorithm, and the results show that the classification method of the SDP-SVM algorithm has high classification accuracy, strong practicability, and a wide variety of application prospects.

Cold Roll Bond of Al-St Strips with Wire Brush Surface Treatment

The effect of interface microstructure by wire brush treatment on the bond strength of Al-St clad strips by cold roll bonding was investigated. For the purpose, wire brush surface preparations on steel strip surface have been done before the cold roll bonding of the Al-St clad strips, and evaluate the sample bonding strength by peeling test of the rolling deformation zone. The results show that bond strength of Al-St strip by wire brush treatment increases with the total reduction increases and the threshold reduction of Al-St strip is 15%. Meanwhile the total crack area increase with the reduction. The oxide film on the surface of brittle layer hinds the metal bond and the crack between the scratch brush layer and substrate metal is the weak point and decrease the bond strength. A major feature of the bonding mechanism described in this paper is that extruded aluminum squeezed into the cracks of steel surface by normal roll pressure action, and metallic bond with underlying fresh steel metal.

Image De-noising on Strip Steel Surface Defect Using Improved Compressive Sensing Algorithm

De-noising for the strip steel surface defect image is conductive to the accurate detection of the strip steel surface defects. In order to filter the Gaussian noise and salt and pepper noise of strip steel surface defect images, an improved compressive sensing algorithm was applied to defect image de-noising in this paper. First, the improved Regularized Orthogonal Matching Pursuit algorithm was described. Then, three typical surface defects (scratch, scar, surface upwarping) images were selected as the experimental samples. Last, detailed experimental tests were carried out to the strip steel surface defect image de-noising. Through comparison and analysis of the test results, the Peak Signal to Noise Ratio value of the proposed algorithm is higher compared with other traditional de-noising algorithm, and the running time of the proposed algorithm is only26.6\% of that of traditional Orthogonal Matching Pursuit algorithms. Therefore, it has better de-noising effect and can meet the requirements of real-time image processing.

Image De-noising on Strip Steel Surface Defect Using Improved Compressive Sensing Algorithm

Image De-noising on Strip Steel Surface Defect Using Improved Compressive Sensing Algorithm

Online Surface Defects Detection System for Cold-rolled Steel Strip

Background: Online inspection technology of cold-rolled steel surface defects has become an important means to improve product quality. This paper focuses on the development of cold-rolled steel strip surface defects detection system using the line light source and line scan cameras. Keywords: Cold-rolled steel strip, surface defects, closed-loop control method, image processing, contour extraction, inspection.

Влияние геометрии полосы анизотропной электротехнической стали на ее магнитные свойства

The impact of the geometry (mainly the surface roughness) of cold rolled strips of anisotropic electrical (transformer) steel of domestic production (technical soft magnetic bcc alloy Fe - 3 wt.% Si with an edge crystallographic texture (110)[001]) on the magnetic properties (particularly on electromagnetic induction B 800 and specific magnetic losses P 1 7/ 50 ) is studied. It is shown that corrugations are formed on the surface of steel strips during cold rolling to a final thickness of 0.23 - 0.30 mm with a reduction of 50 - 70%. As a rule, their ridges and valleys are perpendicular to the direction of rolling and have a wavy form. Formation of the relief is attributed to the gradient of the stress-strain state along the cross section of the strip and to the tightness of the flow of the surface layers at the end of the deformation region and inevitably accompanies the rolling process. The presence of corrugations like any other roughness on the strip surface reduces B 800 and increases P 1, 7/ 50 . We propose a method for elimination of the surface defects (including corrugations) of cold rolled steel strips after cold rolling, i.e., ironing, which can be carried out by drawing of strips with a small draft (~3%) in cold rolling mills with a disconnected work roll drive using coilers. Introduction of drawing into the transformer steel manufacture technology improves the magnetic properties of steel due to increased perfection of the crystallites (grains), increased sharpness of the edge(110)[001] texture and free movement of the boundaries of magnetic domains upon magnetic reversal. Ironing of the steel strips of nitride-copper melt (inhibitors - dispersed particles of AlN and CuMn 2 O 4 ) with a thickness of 0.30 mm increased B 800 up to 1.93 T and decreased P 1, 7/ 50 to 1.07 W/kg (thus improving the magnetic properties by 5.7 and 10%, respectively). The resulted properties of domestic steel production are not inferior to those of foreign steel.

Adaptive classifier for steel strip surface defects

Surface defects detection system has been receiving increased attention as its precision, speed and less cost. One of the most challenges is reacting to accuracy deterioration with time as aged equipment and changed processes. These variables will make a tiny change to the real world model but a big impact on the classification result. In this paper, we propose a new adaptive classifier with a Bayes kernel (BYEC) which update the model with small sample to it adaptive for accuracy deterioration. Firstly, abundant features were introduced to cover lots of information about the defects. Secondly, we constructed a series of SVMs with the random subspace of the features. Then, a Bayes classifier was trained as an evolutionary kernel to fuse the results from base SVMs. Finally, we proposed the method to update the Bayes evolutionary kernel. The proposed algorithm is experimentally compared with different algorithms, experimental results demonstrate that the proposed method can be updated with small sample and fit the changed model well. Robustness, low requirement for samples and adaptive is presented in the experiment.

Strip Steel Surface Defects Detection Based on Low Rank Multi-Kernel Support Vector Machine

Strip Steel Surface Defects Detection Based on Low Rank Multi-Kernel Support Vector Machine

Formation and prevention of flexure defects at the surface of cold-rolled steel strip

The formation of flexure defects when coils of thin cold-rolled steel strip are unwound is considered. The influence of the strip thickness, surface roughness, temperature, properties of the steel, and other factors on the formation of such defects is analyzed. Measures are proposed for the prevention of the defects.

The Strip Steel Surface Defect Recognition Based on Multiple Support Vector Hyper-Sphere with Feature and Sample Weights

The Strip Steel Surface Defect Recognition Based on Multiple Support Vector Hyper-Sphere with Feature and Sample Weights