Wear and Contact Temperature on Steel Surface in Linear Dry Friction Contact with Polimers with SGF

In this paper, we tried to present a qualitative correlation, based on extensive experimental trials between the value and the evolution of the friction coefficient, wear and contact temperature in the case of linear dry contact, thermoplastic material reinforced with SGF （short glass fibres） and various steel surfaces. We tried, believing successfully, the graphic illustration of the evolution of the steel surface wear and of the contact temperature, depending on the friction coefficient. It was analyzed in detail the influence of the normal load and sliding speed, but also of the metallic surface roughness on the friction coefficient.

A qualitative correlation between friction coefficient and steel surface wear in linear dry sliding contact to polymers with SGF

In this paper we tried to present a qualitative correlation,based on extensive experimental determinations between the value and the evolution of the friction coefficient,wear,and contact temperature,in the case of linear dry contact,for thermoplastic material reinforced with short glass fibers(SGF)and various steel surfaces.The aim was to highlight the evolution of the wear process depending on the evolution of the friction coefficient.As a result,it was possible to graphically illustrate the evolution of the friction coefficient and the change of the wear process,emphasizing the abrasive,adhesive and corrosive wear.The evolution of the plastic material transfer function of the contact temperature,namely of the power lost by friction(product between the contact pressure and sliding speed,p and v)was aimed and it was highlighted.It has been demonstrated that in the case of a 30%SGF content it can reach and even exceed contact temperatures very close to the flow limit of the plastic material.We tried,believing successfully,the graphic illustration of the evolution of the steel surface wear and of the contact temperature,depending on the friction coefficient.The influence of the normal load and sliding speed was evaluated in detail,but also the influence of the metallic surface roughness on the friction coefficient was discussed.

Multi-class Classification Methods of Enhanced LS-TWSVM for Strip Steel Surface Defects

Considering strip steel surface defect samples, a multi-class classification method was proposed based on enhanced least squares twin support vector machines （ELS-TWSVMs） and binary tree. Firstly, pruning region samples center method with adjustable pruning scale was used to prune data samples. This method could reduce classifierr s training time and testing time. Secondly, ELS-TWSVM was proposed to classify the data samples. By introducing error variable contribution parameter and weight parameter, ELS-TWSVM could restrain the impact of noise sam- ples and have better classification accuracy. Finally, multi-class classification algorithms of ELS-TWSVM were pro- posed by combining ELS-TWSVM and complete binary tree. Some experiments were made on two-dimensional data- sets and strip steel surface defect datasets. The experiments showed that the multi-class classification methods of ELS-TWSVM had higher classification speed and accuracy for the datasets with large-scale, unbalanced and noise samples.

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.

Unbalanced classification method using least squares support vector machine with sparse strategy for steel surface defects with label noise

Least squares support vector machine (LS-SVM) plays an important role in steel surface defects classification because of its high speed. However, the defect samples obtained from the real production line may be noise. LS-SVM suffers from the poor classification performance in the classification stage when there are noise samples. Thus, in the classification stage, it is necessary to design an effective algorithm to process the defects dataset obtained from the real production line. To this end, an adaptive weight function was employed to reduce the adverse effect of noise samples. Moreover, although LSSVM offers fast speed, it still suffers from a high computational complexity if the number of training samples is large. The time for steel surface defects classification should be as short as possible. Therefore, a sparse strategy was adopted to prune the training samples. Finally, since the steel surface defects classification belongs to unbalanced data classification, LSSVM algorithm is not applicable. Hence, the unbalanced data information was introduced to improve the classification performance. Comprehensively considering above-mentioned factors, an improved LS-SVM classification model was proposed, termed as ILS-SVM. Experimental results show that the new algorithm has the advantages of high speed and great anti-noise ability.

Multi-class classification method for steel surface defects with feature noise

Defect classification is the key task of a steel surface defect detection system.The current defect classification algorithms have not taken the feature noise into consideration.In order to reduce the adverse impact of feature noise,an anti-noise multi-class classification method was proposed for steel surface defects.On the one hand,a novel anti-noise support vector hyper-spheres(ASVHs)classifier was formulated.For N types of defects,the ASVHs classifier built N hyper-spheres.These hyper-spheres were insensitive to feature and label noise.On the other hand,in order to reduce the costs of online time and storage space,the defect samples were pruned by support vector data description with parameter iteration adjustment strategy.In the end,the ASVHs classifier was built with sparse defect samples set and auxiliary information.Experimental results show that the novel multi-class classification method has high efficiency and accuracy for corrupted defect samples in steel surface.

Laser cleaning of steel structure surface for paint removal and repaint adhesion

Paint removal from steel structure is executed for shipyards of marine and offshore engineering.Due to environmental unfriendliness and unhealthy drawbacks of sand blasting technique, laser ablation technique is proposed as a substituting method.By absorbing high energy of the 1064 nm pulsed laser, the paint is vaporized quickly.The ablated debris is then collected by using a suction pump.Initial metal surface of the steel is exposed when laser beam irradiates perpendicularly and scans over it.The cleaned surface fulfills the requirements of surface preparation standards ISO 8501 of SA2.The adhesion is further characterized with pull-off test after carrying out painting with Jotamastic 87 aluminum paint.The repainting can be embedded onto the laser cleaned surface to bond much more tightly.The excellent adhesion strength of 20 MPa between repainted coating and the substrate is achieved, which is higher than what is required by shipyards applications.

Immersing Treatment of Steel Plate Surface in Steel-Aluminum Solid to Liquid Bonding

The interfacial status of the steel-aluminum solid to liquid bonding plates (their steel plate surfaces were or were not immersed in flux aqueous solution) were measured by using SEM (Scanning Electron Microscope) and X-ray diffraction . The results showed that the layer of flux (the minimum thickness was 15 μm on the steel plate surface) could protect the steel plate surface from oxidizing effectively at high temperature in solid to liquid bonding. The melt temperatUre of the flux should be lower than 580 ℃ so that it could be melted and removed completely. No. 1 flux (patent product made by the author) made up of halogeindes could also force liquid aluminum to infiltrate into steel plate surface and thus the interfacial shear strength of the bonding plate was rather large.

Experimental Study on Material Surface Modification of Tool Steel

This paper presents the surface temperature behavior of M42 high-speed tool steel samples during N+ implantation in an industrialized GLZ-100 metal-ion implantation machine. A detail study has been made on the parameters of N+ implantation. Optimized technical parameters have been presented. The microhardness of the sample surface implanted under these parameters has been increased by a factor of 2.3, and the wear-resistance has been improved by about 5.4 times. The research on the mechanism of surface modification of M42 steel by nitrogen ion implantation has also been made.

Optimizing Domain Distribution of Grain Oriented Silicon Steel by Using Antimony as the Laser Surface Alloying Element

For reducing the core loss of grain oriented silicon steel and improving its aging property, a new method, the LLSA by using Sb as the laser surface alloying element, was investigated, and at proper technique conditions rather good result was obtained.

DLF-YOLOF:an improved YOLOF-based surface defect detection for steel plate

Surface defects can affect the quality of steel plate.Many methods based on computer vision are currently applied to surface defect detection of steel plate.However,their real-time performance and object detection of small defect are still unsatisfactory.An improved object detection network based on You Only Look One-level Feature(YOLOF)is proposed to show excellent performance in surface defect detection of steel plate,called DLF-YOLOF.First,the anchor-free detector is used to reduce the network hyperparameters.Secondly,deformable convolution network and local spatial attention module are introduced into the feature extraction network to increase the contextual information in the feature maps.Also,the soft non-maximum suppression is used to improve detection accuracy significantly.Finally,data augmentation is performed for small defect objects during training to improve detection accuracy.Experiments show the average precision and average precision for small objects are 42.7%and 33.5%at a detection speed of 62 frames per second on a single GPU,respectively.This shows that DLF-YOLOF has excellent performance to meet the needs of industrial real-time detection.

Study on low temperature performance of Gussasphalt on steel decks with hard bitumen

Using a Hamburg wheel-track test device, the resistance to rutting of Gussasphalt is tested and compared. Gussasphalt with hard bitumen has good resistance to rutting. The related resistance abilities to cracking at low temperature of Gussasphalt are tested and compared through flexural experiments and the composite structure fatigue test with temperature dropping. Gussasphalt with high performance polymer modified bitumen has a longer fatigue life and a lower breaking temperature; they can be used in the future surfaces for steel bridge decks in Germany.

Effects of asymmetric rolling process on ridging resistance of ultra-purified 17%Cr ferritic stainless steel

In ferritic stainless steels, a significant non-uniform recrystallization orientation and a substantial texture gradient usually occur, which can degrade the ridging resistance of the final sheets. To improve the homogeneity of the recrystallization orientation and reduce the texture gradient in ultra-purified 17%Cr ferritic stainless steel, in this work, we performed conventional and asymmetric rolling processes and conducted macro and micro-texture analyses to investigate texture evolution under different cold-rolling conditions. In the conventional rolling specimens, we observed that the deformation was not uniform in the thickness direction, whereas there was homogeneous shear deformation in the asymmetric rolling specimens as well as the formation of uniform recrystallized grains and random orientation grains in the final annealing sheets. As such, the ridging resistance of the final sheets was significantly improved by employing the asymmetric rolling process. This result indicates with certainty that the texture gradient and orientation inhomogeneity can be attributed to non-uniform deformation, whereas the uniform orientation gradient in the thickness direction is explained by the increased number of shear bands obtained in the asymmetric rolling process.

Present Situation of the Anti-Fatigue Processing of High-Strength Steel Internal Thread Based on Cold Extrusion Technology:A Review

The adoption of cold-extrusion forming for internal thread net forming becomes an important component of anti-fatigue processing with the development of internal thread processing towards high performance, low cost and low energy consumption. It has vast application foreground in the field of aviation, spaceflight, high speed train and etc. The internal thread processing and anti-fatigue manufacture technology are summarized. In terms of the perspective of processing quality and fatigue serving life, the advantages and disadvantages of the processing methods from are compared. The internal thread cold-extrusion processing technology is investigated for the purpose of improving the anti-fatigue serving life of internal thread. The superiorities of the plastic deformation law and surface integrity of the metal layer in the course of cold extrusion for improving its stability and economy are summed up. The proposed research forecasts the develop- ment tendency of the internal thread anti-fatigue manufacturing technology.

Hysteretic behavior of cambered surface steel tube damper:Theoretical and experimental research

A novel cambered surface steel tube damper(CSTD)with a cambered surface steel tube and two concave connecting plates is proposed herein.The steel tube is the main energy dissipation component and comprises a weakened segment in the middle,a transition segment,and an embedded segment.It is believed that during an earthquake,the middle weakened segment of the CSTD will be damaged,whereas the reliability of the end connection is ensured.Theoretical and experimental studies are conducted to verify the effectiveness of the proposed CSTD.Formulas for the initial stiffness and yield force of the CSTD are proposed.Subsequently,two CSTD specimens with different steel tube thicknesses are fabricated and tested under cyclic quasi-static loads.The result shows that the CSTD yields a stable hysteretic response and affords excellent energy dissipation.A parametric study is conducted to investigate the effects of the steel tube height,diameter,and thickness on the seismic performance of the CSTD.Compared with equalstiffness design steel tube dampers,the CSTD exhibits better energy dissipation performance,more stable hysteretic response,and better uniformity in plastic deformation distributions.

Surface microstructure control of microalloyed steel during slab casting

Lots of work has been done to investigate slab surface microstructure evolution during continuous casting in order to improve hot ductility and avoid transverse cracks.The slab surface microstructure after continuous casting was characterized by optical microscopy,and the precipitation behavior was investigated by transmission electron microscopy.At the same time,the mechanical properties of the slabs were measured using a Gleeble 1500 Dthermal simulator and the transformation temperatures were examined by means of a thermal dilatometer.The experimental results show that homogeneous microstructure without film-like ferrites and chain-like precipitates at grain boundary can be obtained through surface intensive cooling and transverse cracks do not occur on the slab surface.For the experimental steel,fine ferrite can form at slab surface when the water flow rate is larger than 1560L/min at vertical section.As the distance to surface increases,microstructure turned to ferrite and pearlite.Moreover,nano-size carbonitrides precipitated in the ferrite grain and the size was larger at the junction of the dislocations.The mechanical experiment results show that the hot ductility of the sample deformed at 650°C was better than that of the sample deformed at 750°C.The reason is that filmlike ferrite formed at the grain boundary in the sample deformed at 750°C.Thus,the slab must be cooled quickly below Ar3 to prevent the occurrence of film-like ferrite and transverse cracks on the slab surface during casting.

Modification of wettability of stainless steel by picosecond laser surface microstructuring

We report on the modification of the wettability of stainless steel by picosecond laser surface microstructuring in this paper. Compared with traditional methods, picosecond laser-induced surface modification provides a fast and facile method for surface modification without chemical damage and environmental pollution. As a result of treatment by 100 ps laser pulses, microstructures are fabricated on the stainless steel sample surface, contributing to the increase of the contact angle from 88° to 105°, which realizes a transformation from hydrophilicity to hydrophobicity. The morphological features of fabricated microstructures are characterized by scanning electron microscopy and optical microscopy.

Improvement of Surface Ridging Resistance of an Ultra-purified Ferritic Stainless Steel by Optimizing Hot Rolling Condition

The effects of the finisher entry temperatures（FETs） on the surface ridging behavior for an ultra-purified21%Cr ferritic stainless steel have been investigated. The results indicate that decreasing the FET facilitates the formation of in-grain shear bands in the hot rolled slab. The in-grain shear bands supplied recrystallization nucleation sites in grains during subsequent annealing through coalescence of subgrains, which is beneficial to refine the microstructures and intensify the {111}//ND textures. This effect will evolve to the final cold rolled and annealed sheet. The micro-texture analysis indicates that the formation of grain colony in the final sheet is weakened by decreasing the FET. Then, the surface ridging resistance of FSS is enhanced due to the optimizing of micro-texture distribution.

Secondary Hardening, Austenite Grain Coarsening and Surface Decarburization Phenomenon in Nb-Bearing Spring Steel

The secondary hardening, the austenite grain coarsening and the surface decarburization phenomenon of Nb-bearing spring steel were investigated, and the effects of niobium on tempered microstructure was studied using scanning electron microscope. The results show that the micro-addition of niobium increases the tempering resistance and produces secondary hardening. The effect of niobium on the size and distribution of cementite particles is one of the primary reasons to increase the hardness after tempering. The grain-coarsening temperature of the spring steel is raised 150 ~C due to Nb-addition. Furthermore, both the secondary hardening and the austenite grain coarsening phenomenon congruously demonstrate niobium begins observably dissolving above 1 100 ℃ in the spring steel. Be- sides, niobium microalloying is an effective and economy means to decrease the decarburization sensitivity of the spring steels.

Strength Prediction of Aluminum–Stainless Steel-Pulsed TIG Welding–Brazing Joints with RSM and ANN

Pulsed TIG welding–brazing process was applied to join aluminum with stainless steel dissimilar metals. Major parameters that affect the joint property significantly were identified as pulsed peak current, base current, pulse on time,and frequency by pre-experiments. A sample was established according to central composite design. Based on the sample,response surface methodology（RSM） and artificial neural networks（ANN） were employed to predict the tensile strength of the joints separately. With RSM, a significant and rational mathematical model was established to predict the joint strength.With ANN, a modified back-propagation algorithm consisting of one input layer with four neurons, one hidden layer with eight neurons, and one output layer with one neuron was trained for predicting the strength. Compared with RSM, average relative prediction error of ANN was ／10% and it obtained more stable and precise results.