Flatness Prediction of Cold Rolled Strip Based on Deep Neural Network with Improved Activation Function

With the improvement of industrial requirements for the quality of cold rolled strips, flatness has become one of the most important indicators for measuring the quality of cold rolled strips. In this paper, the strip production data of a 1250 mm tandem cold mill in a steel plant is modeled by an improved deep neural network (the improved DNN) to improve the accuracy of strip shape prediction. Firstly, the type of activation function is analyzed, and the monotonicity of the activation function is deemed independent of the convexity of the loss function in the deep network. Regardless of whether the activation function is monotonic, the loss function is not strictly convex. Secondly, the non-convex optimization of the loss functionextended from the deep linear network to the deep nonlinear network, is discussed, and the critical point of the deep nonlinear network is identified as the global minimum point. Finally, an improved Swish activation function based on batch normalization is proposed, and its performance is evaluated on the MNIST dataset. The experimental results show that the loss of an improved Swish function is lower than that of other activation functions. The prediction accuracy of a deep neural network (DNN) with an improved Swish function is 0.38% more than that of a deep neural network (DNN) with a regular Swish function. For the DNN with the improved Swish function, the mean square error of the prediction for the flatness of cold rolled strip is reduced to 65% of the regular DNN. The accuracy of the improved DNN is up to and higher than the industrial requirements. The shape prediction of the improved DNN will assist and guide the industrial production process, reducing the scrap yield and industrial cost.

Fracture Behavior of the Hot Rolled Strip’s Surface Scale Layer from Different Impacts Angles

In order to improve the energy efficiency of shot blasting impact descaling, a three-dimensional finite element impact descaling model was established. Based on the finite element model, the cracking behavior of the scale layer on hot rolled strip from different impacts angles was simulated. The results of finite element calculation and theoretical analysis show that: (1)Under the premise of constant velocity, the descaling area increases with the increase of impact angle, but the increasing rate tends to be moderate. (2)The depth of the impact tunnel and the residual compressive stress surface (-200 MPa) increase as the impact angle goes bigger. The ideal range of impact angle for shot blasting descaling should be 60°-75°.

UDDEHOLM VANADIS 23 SUPERCLEAN

Uddeholm Vanadis 23 SuperClean is a chromium-molybdenum-tungsten-vanadium high speed tool steel that is produced by the powder metallurgy process. This steel is similar to Type M3, Class 2 high speed tool steel, and combines high compressive strength with good abrasive wear resistance. It is suitable for demanding cold work applications like blanking of harder materials like carbon steel or cold rolled strip steel and for cutting tools. This datasheet provides information on composition, physical properties, hardness, and elasticity. It also includes information on heat treating, machining, and surface treatment. Filing Code: TS-820. Producer or source: Uddeholms AB (a voestalpine company).

An improved Vision Transformer-based method for classifying surface defects in hot-rolled strip steel

A new Vision Transformer(ViT) model is proposed for the classification of surface defects in hot rolled strip, optimizing the poor learning ability of the original Vision Transformer model on smaller datasets. Firstly, each module of ViT and its characteristics are analyzed; Secondly, inspired by the deep learning model VGGNet, the multilayer fully connected layer in VGGNet is introduced into the ViT model to increase its learning capability; Finally, by performing on the X-SDD hot-rolled steel strip surface defect dataset. The effect of the improved algorithm is verified by comparison experiments on the X-SDD hot-rolled strip steel surface defect dataset. The test results show that the improved algorithm achieves better results than the original model in terms of accuracy, recall, F1 score, etc. Among them, the accuracy of the improved algorithm on the test set is 5.64% higher than ViT-Base and 2.64% higher than ViT-Huge; the accuracy is 4.68% and 1.36% higher than both of them, respectively.

Effect of Carbon Content on Microstructure, Properties and Texture of Ultra-Thin Hot Rolled Strip Produced by Endless Roll Technology

In order to make a comprehensive comparison between ultra-thin hot rolled low carbon steel (LC) and extra low carbon steel (ELC) produced by endless roll technology and explain the differences, a detailed investigation into the microstructural characterization, characteristics of cementite and precipitates, mechanical properties, internal friction peaks, texture characterization by an X-ray powder diffractometer and electron backscatter diffraction, and formability including earing behavior, hole expanding ratio and V-shaped bending properties was carried out with different carbon content for 1.0 mm thickness ultra-thin hot rolled strip produced in endless strip production line. The experimental results indicate that the microstructure of both is composed of multi-layer areas with different grain sizes and thicknesses, the strength and elongation of LC are higher than that of ELC, but the content of solid solution carbon atoms and r of ELC are higher than that of LC, at the same time, the formability of ultra-thin strip ELC is better than that of LC mainly related to the content of {hkl} <110> and {111} <112> of ELC was higher than those of LC. The mechanical and formability properties of ultra-thin hot rolled strip by endless roll technology can meet the requirements of replacement cold rolled strip by hot rolled strip.

A ResNet50-Based Method for Classifying Surface Defects in Hot-Rolled Strip Steel

Hot-rolled strip steel is widely used in automotive manufacturing, chemical and home appliance industries, and its surface quality has a great impact on the quality of the final product. In the manufacturing process of strip steel, due to the rolling process and many other reasons, the surface of hot rolled strip steel will inevitably produce slag, scratches and other surface defects. These defects not only affect the quality of the product, but may even lead to broken strips in the subsequent process, seriously affecting the continuation of production. Therefore, it is important to study the surface defects of strip steel and identify the types of defects in strip steel. In this paper, a scheme based on ResNet50 with the addition of FcaNet and Convolutional Block Attention Module (CBAM) is proposed for strip defect classification and validated on the X-SDD strip defect dataset. Our solution achieves a classification accuracy of 94.11%, higher than more than a dozen other compared deep learning models. Moreover, to adress the problem of low accuracy of the algorithm in classifying individual defects, we use ensemble learning to optimize. By integrating the original solution with VGG16 and SqueezeNet, the recognition rate of oxide scale of plate system defects improved by 21.05 percentage points, and the overall defect classification accuracy improved to 94.85%.

Shear-based deformation processing of age-hardened aluminum alloy for single-step sheet production

Shear-based deformation processing by hybrid cutting-extrusion and free machining are used to make continuous strip, of thickness up to one millimeter, from low-workability AA6013-T6 in a single deformation step. The intense shear can impose effective strains as large as 2 in the strip without pre-heating of the workpiece. The creation of strip in a single step is facilitated by three factors inherent to the cutting deformation zone: highly confined shear deformation, in situ plastic deformation-induced heating and high hydrostatic pressure. The hybrid cutting-extrusion, which employs a second die located across from the primary cutting tool to constrain the chip geometry, is found to produce strip with smooth surfaces (Sa &lt; 0.4 μm) that is similar to cold-rolled strip. The strips show an elongated grain microstructure that is inclined to the strip surfaces – a shear texture – that is quite different from rolled sheet. This shear texture (inclination) angle is determined by the deformation path. Through control of the deformation parameters such as strain and temperature, a range of microstructures and strengths could be achieved in the strip. When the cutting-based deformation was done at room temperature, without workpiece pre-heating, the starting T6 material was further strengthened by as much as 30% in a single step. In elevated-temperature cutting-extrusion, dynamic recrystallization was observed, resulting in a refined grain size in the strip. Implications for deformation processing of age-hardenable Al alloys into sheet form, and microstructure control therein, are discussed.