Stream Surface Strip Element Method and Simulation of Three-Dimensional Deformation of Continuous Hot Rolled Strip

A new method,the stream surface strip element method,for simulating the three-dimensional deformation of plate and strip rolling process was proposed.The rolling deformation zone was divided into a number of stream surface(curved surface)strip elements along metal flow traces,and the stream surface strip elements were mapped into the corresponding plane strip elements for analysis and computation.The longitudinal distributions of the lateral displacement and the altitudinal displacement of metal were respectively constructed to be a quartic curve and a quadratic curve,of which the lateral distributions were expressed as the third-power spline function,and the altitudinal distributions were fitted in the quadratic curve.From the flow theory of plastic mechanics,the mathematical models of the three-dimensional deformations and stresses of the deformation zone were constructed.Compared with the streamline strip element method proposed by the first author of this paper,the stream surface strip element method takes into account the uneven distributions of stresses and deformations along altitudinal direction,and realizes the precise three-dimensional analysis and computation.The simulation example of continuous hot rolled strip indicates that the method and the model accord with facts and provide a new reliable engineering-computation method for the three-dimensional mechanics simulation of plate and strip rolling process.

Automated visual inspection of surface defects based on compound moment invariants and support vector machine

The traditional inspection methods are mostly based on manual inspection which is very likely to make erroneous judgments due to personal subjectivity or eye fatigue, and can＇t satisfy the accuracy. To overcome these difficulties, we develop a machine vision inspection system. We first compare several kinds of methods for feature extraction and classification, and then present a real-time automated visual inspection system for copper strips surface （CSS） defects based on compound moment invariants and support vector machine （SVM）. The proposed method first processes images collected by hardware system, and then extracts feature characteristics based on grayscale characteristics and morphologic characteristics （Hu and Zernike compound moment invariants）. Finally, we use SVM to classify the CSS defects. Furthermore, performance comparisons among SVM, back propagation （BP） and radial basis function （RBF） neural networks have been involved. Experimental results show that the proposed approach achieves an accuracy of 95.8% in detecting CSS defects.

Shape Control Simulation on 4-High CVC Mill

The computation model of shape and crown on 4-high CVC mill was established by combining the stream surface strip element method for analyzing three-dimensional plastic deformation of strip and the influence coefficient method for elastic deformation of rolls, and the simulation of the shape and crown control on 4-high CVC hot strip mill was conducted. The simulated results indicate that the influence of the shifting of CVC work roll on shape and crown is very large, and the shifting of work roll can be used to preset shape and crown. The influence of the bending force of work roll on shape and crown is smaller, and it is suitable to use the bending force of work roll for shape and crown adjustment on line. With the increase of strip width, the exit crown of strip increases firstly and decreases then, and the roll gap becomes smoother increasingly. Meanwhile, the transverse difference of front tension stress decreases firstly and increases then.

Complete Mill Simulation of the Rolling Process of 1660 mm Hot Strip Continuous Mills

The three-dimensional plastic deformations of strip are analyzed using the stream surface strip element method, the elastic deformations of rolls are analyzed using the influence coefficient method, the analyzing and computing model of shape and crown of 4-high mill was established by combining them, and the rolling process of 1660 mm hot strip continuous mills was simulated. The simulated results tally well with the experimental results. The modei and the method for simulation of shape analysis and control of hot strip mills were provided.

An FPGA-based real-time image processing system

This paper analyzes the current difficulties encountered in on-line inspection systems of strip surface quality, specifically relating to problems with real-time processing of huge amounts of data. To address this need, this paper describes an FPGA-based high-speed image processing module with both hardware and software aspects. Improving these two aspects together will help the system achieve real-time processing of massive image data, and simplifies the architecture of the strip surface quality on-line inspection system.

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.