Laser-induced breakdown spectroscopy as a method for millimeter-scale inspection of surface flatness

A non-contact method for millimeter-scale inspection of material surface flatness via Laser-Induced Breakdown Spectroscopy(LIBS)is investigated experimentally.The experiment is performed using a planished surface of an alloy steel sample to simulate its various flatness,ranging from 0 to 4.4 mm,by adjusting the laser focal plane to the surface distance with a step length of 0.2 mm.It is found that LIBS measurements are successful in inspecting the flatness differences among these simulated cases,implying that the method investigated here is feasible.It is also found that,for achieving the inspection of surface flatness within such a wide range,when univariate analysis is applied,a piecewise calibration model must be constructed.This is due to the complex dependence of plasma formation conditions on the surface flatness,which inevitably complicates the inspection procedure.To solve the problem,a multivariate analysis with the help of Back-Propagation Neural Network(BPNN)algorithms is applied to further construct the calibration model.By detailed analysis of the model performance,we demonstrate that a unified calibration model can be well established based on BPNN algorithms for unambiguous millimeter-scale range inspection of surface flatness with a resolution of about 0.2 mm.

Differential flatness-based distributed control of underactuated robot swarms

This paper proposes a distributed control method based on the differential flatness(DF) property of robot swarms. The swarm DF mapping is established for underactuated differentially flat dynamics, according to the control objective. The DF mapping refers to the fact that the system state and input of each robot can be derived algebraically from the flat outputs of the leaders and the cooperative errors and their finite order derivatives. Based on the proposed swarm DF mapping, a distributed controller is designed. The distributed implementation of swarm DF mapping is achieved through observer design. The effectiveness of the proposed method is validated through a numerical simulation of quadrotor swarm synchronization.

Nanogrinding of SiC wafers with high flatness and low subsurface damage

Nanogrinding of SiC wafers with high flatness and low subsurface damage was proposed and nanogrinding experiments were carried out on an ultra precision grinding machine with fine diamond wheels. Experimental results show that nanogrinding can produce flatness less than 1.0μm and a surface roughness Ra of 0.42nm. It is found that nanogrinding is capable of producing much flatter SiC wafers with a lower damage than double side lapping and mechanical polishing in much less time and it can replace double side lapping and mechanical polishing and reduce the removal amount of chemical mechanical polishing.

An Uncertainty Assessment Approach for Measuring Flatness Error in Close Way

It is necessary for precise measurement to estimate the uncertainty of measurement result. When measuring flatness error in close way by pitch, usually the uncertainty of measurement result is independently estimated according to pitch points. By analyzing a concrete example, this paper proposed that the uncertainty should be evaluated by the correlation calculating method. This approach greatly improved the deficiencies of the assessment method according to independent measurement and enhanced measurement precision. It provides a reference value for uncertainty assessment in leveling a flat.

Flatness Control Based on Dynamic Effective Matrix for Cold Strip Mills

Steel strips are the main of steel products and flatness is an important quality indicator of steel strips. Flatness control is the key and highly difficult technique of strip mills. The bottle-neck restricting the improvement of flatness control techniques is that the research on flatness theories and control mathematic models is not in accordance with the requirement of technique developments. To build a simple, rapid and accurate explicit formulation control model has become an urgent need for the development of flatness control technique. This paper puts forward the conception of dynamic effective matrix based on the effective matrix method for flatness control proposed by the authors under the consideration of the influence of the change of parameters in roiling processes on the effective matrix, and the concept is validated by industrial productions. Three methods of the effective matrix generation are induced： the calculation method based on the flatness prediction model; the calculation method based on the data excavation in rolling processes and the direct calculation method based on the network model. A fuzzy neural network effective matrix model is built based on the clusters, and then the network structure is optimized and the high-speed-calculation problem of the dynamic effective matrix is solved. The flatness control scheme for cold strip mills is proposed based on the dynamic effective matrix. On stand 5 of the 1 220 mm five-stand 4-high cold strip tandem mill, the industrial experiment with the control methods of tilting roll and bending roll is done by the control scheme of the static effective matrix and the dynamic effective matrix, respectively. The experiment result proves that the control effect of the dynamic effective matrix is much better than that of the static effective matrix. This paper proposes a new idea and method for the dynamic flatness control in the rolling processes of cold strip mills and develops the theory and model of the flatness control effective matrix method.

Dynamic characteristics of cold rolling mill and strip based on flatness and thickness control in rolling process

The dynamic model of cold rolling mill based on strip flatness and thickness integrated control was proposed,containing the following sub-models:the rolling process model,the dynamic model of rolls along axial direction,and the compensation model.Based on the rule of volume flow rate,the dynamic rolling process model was built.The work roll and backup roll were taken as elastic continuous bodies,the effect of shear and moment of inertia were taken into consideration,and then the dynamic model of rolls was built.The two models were coupled together,and the dynamic model of rolling mill was built.In the dynamic model,the thermal expansion of the rolls,the wear of the rolls and other related parameters can not be considered.In order to compensate the dynamic model,the coupled static model of rolls and strip was applied.Then,according to the inner relationship of these models,the dynamic model and the compensation model were coupled,and the dynamic model of rolling mill based on the strip flatness and thickness integrated control was built.The dynamic simulation of the rolling process was made,and the dynamic thickness and the dynamic flatness information were obtained.This model not only provides a theory basis for the virtual rolling,but also provides a platform for the application of advanced control theory.

VCR and ASR technology for profile and flatness control in hot strip mills

The roll contour pattern and variety of work and backup rolls in service and its effect on profile and flatness control performance in 1 700 mm hot strip mill at Wuhan Iron and Steel(Group) Corporation were tested and analyzed by the developed finite element models of different typical roll contours configurations.A rather smooth local work roll contour near strip edges and an increase in rolled length can be obtained by application of long stroke work roll shifting system with conventional work roll contours that is incapable of the crown control.In comparison with the conventional backup and work roll contours configuration,the crown control range by the roll bending force enhances by 12.79% and the roll gap stiffness increases by 25.26% with the developed asymmetry self-compensating work rolls(ASR) and varying contact backup rolls(VCR).A better strip profile and flatness quality,an increase in coil numbers within the rolling campaign and a significant alleviated effect of severe work roll wear contours on performance of edge drop control are achieved by the application of ASR with crown control and wear control ability in downstream stand F5 and VCR in all stands of 1 700 mm hot strip mill.

Flatness predictive model based on T-S cloud reasoning network implemented by DSP

The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.

Application of Crown-Flatness Vector Analysis in Plate Rolling Schedule

A simple plate crown model was introduced,and the crown-flatness vector analysis method was analyzed.Based on the plate rolling technology,the rolling schedule design of elongation phase is divided into three steps.First step is to calculate the reductions of first pass of elongation making full use of the mill capability to decrease the total pass number.The second step is to calculate the pass reduction for the last three or four passes to control crown and flatness by crown-flatness vector analysis method.In the third step,the maximum rolling force limit and the total pass number are adjusted to make the plate gauge at exit equal to target gauge with satisfactory flatness.The on-line application shows that this method is effective.

Flatness intelligent control via improved least squares support vector regression algorithm

To overcome the disadvantage that the standard least squares support vector regression(LS-SVR) algorithm is not suitable to multiple-input multiple-output(MIMO) system modelling directly,an improved LS-SVR algorithm which was defined as multi-output least squares support vector regression(MLSSVR) was put forward by adding samples' absolute errors in objective function and applied to flatness intelligent control.To solve the poor-precision problem of the control scheme based on effective matrix in flatness control,the predictive control was introduced into the control system and the effective matrix-predictive flatness control method was proposed by combining the merits of the two methods.Simulation experiment was conducted on 900HC reversible cold roll.The performance of effective matrix method and the effective matrix-predictive control method were compared,and the results demonstrate the validity of the effective matrix-predictive control method.

Research of the image processing in dynamic flatness detection based on improved laser triangular method

As a commonly used non-contact flatness detection method, laser triangular detection method is designed with low cost, but it cannot avoid measurement errors caused by strip steel vibration effectively. This paper puts forward a dynamic flatness image processing method based on improved laser triangular detection method. According to the practical application of strip steel straightening, it completes the image pre-processing, image feature curve extraction and calculation of flatness elongation using digital image processing technology. Finally it eliminates elongation measurement errors caused by the vibration.

REQUIREMENT OF SILICON FLATNESS FOR SILICON DIRECT BONDING TECHNOLOGY

The influence of silicon slice flatness on bonding technology and the relation between a foreign particle and resulting bubble are quantitatively presented by the elastic theory. It is demonstrated experimentally by X-ray double crystal diffractometry and infrared imager.

Development and application of cold strip rolling flatness control system in Ansteel

In this paper,the flatness control technology AnShaper^(TM) for cold-rolling mill and industry application are introduced.AnShaper^(TM) includes;partitioning piezomagnetic shape meter for flatness measurement for cold-rolling strip;flatness measured signals processing system based on digital signal processing(DSP);flatness feedback control model system based on the control efficiency of flatness control actuators and model adaptive function.The application verifies that strip flatness can meet the need of high quality product by using AnShaperTM.The average flatness quality is about 5Ⅰ-unit and the 0.18 mm ultrathin thickness strip flatness is 10Ⅰ-unit.

Intelligent representation method of image flatness for cold rolled strip

Real flatness images are the bases for flatness detection based on machine vision of cold rolled strip.The characteristics of a real flatness image are analyzed,and a lightweight strip location detection(SLD)model with deep semantic segmentation networks is established.The interference areas in the real flatness image can be eliminated by the SLD model,and valid information can be retained.On this basis,the concept of image flatness is proposed for the first time.An image flatness representation(IFAR)model is established on the basis of an autoencoder with a new structure.The optimal structure of the bottleneck layer is 16×16×4,and the IFAR model exhibits a good representation effect.Moreover,interpretability analysis of the representation factors is carried out,and the difference and physical meaning of the representation factors for image flatness with different categories are analyzed.Image flatness with new defect morphologies(bilateral quarter waves and large middle waves)that are not present in the original dataset are generated by modifying the representation factors of the no wave image.Lastly,the SLD and IFAR models are used to detect and represent all the real flatness images on the test set.The average processing time for a single image is 11.42 ms,which is suitable for industrial applications.The research results provide effective methods and ideas for intelligent flatness detection technology based on machine vision.

Strip flatness prediction of cold rolling based on ensemble methods

Aiming at the problem of insufficient prediction accuracy of strip flatness at the outlet of cold tandem rolling,the prediction performance of strip flatness based on different ensemble methods was studied and a high-precision prediction ensemble model of strip flatness at the outlet was established.Firstly,based on linear regression(LR),K nearest neighbors(KNN),support vector regression,regression trees(RT),and backpropagation neural network(BPN),bagging,boosting,and stacking ensemble methods were used for ensemble experiments.Secondly,three existing ensemble models,i.e.,random forest,extreme random tree(ET)and extreme gradient boosting,were used to conduct experiments and compare the results.The research shows that bagging,boosting,and stacking three ensemble methods have the most significant improvement in the prediction accuracy of the regression trees model,which is increased by 5.28%,6.51%,and 5.32%,respectively.At the same time,the stacking ensemble method improves both the simple model and the complex model,and the improvement effect on the simple base model is the greatest,which is 4.69%higher than that of the base model KNN.Comparing all of the ensemble models,the stacking ensemble model of level-1(ET,AdaBoost-RT,LR,BPN)paired with level-2(LR)was discovered to be the best model(EALB-LR)and can be further studied for industrial applications.

Flatness-Based Control in Successive Loops for Unmanned Aerial Vehicles and Micro-Satellites

The control problem for the multivariable and nonlinear dynamics of unmanned aerial vehicles and micro-satellites is solved with the use of a flatness-based control approach which is implemented in successive loops.The state-space model of(i)unmanned aerial vehicles and(ii)micro-satellites is separated into two subsystems,which are connected between them in cascading loops.Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems.The state variables of the second subsystem become virtual control inputs for the first subsystem.In turn,exogenous control inputs are applied to the first subsystem.The whole control method is implemented in two successive loops and its global stability properties are also proven through Lyapunov stability analysis.The validity of the control method is confirmed in two case studies:(a)control and trajectories tracking for the autonomous octocopter,(ii)control of the attitude dynamics of micro-satellites.

Data-driven flatness intelligent representation method of cold rolled strip

A high-accuracy flatness prediction model is the basis for realizing flatness control.Real flatness is typically reflected as the strain distribution,which is a vector.However,it is difficult to obtain ideal results if the real flatness is directly used as the output value of the flatness intelligent prediction model.Thus,it is necessary to seek an abstract representation method of real flatness.For this reason,two new intelligent flatness representation models were proposed based on the autoencoder of unsupervised learning theory:the flatness autoencoder representation(FAR)model and the flatness stacked sparse autoencoder representation(FSSAR)model.Compared with the traditional Legendre fourth-order polynomial representation model,the representation accuracies of the FAR and FSSAR models are significantly improved,better representing the flatness defects,like the double tight edge.The optimal number of bottleneck layer neurons in the FAR and FSSAR models is 5,which means that five basic patterns can accurately represent real flatness.Compared with the FAR model,the FSSAR model has higher representation accuracy,although the flatness basic pattern is more abstract,and the physical meaning is not clear enough.Furthermore,the accuracy of the FAR model is slightly lower than that of the FSSAR model.However,it can automatically learn the flatness basic pattern with a very clear physical meaning for both the theoretical and real flatness,which is an optimal intelligent representation method for flatness.

Data-based flatness prediction and optimization in tandem cold rolling

In cold rolling process,the flatness actuator efficiency is the basis of the flatness control system.The precision of flatness is determined by the setpoints of flatness actuators.In the presence of modeling uncertainties and unmodeled nonlinearities in rolling process,it is difficult to obtain efficiency factors and setpoints of flatness actuators accurately.Based on the production data,a method to obtain the flatness actuator efficiency by using partial least square(PLS)combined with orthogonal signal correction(OSC)was adopted.Compared with the experiential method and principal component analysis method,the OSC-PLS method shows superior performance in obtaining the flatness actuator efficiency factors at the last stand.Furthermore,kernel partial least square combined with artificial neural network(KPLS-ANN)was proposed to predict the flatness values and optimize the setpoints of flatness actuators.Compared with KPLS or ANN,KPLS-ANN shows the best predictive ability.The root mean square error,mean absolute error and mean absolute percentage error are 0.51 IU,0.34 IU and 0.09,respectively.After the setpoints of flatness actuators are optimized,KPLS-ANN shows better optimization ability.The result in an average flatness standard deviation is 2.22 IU,while the unoptimized value is 4.10 IU.

On the Flatness and Injectivity of Dual Modules(II)

For a commtative ring R and an injective cogenerator E in the category of R-modules, we characterize QF rings, IF rings and semihereditary rings by using the properties of the dual modules with respect to E.

Theory-Intelligent Dynamic Matrix Model of Flatness Control for Cold Rolled Strips

In order to increase the precision of flatness control, considering the principle and the measured data of rolling process essence, the theory-intelligent dynamic matrix model of flatness control is established by using theory and in-telligent methods synthetically. The network model for rapidly calculating the theory effective matrix is established by the BP network optimized by the particle swarm algorithm. The network model for rapidly calculating the meas- urement effective matrix is established by the RBF network optimized by the cluster algorithm. The flatness control model can track the practical situation of roiling process by on-line selVlearning. The scheme for flatness control quantity calculation is established by combining the theory control matrix and the measurement control matrix. The simulation result indicates that the establishment of theory-intelligent dynamic matrix model of flatness control with stable control process and high precision supplies a new way and method for studying flatness on-line control model.