Optimization-based automatic flatness control in cold tandem rolling

For the problem of automatic flatness control (AFC) in cold tandem mills this paper proposes control techniques based on quadratic optimization and delay compensation. Three different strategies are presented and compared: a centralized solution based on a global quadratic programming (QP) problem that decides the commands to all the actuators, and two decentralized solutions where each actuator command is optimized locally. All schemes are based on a global exchange of information about the commands generated at the previous time step at each stand to compensate for the numerous delays present in the mill. Control algorithms are tested in simulation considering a tandem mill with five stands as a benchmark, and results are shown to demonstrate the performance of the proposed schemes.     

Observer-based multivariable flatness control of a cold rolling mill

The synthesis methodology developed by Kimura (1985) based on the design theory of output regulators essentially due to Wonham (1974) has been applied successfully to the flatness control system for a 6-high cold rolling mill. The system has the following remarkable features.

1. (1) The structure of the controller is simple. This makes it easy to tune the control system.

2. (2) The controller copes well with the detection time delay, and thus high performance is obtained even at a low rolling speed.

3. (3) The flatness error caused by the rolling force variation in mill acceleration and deceleration time would be kept to a minimum by the function to adjust roll bending force using the signal of rolling force.

RBF神经网络的板形预测控制

由于板带轧制的环境十分复杂,如温度的变化是无法避免的干扰,以及HC轧机液压弯辊系统的非线性和不确定性,使得按传统理论建立的模型和控制方法都难以达到理想的效果.针对这一问题,提出了一种基于径向基函数(RBF)神经网络的模型预测控制方案应用于带材控制中,以提高带材的成材率,充分发挥液压弯辊力对板形的调整作用,改善轧机系统的动态特性.仿真结果表明了该控制系统的性能良好,有较强的抗干扰能力和较好的鲁棒性和快速性.     

基于遗传小波神经网络的冷轧轧制力预报研究

依据小波函数的非线性逼近能力和神经网络的自学习特性，提出一种适合高维输入的小波神经网络建模方法，这种网络结构类似于多层感知器，不同的是隐层神经元的激励函数为小波基函数．为使小波神经网络具有更高的学习精度和更快的收敛速度，将遗传算法、小波神经网络和梯度下降算子结合起来，提出一种遗传小波神经网络．将该网络应用于冷轧轧制力的预报，仿真结果表明预报精度大为提高．     

基于工况识别的辊式淬火过程板形预报方法

板形是衡量淬火后钢板质量的重要指标之一,板形的预报对高质量钢板的持续稳定生产具有重要的指导意义.本文提出一种基于工况识别的辊式淬火过程板形预报方法,为淬火生产控制决策提供参考依据.首先对淬火过程进行特性分析;然后采用模糊C均值聚类算法对淬火过程进行工况识别,使用支持向量机建立各工况的板形预报模型,并运用改进的粒子群优化...     

Tracking control for boundary controlled parabolic PDEs with varying parameters: Combining backstepping and differential flatness

The combination of backstepping-based state-feedback control and flatness-based trajectory planning and feedforward control is considered for the design of an exponentially stabilizing tracking controller for a linear diffusion-convection-reaction system with spatially and temporally varying parameters and nonlinear boundary input. For this, in a first step the backstepping transformation is utilized to determine a state-feedback controller, which transforms the original distributed-parameter system into an appropriately chosen exponentially stable distributed-parameter target system of a significantly simpler structure. In a second step, the flatness property of the target system is exploited in order to determine the feedforward controller, which allows us to realize the tracking of suitably prescribed trajectories for the system output. This results in a systematic procedure for the design of an exponentially stabilizing tracking controller for the considered general linear diffusion-convection-reaction system with varying parameters, whose applicability and tracking performance is evaluated in simulation studies.     

Visual analysis of a cold rolling process using a dimensionality reduction approach

The rolling process is a strategical industrial and economical activity that has a large impact among world-wide commercial markets. Typical operating conditions during the rolling process involve extreme mechanical situations, including large values of forces and tensions. In some cases, these scenarios can lead to several kinds of faults, which might result in large economic losses. Thereby, a proper assessment of the process condition is a key aspect, not only as a fault detection mechanism, but also as an economic saving system. In the rolling process, a remarkable kind of fault is the so-called chatter, a sudden powerful vibration that affects the quality of the rolled material. In this paper, we propose a visual approach for the analysis of the rolling process. According to physical principles, we characterize the exit thickness and the rolling forces by means of a large dimensional feature vector, that contains the energies at specific frequency bands. Afterwards, we use a dimensionality reduction technique, called t-SNE, to project all feature vectors on a visual 2D map that describes the vibrational states of the process. The proposed methodology provides a way for an exploratory analysis of the dynamic behaviors in the rolling process and allows to find relationships between these behaviors and the chatter fault. Experimental results from real data of a cold rolling mill are described, showing the application of the proposed approach.     

可延迟供货的冷轧生产库存问题之建模与优化

本文针对可延迟供货的冷轧生产系统,建立了以最小化库存成本、拖期惩罚和启动成本为目标的多阶段生产库存模型,模型中充分考虑了工序不允许停机的情况以及计划与调度之间的一致性问题.同时开发了基于变量分离的有效拉格朗日松弛求解算法,并使用120个基于实际生产数据的算例进行了仿真实验,计算结果显示该算法能够在合理的时间内得到高质量的解.     

Data mining-based hierarchical transaction model for multi-level consistency management in large-scale replicated databases

Scalability and availability in a large-scale distributed database is determined by the consistency strategies used by the transactions. Most of the big data applications demand consistency and availability at the same time. However, a suitable transaction model that handles the trade-obetween availability and consistency is presently lacking. In this article, we have proposed a hierarchical transaction model that supports multiple consistency levels for data items in a large-scale replicated database. The data items have been classified into different categories based on their consistency requirement, computed using a data mining algorithm. Thereafter, these have been mapped to the appropriate consistency level in the hierarchy. This allows parallel execution of several transactions belonging to each level. The topmost level called the Serializable (SR) level follows strong consistency applicable to data items that are mostly read and updated both. The next level of consistency, Snapshot Isolation (SI), maps to data items which are mostly read and demand unblocking read. Data items which are mostly updated do not follow strict consistent snapshot and have been mapped to the next lower level called Non- monotonic Snapshot Isolation (NMSI). The lowest level in the hierarchy correspond to data items for which ordering of operations does not matter. This level is called the Asynchronous (ASYNC) level. We have tested the proposed transaction model with two different workloads on a test-bed designed following the TPC-C benchmark schema. The performance of the proposed model has been evaluated against other transaction models that support single consistency policy. The proposed model has shown promising results in terms of transaction throughput, commit rate and average latency.     

Qualitative behavior rules for the cold rolling process extracted from trained ANN via the FCANN method

Nowadays, artificial neural networks (ANN) are being widely used in the representation of different systems and physics processes. In this paper, a neural representation of the cold rolling process will be considered. In general, once trained, the networks are capable of dealing with operational conditions not seen during the training process, keeping acceptable errors in their responses. However, humans cannot assimilate the knowledge kept by those networks, since such knowledge is implicit and difficult to be extracted. For this reason, the neural networks are considered a “black-box”.In this work, the FCANN method based on formal concept analysis (FCA) is being used in order to extract and represent knowledge from previously trained ANN. The new FCANN approach permits to obtain a non-redundant canonical base with minimum implications, which qualitatively describes the process. The approach can be used to understand the relationship among the process parameters through implication rules in different operational conditions on the load-curve of the cold rolling process. Metrics for evaluation of the rules extraction process are also proposed, which permit a better analysis of the results obtained.     

Continuum damage mechanics analysis of strip tearing in a tandem cold rolling process

Strip tearing during tandem cold rolling is one of the manufacturing issues in the rolling industry that can significantly increase production costs. In this paper, an explicit finite element code coupled with the improved Lemaitre damage model is developed to predict strip tearing in a five-stand tandem rolling mill. The simulation results are validated using experimental data from an industrial rolling mill, and there is good agreement between the numerical and experimental results. Some factors related to strip tearing, such as friction coefficient variations, thickness difference between two welded strips and reduction schedule, are introduced, and their influence on the damage evolution through the strip during the process is investigated by using damage-coupled finite element simulations.     

Toward a heuristic optimum design of rolling schedules for tandem cold rolling mills

Scheduling for tandem cold mills refers to the determination of inter-stand gauges, tensions and speeds of a specified product. Optimal schedules should result in maximized throughput and minimized operating cost. This paper presents a genetic algorithm based optimization procedure for the scheduling of tandem cold rolling mills. The optimization procedure initiates searching from a logical staring point — an empirical rolling schedule — and ends with an optimum cost. Cost functions are constructed to heuristically direct the genetic algorithm’s searching, based on the consideration of power distribution, tension, strip flatness and rolling constraints. Numerical experiments have shown that the proposed method is more promising than those based on semi-empirical formulae. The results generated from a case study show that the proposed approach could significantly improve empirically derived settings for the tandem cold rolling mills.     

Real-time monitoring for a process with multiple operating modes

The real-time monitoring of a chemical process with multiple operating modes is a challenging problem. The frequent changes of operating modes require frequent updates of the monitoring models, which lead to frequent pauses in the real-time monitoring activities. This paper proposes a monitoring methodology for a process with multiple operating modes, based on hierarchical clustering and a super PCA model. The case studies show that the super PCA model performs better than a single PCA model for all operating modes, or local PCA models developed for each operating mode.     

Research on the expanding deformation of ring radius in cold profiled ring rolling process

Cold profiled ring rolling is an advanced local plastic deformation technology widely used to precisely manufacture various complex seamless ring parts.It plays an important role in many industrial fields such as aeronautics,astronautics,automobile,bearings,etc.In the cold ring rolling process,the ring radius expands gradually with the increase of the feeding amount(i.e.rolling reduction),and the expanding deformation of the ring radius is closely related to the rolling reduction.It is very important to inv...     

冷轧薄板设定计算模型与仿真研究

研究冷轧薄板计算机系统的设定计算模型和计算方法,采集了大量实际生产数据,研究了这些模式对各个钢种的最优稳态轧制数据,基于上述结果,优化设定计算模型的参数,控制实际计算精度。并设计了重要模型的计算仿真程序,通过仿真找出现有模型的优缺点,为以后设定计算的最优化提供理论基础。现场实验表明,仿真系统实验结果应用到实际生产中使产品厚度精度得到很大提高。     

改进权重自适应GA及冷连轧轧制规程多目标优化

针对聚合多目标优化方法的权重难以确定的问题,提出了一种改进的权重自适应方法,并以遗传算法为基础对冷连轧轧制规程进行多目标优化.首先,结合某冷轧厂实际的轧制规程优化过程,选取等功率裕量、轧制能耗及带钢打滑概率作为优化目标,建立了冷连轧轧制规程的多目标优化模型.然后将改进的权重自适应遗传算法(GA)应用于不同规格的带钢轧制规程多目标优化中,结果表明,与实际应用的轧制规程相比,该方法有效的降低了3个目标函数的值;与权重自适应GA相比,改进的权重自适应GA的针对性更强,同时重要性高的目标收敛速度更快.     

Evaluation using online support-vector-machines and fuzzy reasoning. Application to condition monitoring of speeds rolling process

A method for process condition monitoring and evaluation, which combines the online support vector machine (SVM) regression and the fuzzy sets methods, is proposed. To account for the time dependence, the proposed approach is based on moving windows in order to take into account the past and new data for the model’s adaptation. The fuzzy analysis is then applied to the generated residual data to give an evaluation of the condition monitoring. The proposed approach is applied to hot rolling for constructing a complementary condition monitoring system, which permits an online quality evaluation in the rolling process. Simulation results based on residual data show that the new approach is easily implementable.