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A novel dynamic rolling force modified model driven by data-mechanism cooperation is proposed, which improves the prediction accuracy and rolling process stability. Firstly, the single-stand strip dynamic rolling force is predicted by traditional mechanism model and modified model respectively. The modified model has a greater probability of making the prediction error closer to zero, and the prediction root mean squared error of the modified model is lower than that of the mechanism model. Subsequently, targeting the strip dynamic rolling force with strength grade M3A30, the prediction effect of the modified model with strength grades M3A33 is better than that of the modified model with strength grades 1CD61. Finally, to reduce dynamic rolling force fluctuations during tandem rolling process, the rolling schedule is optimized before production. The maximum dynamic rolling force difference of S1, S2, S3, and S4 stands are reduced by 24.372 %, 29.071 %, 10.616 %, and 7.447 % respectively.

Hot rolled zirconium alloy strip has a feature of multi-schedule and multi-pass, which undergoes the complex working condition of repeatedly manual experiences. Rolling schedule is a key technology that directly influences strip product quality, which is affected by many factors such as mechanical properties, temperature, strain rate, and capacity of mill. The multi-objective optimization model for hot rolling schedule of zirconium alloy strip is presented by the improve particle swarm optimization algorithm (IPSO), rolling force ratio distribution, and good strip shape as the objective functions. Meanwhile, based on the penalty function method transforming the constraint problem into the unconstrained problem, we used on-line applications for optimized rolling schedule and the comparison provides an effective path as a reference of practical control technology.

In the process of cold tandem rolling, chatter instability leads to serious impacts on enhancing rolling speed, improving product quality, reducing production cost and realizing intellectualization. Chatter occurs with the rolling speed up to a certain threshold value, but the critical speed is determined by both product specifications and rolling schedules. A 5-stand cold tandem rolling mill whose first three stands and subsequent two stands, respectively, have four and six rolls was investigated by formulating its dynamic equations with the corresponding structure–process coupling. By applying the stability-based calculation model about the critical rolling speed in each stand, the system dynamic responses around the critical rolling speed were simulated, and the system eigenvalues which represent instability and characteristic frequencies were figured out. Thereafter, via combining the critical rolling speeds with the system dynamic behavior, a dynamics-based optimization model of rolling schedule for the 5-stand cold tandem system was proposed for the purposes of both the chatter suppression and rolling speed increase. In the optimization model, eight rolling technique parameters (four strip thicknesses and four tensions between the upstream and downstream stands) were taken as design variables, and the constraint conditions were set as no chatter instability in all five stands, and the optimization goal was to maximize the outlet speed of the final stand. The pattern search method was introduced to solve the optimization model. By applying such a dynamics-based optimization model for the 5-stand cold tandem rolling process, the chatter instability was suppressed effectively and the rolling efficiency was improved considerably; therefore, such an optimization model is expected to be valuable for intelligent manufacturing of rolling process.

Aiming at the problem of load distribution during multi-pass cold rolling of nuclear zirconium alloy strip, the load distribution model with good plate shape is established by the self-adaptive particle swarm optimization (SAPSO) algorithm, considering the main constraint conditions including rolling force, reduction, and torque in cold rolling process. Based on the penalty function method transforming the constraint problem into the unconstrained problem, the particle swarm optimization algorithm (PSO) combined with self-adaptive inertia weight factor optimized the load distribution model is developed to improve the local search ability of the particle swarm optimization algorithm. Compared with the original nuclear zirconium alloy cold rolling schedule, the simulation results of load distribution based on the SAPSO algorithm can keep good plate shape in multi-pass cold rolling process with the high prediction accuracy. The industrial experiments demonstrate that the proportional crown difference value is consistent, and the plate shape flatness is good.

In hot strip rolling process, rolling schedule is a key technology which directly influences strip product quality. Rolling schedule optimization is actually a problem of load distribution. To make a better rule of the load distribution of aluminum hot tandem rolling, multi-objective optimization algorithm is used to optimize rolling schedule. Preventing slipping, power margin and minimum energy consumption are selected as the optimization objectives. To make a precision calculation of rolling schedule, an adaptive neural network which is based on classification system is applied to improve the prediction ability for the rolling force, and its on-line training system reduces the prediction errors caused by different rolling conditions. The improved differential evolution algorithm is used to search the Pareto front, and it obtains a good approximation of the Pareto-front and decreases computation time. Load distribution strategies focused on different objectives are generated from the Pareto front to meet the requirements of industrial spots. The experiment result shows the algorithm covers the front quickly and distributes well. Comparing with the original schedule, the proposed method reduces the probability of slippage and energy consumption.

Processing parameters have direct impacts on the quality of the steels produced. This is particularly true for microalloyed steels, the production of which involves a thermomechanical controlled rolling process, which combines multi-pass hot rolling with accelerated cooling. On one hand, hot rolling may finish below A3 temperature when austenite starts to transform to ferrite. On the other hand, controlled cooling is applied to obtain the desired microstructure from austenite decomposition. To optimise the TMCP parameters of such alloys, not only a clear understanding of each metallurgical phenomenon involved is required, but also the interactions among them. This paper reports our recent work on modelling of microstructural evolution and deformation resistance during multi-pass hot rolling of steels. The model considers the following metallurgical phenomena as well as their interactions: - Precipitation of MX type carbides, nitrides or carbonitrides. - Interactions between precipitation and recrystallisation and their effects on grain refinement. - Effect of grain size and cooling path on transformations from austenite to ferrite, pearlite, bainite and martensite. - Effect of rolling parameters, recrystallisation and microstructure on the deformation resistance of the alloy. The model predicts the evolution of microstructural features such as precipitate size and amount, recrystallisation fraction and effective strain, grain size, and austenite decomposition, as well as the alloy’s deformation resistance during hot rolling. It has been applied to a wide range of steels and demonstrated good agreement with experimental observations. Therefore, it has the great potential to be implemented in a production line to help optimise the rolling schedule for both C-Mn and microalloyed steels.

Purpose The purpose of this study is to improve the global optimization ability of the Tabu search (TS) algorithm, and then improve the calculation efficiency and accuracy of rolling schedule in tandem cold rolling. Design/methodology/approach A case-based reasoning–Tabu search hybrid algorithm (CBRTS) has been presented. First, the case-based reasoning technology was adopted to obtain high-quality initial solution and then the TS algorithm was used for global optimization. Findings The optimization effect of CBRTS is compared with that of the traditional TS algorithm, and the analysis result indicates that the CBRTS has a faster convergence rate than TS, and the optimization results are closer to the global optimal. Meanwhile, the rolling schedule calculated by CBRTS is more reasonable, which can increase the production efficiency while giving full play to the capacity of equipment. Originality/value A CBRTS hybrid algorithm is presented. The strong dependence of the TS algorithm on the initial solution has been solved. The rolling schedule multi-objective optimization functions are established. The proposed algorithm is applied in a 1,450-mm tandem cold rolling production line. The improved method can reduce about half the iterations compared with the traditional one.

For high precision profile and flatness control ability of electrical steel strip with different width during schedule-free rolling (SFR) campaign, the asymmetry self-compensating work roll contour, which is suitable for all strip widths (ASR-C) is developed. The effect of strip width on work roll wear is analyzed through experimental data. By combining shifting strategy and initial shifting position, the new asymmetry self-compensating work roll (ASR) contour is designed. Compared with the ASR work roll for narrow strip (ASR-Y), ASR work roll for wide strip (ASR-N), and conventional work roll, ASR-C presents well profile and flatness control ability for all strip widths during entire rolling campaign. The characteristics of ASR work roll can be fully reflected when ASR-C and its shifting strategy are used. Experimental results show that the ratio of strip crown (C40) less than 45 μm is increased from 41.8 to 98.2% by using the ASR-C work roll of the ASR mill type, compared with conventional work roll of the K-WRS (Kawasaki-Work Roll Shifting) mill type.

Minimizing the maximum load on rolls in ring rolling process has been the most urgent demand in producing large rings made of high-strength material. With a view to meet the essential demand, the problem was solved by a novel process design, which respectively calculated the feed-rates of mandrel and axial rolls through optimum design. Based on the finite element simulation of ring rolling process by varying the feed-rates of the mandrel and axial rolls, an improved rolling schedule was established. The design of experiments via Taguchi method and the optimum design method such as Conjugate Gradient Method were used. The FE simulation was verified by comparison with experiment results. The optimized rolling schedule was suitable and reliable in the practical manufacture for both pure and radial-axial ring rolling process.

Rolling schedule not only determines the rolling process to be going smoothly, but also affects the shape accuracy and structure properties of finished strip. In order to gain good strip crown and flatness, the calculation formulas of the most suitable rolling force and bending force are deduced. By taking relatively equal load of rolling power and good shape as objective functions, the optimization mathematical models of finish rolling schedule are established. By contrast, the rolling schedules after optimization can improve the rolling mill working status and ensure the strip crown and flatness to be good. At the same time, the setting value of bending force is improved and this leaves more space for on-line shape control.