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Laminated metal composites have attracted more attention due to their ultra-high strength, fracture toughness and elongation, full considerations of each component, and coordinated deformation effect between interfaces. Herein, Zn and Al were selected as the component materials to prepare Al/Zn laminated composite plates using hot rolling and low temperature annealing, and studied the influence of the thickness of the diffusion layer at the interface to understand the reason for the strengthening and toughening mechanism. The results show that, with an increase in annealing temperature and time, the grain will recover and recrystallize to grow. A diffusion layer with a certain thickness formed at the interface due to the mutual diffusion of Al and Zn atoms at the interface. As annealing time and temperature increase, the yield and tensile strengths first increased and then decreased. The tensile fracture morphology showeds many deep dimples. In the rolling state, the micro-hardness value at the interface was between Al and Zn.

Customized small batch orders and sustainable development requirements pose challenges for product quality control and manufacturing process optimization for steel production. Building a multi-quality objective process parameter optimization method that converts the original single target optimization into multi-objective interval capability optimization has become a new method to ensure product quality qualification rate and reduce production costs. Aiming at the multi-quality objective control problem of plate products, we proposed a novel multi-objective process parameter interval optimization model (MPPIO) with equipment process control capability and parameter sensitive analysis. The multi-output support vector regression method was used to establish a multi-quality objective prediction model, which was settled as a verification model for the process parameter optimization results based on the particle swarm optimization algorithm (PSO). The process control capability functions of key parameters were fitted based on the real data in production. With these functions, each optimized particle of the classical PSO was converted into the particle beam of the MIPPO. The iteration process was weight controlled by calculating the Morris sensitivity between each input parameter and output index in the multi-quality objective prediction model, and finally the processing control window of each key parameter was determined according to the process parameter optimization results. The experimental results show that the MPPIO model can obtain the optimal parameter optimization results with the maximum processing capacity and meet the customized processing range requirements. The MPPIO model can reduce the difficulty of control and save production costs while ensuring the product properties is qualified.

Hot plastic working behavior and microstructure evolution were investigated during a production process of four medium-Mn steels, which differed in Mn (3 and 5%) and Nb contents. The production process started with casting, followed by hot forging, rough hot-rolling and concluded with final thermomechanical processing, which was performed to obtain multiphase bainite-based alloys with some fractions of retained austenite. The rough rolling was composed of four passes with total true strain of 0.99 and finishing rolling temperature of 850 °C, whereas thermomechanical processing contained five passes and total true strain of 0.95 at a finishing rolling temperature of 750 °C. During the process, the force parameters were recorded, which showed that the rolling forces for steels containing 3% Mn are higher compared to the 5% Mn alloys. There was no significant influence of Nb on the rolling parameters. The produced as-cast microstructures were composed of dendritic bainitic-martensitic phases. A positive effect of Nb micro-addition on a refinement of the as-cast structure was noticed. The thermomechanical processed steels showed fine multiphase microstructures with some fractions of retained austenite, the fraction of which depended on the Mn content in steel. The steels containing 3% Mn generated higher forces both during rough and thermomechanical rolling, which is related to slower recrystallization softening in these alloys compared to the steels containing 5% Mn.

Entry temperature estimation is a major concern for finishing mill set-up in hot strip mills. Variations in the incoming bar conditions, frequent product changes and measurement uncertainties may cause erroneous estimation, and hence, an incorrect mill set-up causing a faulty bar head-end. In earlier works, several varieties of neuro-fuzzy systems have been tested due to their adaptation capabilities. In order to test the combination of the simplicity offered by Takagi–Sugeno–Kang systems (also known as Sugeno systems) and the modeling power of type-2 fuzzy, in this work, hybrid-learning type-2 Sugeno fuzzy systems are evaluated and compared with the results presented earlier. Systems with both empirically and fuzzy c-means-generated rules as well as purely fuzzy systems and grey-box models are tested. Experimental data were collected from a real-life mill; datasets for rule-generation, training, and validation were randomly drawn. Two of the grey-box models presented here reach 100% of bars with 20 °C or less prediction error, while two of the purely fuzzy systems improved performance with respect to purely fuzzy systems presented elsewhere, however it was only a slight improvement.