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The prediction of the mechanical properties of hot-rolled strips is a very complex, highly dimensional and nonlinear problem, and the published models might lack reliability, practicability and generalization. Thus, a new model was proposed for predicting the mechanical properties of hot-rolled strips by deep learning. First, the one-dimensional numerical data were transformed into two-dimensional data for expressing the complex interaction between the influencing factors. Subsequently, a new convolutional network was proposed to establish the prediction model of tensile strength of hot-rolled strips, and an improved inception module was introduced into this network to abstract features from different scales. Many comparative experiments were carried out to find the optimal network structure and its hyperparameters. Finally, the prediction experiments were carried out on different models to evaluate the performance of the new convolutional network, which includes the stepwise regression, ridge regression, support vector machine, random forest, shallow neural network, Bayesian neural network, deep feed-forward network and improved LeNet-5 convolutional neural network. The results show that the proposed convolutional network has better prediction accuracy of the mechanical properties of hot-rolled strips compared with other models.

In order to study the optimal coal pillar width and surrounding rock control mechanism of gob-side entry under inclined seam condition, the 130205 return air entry adjacent to 130203 gob in Yangchangwan No. 1 well is taken as a typical engineering background. By means of engineering background analysis, theoretical analysis based on inside and outside stress field, numerical simulation by FLAC3D software, and in situ industrial test and relevant monitoring methods, the optimal coal pillar width and surrounding rock control technology are obtained. The results show that the influence range of inside stress field is about 12.2∼12.8 m based on theoretical calculation result; under the influence of 10 m coal column, the overall deformation of the roadway is relatively small and within the reasonable range of engineering construction, so the width of the coal pillar along the return air roadway is set to 10 m which is more reasonable; the cross-section characteristics of special-shaped roadway lead to asymmetric stress distribution and fragmentation of surrounding rock, and then the asymmetric surrounding rock control technology under the coupling effect of roof prestressed anchor + high-strength single anchor cable + truss anchor cable support is proposed. The monitoring results of this support method are effective for the maintenance of gob-side entry, and the study conclusions provide new guidance for the surrounding rock control mechanism of gob-side entry under inclined seam conditions.

In order to study the occurrence mechanism of advanced coal pillar instability failure and support crushing accidents during a long-wall face passing across abandoned roadways period, the Panel LW3101 in Shenghua Colliery was taken as a typical engineering background. By means of in situ investigation, physical simulation experiment, theoretical analysis, in situ tests, and relevant monitoring methods, the occurrence mechanism and prevention measures are studied. The results show that the bearing stress in advanced coal pillar is accumulated larger and larger with the width of advanced coal pillar gradually decreasing, and meanwhile, the key stratum in overlying strata would also be sliding instability under some minimum critical width condition; the sudden failure of advanced coal pillar which leads to a sharp increase of bending moment is the key factor that induces fracture of the main roof in front of long-wall face, and the advanced fracture of the main roof can be prevented by changing the stress state of the advanced coal pillar from a two-dimensional state to a three-dimensional state; the method of backfilling abandoned roadway is used to improve the stability of advanced coal pillar and the corresponding monitoring results verify that this method is effective. The research conclusions provide a theoretical foundation and new guidance for preventing advanced coal pillar instability failure and support crushing accidents under a long-wall face passing across abandoned roadways condition.

H. longicornis is used as an experimental animal model for the study of three-host ticks due to its special life cycle and easy maintenance in the laboratory and in its reproduction. The life cycle of H. longicornis goes through a tightly regulated life cycle to adapt to the changing host and environment, and these stages of transition are also accompanied by proteome changes in the body. Here, we used the isobaric tags for a relative and absolute quantification (iTRAQ) technique to systematically describe and analyze the dynamic expression of the protein and the molecular basis of the proteome of H. longicornis in seven differential developmental stages (eggs, unfed larvae, engorged larvae, unfed nymphs, engorged nymphs unfed adults, and engorged adults). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the differentially expressed proteins (DEPs) were used. In our study, A total of 2044 proteins were identified, and their expression profiles were classified at different developmental stages. In addition, it was found that tissue and organ development-related proteins and metabolism-related proteins were involved in different physiological processes throughout the life cycle through the GO and KEGG analysis of DEPs. More importantly, we found that the up-regulated proteins of engorged adult ticks were mainly related to yolk absorption, degradation, and ovarian development-related proteins. The abundance of the cuticle proteins in the unfed stages was significantly higher compared with those of the engorged ticks in the previous stages. We believe that our study has made a significant contribution to the research on H. longicornis, which is an important vector of SFTSV. In this study, we identified changes in the proteome throughout the H. longicornis development, and functional analysis highlighted the important roles of many key proteins in developmental events (ovarian development, the molting process, the development of midgut, the development and degeneration of salivary glands, etc.). The revelation of this data will provide a reference proteome for future research on tick functional proteins and candidate targets for elucidating H. longicornis development and developing new tick control strategies.

To reveal the generation mechanism of the quarter buckle in the process of hot-rolled temper rolling, the elastic–plastic finite element method is used to calculate the deformation of the roll and the strip during the temper rolling. The change of the cross section of the strip and the distribution of the longitudinal stress are analysed under different bending forces to obtain the boundary conditions of the quarter buckle. The generation of the quarter buckle is further analysed from the bending and contact flattening of the roll system and the elastic recovery of the strip after rolling. We found that the quarter buckle is closely related to the high-order distribution of the contact flattening between the work roll and the strip and is less affected by the bending deformation of the roll and the contact flattening between the rolls. Finally, a new work roll contour of the locally variable crown is proposed to change the distribution of contact flattening between the work roll and the strip. It is verified through theoretical calculations and industrial applications that the new contour can effectively improve the quarter buckle.

A mathematical model of friction coefficient was established to calculate the roll force during hot strip rolling process. Firstly, a linear regression model with four input variables was proposed to describe friction behavior, namely rolling speed, strip temperature, reduction rate, and the number of rolled strips since roll change. Then, the method of principal component regression, which can eliminate the effect of multi-collinearity among the input variables, is used to build the friction model. The obtained model was tested by statistical functions, and the results indicated that the model was valid and showed as well that the cumulative contribution rate of the first two principal components of the regression model was as high as 98% and the first two principal components contained almost all the information of original input variables. The industrial experiment confirmed that the roll force model with the proposed friction model had the higher prediction accuracy than the Sims model, and the proposed model could be used in online calculation for hot-rolled roll force.

In order to obtain good geometry quality in the wire and arc additive manufacture, it is important to select the appropriate process parameters. Firstly, based on the 4-factor and 5-level experiments, the multi-objective mathematical model of process parameters and geometry quality is established by response surface methodology. Secondly, an adaptive grey wolf algorithm for solving multi-objective problems is proposed. The algorithm introduces external Archive, adaptive hunting mechanism, and fusion polynomial mutation mechanism to improve the search ability of the grey wolf algorithm. Experiments show that the Pareto set obtained by the adaptive multi-objective grey wolf algorithm is more diverse and convergent than the other five well-known algorithms. Meanwhile, in order to obtain the desired geometry quality, the TOPSIS algorithm is used to analyze the Pareto set obtained to get the optimal process parameters.

The rolling force model is the basis for reasonable selection of rolling equipment and optimization of rolling process, and the establishment of an accurate mathematical model as well as doing the corresponding parameter analysis has been the focus of research in this field for many years. Different modeling methods of the rolling force and their research progress were introduced, the main methods of which are the theoretical analysis, the finite element simulation, the artificial neural network modeling, the hybrid modeling of theory and neural network, as well as the hybrid modeling of finite element and neural network. Meanwhile, the application examples of rolling force models in thickness control, strip crown control, and schedule optimization were presented, and an outlook on the new directions of future development was made, including establishing new type of hybrid models, solving the black box problem, and realizing the multi-objective optimization accounting for some special requirements.

During recent decades, evolutionary algorithms have been widely studied in optimization problems. The multi-objective whale optimization algorithm based on multi-leader guiding is proposed in this paper, which attempts to offer a proper framework to apply whale optimization algorithm and other swarm intelligence algorithms to solving multi-objective optimization problems. The proposed algorithm adopts several improvements to enhance optimization performance. First, search agents are classified into leadership set and ordinary set by grid mechanism, and multiple leadership solutions guide the population to search the sparse spaces to achieve more homogeneous exploration in per iteration. Second, the differential evolution and whale optimization algorithm are employed to generate the offspring for the leadership and ordinary solutions, respectively. In addition, a novel opposition-based learning strategy is developed to improve the distribution of the initial population. The performance of the proposed algorithm is verified in contrast to 10 classic or state-of-the-arts algorithms on 20 bi-objective and tri-objective unconstrained problems, and experimental results demonstrate the competitive advantages in optimization quality and convergence speed. Moreover, it is tested on load distribution of hot rolling, and the result proves its good performance in real-world applications. Thus, all of the aforementioned experiments have indicated that the proposed algorithm is comparatively effective and efficient.

A model of deformation resistance during hot strip rolling was established based on generalized additive model. Firstly, a data modeling method based on generalized additive model was given. It included the selection of dependent variable and independent variables of the model, the link function of dependent variable and smoothing functional form of each independent variable, estimating process of the link function and smooth functions, and the last model modification. Then, the practical modeling test was carried out based on a large amount of hot rolling process data. An integrated variable was proposed to reflect the effects of different chemical compositions such as carbon, silicon, manganese, nickel, chromium, niobium, etc. The integrated chemical composition, strain, strain rate and rolling temperature were selected as independent variables and the cubic spline as the smooth function for them. The modeling process of deformation resistance was realized by SAS software, and the influence curves of the independent variables on deformation resistance were obtained by local scoring algorithm. Some interesting phenomena were found, for example, there is a critical value of strain rate, and the deformation resistance increases before this value and then decreases. The results confirm that the new model has higher prediction accuracy than traditional ones and is suitable for carbon steel, microalloyed steel, alloyed steel and other steel grades.