Under the background of dual carbon policy as well as energy conservation, blanks with variable thicknesses (BVTs) which act as structural components have drawn extensive attention due to their excellent strength and formability and reasonable load-bearing distribution characteristics, particularly in the field of automotive manufacturing. With these advantages, the manufacturing technologies of these plates using more efficient rolling methods have thus emerged. This article summarizes four methods and their characteristics for manufacturing plates with variable thicknesses based on rolling technology. In addition, a review is conducted on the latest research progress of the metal flow and rolling theories of existing plates with different thicknesses in the longitudinal and transverse direction.

The goal of this study is to develop an efficient machine learning framework for designing high-hardness multi-metal nitride coatings, overcoming the limitations of traditional trial-and-error methods. The development of multicomponent metal nitride hard coatings via multi-arc ion plating remains a significant challenge due to the vast compositional search space. Although theoretical studies in macroscopic, mesoscopic, and microscopic domains exist, these often focus on idealized models and lack effective coupling across scales, leading to time-consuming and labor-intensive traditional methods. With advancements in materials genomics and data mining, machine learning has become a powerful tool in material discovery. In this work, we construct a compositional search space for multicomponent nitrides based on electronic configuration, valence electron count, electronegativity, and oxidation states of metal elements in unary nitrides. The search space is further constrained by FCC crystal structure and hardness theory. By incorporating a feature library with micro-, meso-, and macro-structural characteristics and using clustering analysis with theoretical intermediate variables, the model enriches dataset information and enhances predictive accuracy by reducing experimental errors. This model is successfully applied to design multicomponent metal nitride coatings using a literature-derived database of 233 entries. Experimental validation confirms the model’s predictions, and clustering is used to minimize experimental and data errors, yielding a strong agreement between predicted optimal molar ratios of metal elements and nitrogen and measured hardness performance. Of the 100 Vickers hardness (HV) predictions made by the model using input features like molar ratios of metal elements (e.g., Ti, Al, Cr, Zr) and atomic size mismatch, 82 exceeded the dataset’s maximum hardness, with the best sample achieving a prediction accuracy of 91.6% validated against experimental measurements. This approach offers a robust strategy for designing high-performance coatings with optimized hardness.

A mathematical model was intricately devised to explore the influence of continuous variations in thickness and mechanical properties on the performance of tailor rolled blanks (TRB) and tailor rolled tubes (TRT). Through the integration of analytical and numerical techniques, it was discerned that these variations play a pivotal role in modulating stress distribution and strain localization, thereby inducing a spectrum of plastic instability behaviors within the structures. The introduction of an ‘equivalent strength’ metric as a novel means to quantify structural performance shed light on strategic material distribution to enhance durability and mechanical efficiency. Moreover, the insights garnered from this research deepen the understanding of the mechanical responses of tailor-rolled constructs under varying loads, offering valuable perspectives for the development and fabrication of engineered materials with bespoke properties. This study not only contributes to bridging a knowledge gap in the realm of tailored material engineering but also fosters the advancement of design methodologies in the construction of high-performance engineered structures.