EXTENDED ABSTRACT: Digitalization and intelligentization have become common global themes, occupying the forefront of digital intelligent manufacturing in the manufacturing industry and leading the way to become a powerful country in the digital intelligence ffeld. Customization, diversification, small batch production, high quality, and rapid response are the new demands raised by customers. The unstable product quality, low efffciency, high cost, and difffcult collaboration have been long-standing pain points for enterprises, while the lack of deep material research and weak iteration and originality capabilities also pose challenges.The research and development of the ffrst generation of aluminum alloys began in 1906, and China started in 1950. Until now, the research and development of key grades of aluminum alloys in China is still in the state of imitation, resulting in insufffcient understanding of the evolution mechanism of composition-processstructure-performance, making it difffcult to achieve independent iteration and upgrading and originality. At present, high-end aluminum alloys are developing from the ffrst generation of static strength to the fffth generation of multi-objective matching comprehensive service performance, and the difffculty of research and imitation is increasing, while some grades and technologies have been in the state of no target for research and imitation.The development of digitalization and intelligence brings an opportunity to solve the above problems. China Aluminum Corporation (Chinalco), based on the idea of material genetic engineering, proposes to build a digital research and development platform for aluminum alloy materials, aiming to establish a material research and development navigation system and create a calculation and data-driven R&D mode. It solves the problem of unknowable and uncontrollable data in material R&D, pilot test and production process, and realizes the reproducible, iterative and original material R&D, pilot test and production process. In terms of industrial big data, due to the multiple production processes in aluminum processing enterprises, data is scattered across various subsystems, making interfactory collaboration difffcult and data sources heterogeneous, resulting in data isolation. Through the material gene big data technology, the data of the production process of the enterprise is collected, cleaned, recognized, connected and analyzed, and the "component-processstructure-performance" database of the whole process of key grades of alloy materials is established, realizing the traceability of the data of the production process of aluminum alloy materials. The life cycle carbon footprint evaluation model and calculation software of alumina, electrolytic aluminum, aluminum processing and product application are developed. In view of the needs of OEMs for the performance of aluminum alloy materials, application technology, and material selection of aluminum alloy vehicle design, the data system of automotive lightweight aluminum alloy grades, material basic performance and service performance is developed. Through the lightweight body, parts model and case demonstration, the EVI material selection system from material to vehicle and vehicle to material is completed. Based on the cross-scale calculation method combined with material characterization means, the relationship model between cold rolling texture and mechanical properties of automotive plates and paint brush line is established, and the regulation model of texture evolution in cold rolling and static recrystallization process is developed, forming a prediction expert system for cold rolling plates, processes and texture properties. Based on high-throughput calculation and experimental testing, a residual stress prediction system for aluminum alloy plates was developed, which realized the rapid regulation of residual stress.The digitization and intelligence of manufacturing industry is still in the initial stage, and the road of research, application and development is still long. However, the trend of digitization and intelligentization remains unchanged. It is necessary to ensure the rapid development of digitization and intelligence technology from the level of mechanism and methodology, so as to help achieve the "high-end manufacturing" of key materials.

Keywords：material genetic engineering; high-level forum; aluminum alloy; industrial big data;