With the development of industry 4.0, which has spurred the transformation of traditional manufacturing into the modern industrial internet-of-things. The most notable feature during this transition is the improvement of digitization and intelligence based on the massive data drives. In such a data-driven environment, the processing, storage and utilization of the industry data get more and more important. Usually, the traditional data processing architecture runs as a one-way streamline, which cannot adapt to the different requirements of the multi-scenarios application. This paper proposed a new industrial big data processing architecture called Phi architecture, which can realize many functions such as batch data processing and stream data processing, distributed storage and access, and real-time control. Compared with other data processing architecture, the Phi architecture combined with edge computing and feedback control, which has the ability to deal with the different demands in aviation manufacturing. Next, the new architecture is designed of Microservices pattern, which improves the flexibility and stability of the architecture, and makes it independent operated in multi-scenarios, such as state monitoring of workshop, adaptive data acquisition, feedback control and user-oriented information classification. As a proof of concept, the architecture has been tested in a simulation digital manufacturing workshop. The results verify the improved effectiveness of the Phi architecture on the data feedback control and real-time processing. And the development of Microservices architecture greatly improves the efficiency, adaptability and extendibility of the manufacturing process.

This paper develops a material-structure integrated design and optimization method based on a multiscale approach for the lightweight design of CFRP car doors. Initially, parametric modeling of RVE is implemented, and their elastic performance parameters are predicted using the homogenization theory based on thermal stress, exploring the impact of RVE parameters on composite material performance. Subsequently, a finite element model of the CFRP car door is constructed based on the principle of equal stiffness, and a parameter transfer across microscale, mesoscale, and macroscale levels is achieved through Python programming. Finally, the particle generation and updating strategies in the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm are improved, enabling the algorithm to directly solve multi-constraint and multi-objective optimization problems that include various composite material layup process constraints. Case study results demonstrate that under layup process constraints and car door stiffness requirements, plain weave, twill weave, and satin weave composite car doors achieve weight reductions of 15.85%, 14.54%, and 15.35%, respectively, compared to traditional metal doors, fulfilling the requirements for a lightweight design. This also provides guidance for the lightweight design of other vehicle body components.