Abstract
In this paper, the simulation and key link characterization of the complex assembly model step‐down process are studied and analysed in depth using the digital twin approach, and the method is used in the practical process. The physical model step‐down method MORA algorithm and its physical interpretation in various simplified cases are given, and the MORA method is improved on this basis. The concept of local activeness based on knot structure is introduced, and the process of model transformation and downscaling and decomposition based on local activeness is explained in detail. The high‐fidelity mapping of solid equipment is completed in virtual space, which can accurately reproduce and predict the health state of engineering equipment throughout its life cycle, effectively avoiding the huge property losses and safety risks caused by early failure of vulnerable structures and providing a safe and stable working environment for offshore oil and gas production. With the prototype monitoring data as reference, the response surface method is used to identify the parameters of the finite element model of the hinge node, which improves the fidelity of the virtual model of the hinge node. Considering the friction coefficient changes and load characteristics during the degradation of the hinge node, the dynamics simulation conditions are set, and the operating states of the hinge node at different stages of its whole life cycle are simulated by using the high‐fidelity virtual model of the hinge node, and the prediction model of the hot spot stress of the hinge node is established to monitor its in‐position state in real time, and the operation and maintenance overhaul method based on the health state of the hinge node is proposed. The system is divided into four modules: multilevel inverse modelling of the assembly twin, statistical shape characterization and analysis of batch parts, optimization of fixture positioning and flexible assembly of thin‐walled parts, and optimization of low‐stress assembly of bolted joint structure, which verifies the feasibility of the method and provides guidance for the actual product forming process.
Highlights
With the continuous development of the economy, people’s demand for personalized products is increasing, requiring product production lines to have multispecies, small-lot, multifreedom, and high-reliability production capacity, and production lines should independently adapt to various changes brought about by-product personalization, which include changes from within and changes from outside [1]
The reuse-oriented conformation space expression is analysed, and it is pointed out that the reuse-oriented conformation space should contain a priori path information, and the concept of a priori degree is defined in combination with ant colony pheromone concentration, and it is introduced into the information expression of a priori path positional points and the construction of a priori path space
The method of calculating the point distance of assembly multisource information nonlinear mapping space is proposed, and the calculation of assembly path reuse degree is realized by the many-to-many matching algorithm that fuses the assembly multisource information, and on this basis, the multigrained clustering of parts path reuse degree is realized by cohesive hierarchical clustering based on the multigrained judging criteria of assembly task coupling degree and dimensional information aggregation degree, and the uniform enclosing box dimensional information between clusters is obtained according to the clustering results
Summary
With the continuous development of the economy, people’s demand for personalized products is increasing, requiring product production lines to have multispecies, small-lot, multifreedom, and high-reliability production capacity, and production lines should independently adapt to various changes brought about by-product personalization, which include changes from within and changes from outside [1]. Industrial robotic arms are widely used in all aspects of flexible production lines, and robotic arms replace humans to complete the tasks of handling and assembly in the production process In these operational tasks, the requirements for robotic arm cooperation and functionality are increasing, and the traditional single robotic arm based on the demonstration mode is already difficult to meet all the needs, while the multirobot collaborative operating system can well solve these problems [2]. Digital prototype models often ignore many elements in the real environment, resulting in discrepancies with the actual part assembly, making it difficult to meet the performance requirements of products with high accuracy [3]. Industrial robotic arms are widely used in all aspects of flexible production lines, and robotic arms replace humans in completing tasks such as handling and assembly in the production process. It is important to analyse the assembly process and error transmission of the product digital model through simulation methods to verify whether the part assembly sequence, assembly path, force deformation, etc. meet the engineering requirements, which is important to guide the actual part manufacturing and assembly operations, improve the efficiency of product development, and reduce costs
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