Optimization method for spatial route adjustment of multi-bends pipes considering assembly demands

Abstract

Purpose This paper aims to propose an optimization method to automatically adjust the spatial route of multibend pipes to meet the assembly demands in constrained space. Design/methodology/approach The compact geometric parameters that uniquely determine the pipe route are analyzed. Besides, the relationship between these parameters and the end pose is revealed based on the exponential product formula. Mathematical representations for the engineering constraints, including the end pose restriction, collision interference, manufacture ability and geometric limitations, are further established. On this basis, the adjustment of the spatial route is formulated as a multiconstraint optimization problem. A modified particle swarm optimization method based on the combination of gradient projection and swarm intelligence is designed to find the near-optimal pipe that meets the required assembly demands. Findings The experimental results show that the proposed method can effectively find the feasible pipe route that satisfies the engineering constraints and the end pose requirement is highly guaranteed. Originality/value The proposed method can automate the geometric adjustment of multi-bend pipes to meet the actual assembly demands, which significantly reduces manual efforts and guarantees high accuracy. The results demonstrate the possibility of further applications in the pipe assembly or design process, especially in ships, aerospace products or pressure vessels.

Optimization method for the assembly pose of parts considering manufacturing deviations and contact deformations

“…Deviation prediction has many application scenarios in actual production. In the significant components’ assembly field, the deviation prediction can reduce the errors and the corresponding stress in the flexible assembly process of aircraft fuselage and other parts (Chen et al , 2022). The deviation prediction also has great prospects in the precise control of robots, such as the accurate estimation of the terminal pose of the medical robot (Su et al , 2020, 2021), the collaborative error of wearable devices and so on.…”