Pipe‐assembly approach for aero‐engines by modified particle swarm optimization
Article publication date: 28 September 2010
The paper aims to present a modified particle swarm optimization (MPSO) approach for automatic generation of near‐optimal pipe routes in constrained aero‐engine 3D rotational space.
Pipe assembly for aero‐engine is formulated as searching for the optimal pipe paths meeting certain objectives in a constrained 3D rotational space. The routing space is first modelled by grid discretization in the cylindrical coordinate system, and then is simplified into several 2D planes by mapping development. The objective function is formulated to minimize the pipe lengths and the number of pipe turns, to place pipes next to the inner jacket as close as possible, and also to make pipe trajectories closely follow around obstacle contours while avoiding collisions. Then, an MPSO approach, which adopts a discrete operator and a fixed‐length encoding mechanism, is developed to seek optimal solutions to the objective function. The convergence of MPSO is theoretically proved. Finally, numerical computations of pipe‐routing examples are conducted by using Matrix Laboratory and Unigraphics NX 4.0 system, which demonstrates effectiveness and efficiency of the proposed method.
Results show that MPSO can quickly find the optimal pipe routes meeting certain engineering constraints, and also manifests better computation convergences.
The application of the MPSO approach in pipe routing for aero‐engines is demonstrated. MPSO is a general modified particle swarm optimization version that it is not restricted to the pipe‐routing problems, and the routing approach can also be applied in similar path‐planning problems such as robot path‐planning and very large‐scale integration design.
The paper develops a new formulation for aero‐engine pipe‐routing problems, and presents an MPSO approach to find the optimal pipe paths.
Liu, Q. and Wang, C. (2010), "Pipe‐assembly approach for aero‐engines by modified particle swarm optimization", Assembly Automation, Vol. 30 No. 4, pp. 365-377. https://doi.org/10.1108/01445151011075825
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