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Multi-objective constraint-based flexible tube assembly technology
DOI:10.1088/1361-6501/ae3253.png)
Abstract
En 中文
In high-precision equipment such as launch vehicles and aero-engines, the stress-free assembly of rigid tubes is a critical factor in ensuring operational reliability; however, the prevalence of manufacturing and positioning deviations has rendered this a persistent and unresolved challenge within the industry. While existing research primarily focuses on the derivation of theoretical pipeline parameters, it largely overlooks the critical influence of end-face machining parameters on the assembly workflow. This oversight necessitates iterative trimming of tube ends during actual production, severely constraining assembly efficiency. Consequently, this significant research gap at the practical implementation level has yet to receive sufficient attention. To address this issue, this paper proposes a novel flexible assembly method governed by multi-objective constraints. The method begins by employing laser scanning to capture the assembly environment and determine installation boundaries, enabling the adaptive modeling of tube geometry. On-site machining is then performed to mitigate dimensional uncertainties caused by environmental variation. A multi-objective optimization model is developed to determine cutting parameters, incorporating three critical constraints: tube length, horseshoe port misalignment, and weld surface perpendicularity. These are formulated within a nonlinear constrained optimization framework to achieve one-time end-face machining. The proposed method is validated through physical model experiments and field trials on rocket tubes. The physical model experimental results demonstrate an average assembly gap within 0.2 mm and an average misshapen edge within 0.035 mm. Furthermore, the rocket field trial confirms that the optimized parameters achieve successful one-time assembly, strictly satisfying stress-free requirements and significantly enhancing overall efficiency.
Journal
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3.4
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2.6K
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2.3W

