Literature DB >> 33466997

Towards Efficient Milling of Multi-Cavity Aeronautical Structural Parts Considering ACO-Based Optimal Tool Feed Position and Path.

Yupeng Xin1, Yuanheng Li1, Wenhui Li2, Gangfeng Wang3.   

Abstract

Cavities are typical features in aeronautical structural parts and molds. For high-speed milling of multi-cavity parts, a reasonable processing sequence planning can significantly affect the machining accuracy and efficiency. This paper proposes an improved continuous peripheral milling method for multi-cavity based on ant colony optimization algorithm (ACO). Firstly, by analyzing the mathematical model of cavity corner milling process, the geometric center of the corner is selected as the initial tool feed position. Subsequently, the tool path is globally optimized through ant colony dissemination and pheromone perception for path solution of multi-cavity milling. With the advantages of ant colony parallel search and pheromone positive feedback, the searching efficiency of the global shortest processing path is effectively improved. Finally, the milling programming of an aeronautical structural part is taken as a sample to verify the effectiveness of the proposed methodology. Compared with zigzag milling and genetic algorithm (GA)-based peripheral milling modes in the computer aided manufacturing (CAM) software, the results show that the ACO-based methodology can shorten the milling time of a sample part by more than 13%.

Entities:  

Keywords:  ant colony optimization algorithm; complex structural parts; corner milling; processing sequence planning; smart manufacturing

Year:  2021        PMID: 33466997      PMCID: PMC7830258          DOI: 10.3390/mi12010088

Source DB:  PubMed          Journal:  Micromachines (Basel)        ISSN: 2072-666X            Impact factor:   2.891


  1 in total

1.  A tool path optimization approach based on blend feature simplification for multi-cavity machining of complex parts.

Authors:  Yupeng Xin; Shengqiang Yang; Gangfeng Wang; Richard Evans; Fengfeng Wu
Journal:  Sci Prog       Date:  2019-09-16       Impact factor: 2.774

  1 in total

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