基于模糊聚类疫苗免疫算法的汽车零件双激光切割路径规划研究

Automobile Part Double Laser Cutting Path Planning Based on Fuzzy Cluster Vaccine Immune Algorithm

激光板材切割不仅可提高生产效率,而且更容易适应多变的生产需求。目前激光切割已经普遍应用于汽车零件切割当中,无论是理论研究还是现实生产当中,出现最多最广泛的是单激光切割。然而,在大规模工业生产中,该切割方法无法满足生产需要。为进一步提高切割效率,对双激光协同切割路径规划进行研究。首先建立双激光协同切割的数学模型,然后提出一种模糊聚类疫苗免疫算法对双激光切割路径进行规划,最后选取ESICUP网站提供的不规则多边形样片以及实际汽车零件数据进行算法仿真验证。试验结果表明,与用于单激光切割路径规划的传统GA/IA算法相比,该算法规划的激光切割路径空行程更短,且算法稳定性更高。

Laser plate cutting not only improves production efficiency,but is also easier to adapt to changing production needs.At present,laser cutting has been widely used in automobile part cutting.However,single laser cutting is the most widely used in both theoretical research and practical production.However,in large-scale industrial production,this cutting method cannot meet the production needs.To further improve the cutting efficiency of automobile parts,dual laser collaborative cutting path planning was studied.First,the mathematical model of double laser collaborative cutting was established.Second,a fuzzy clustering vaccine immune algorithm was proposed to plan the double laser cutting path.Finally,the irregular polygon samples provided by the ESICUP website and actual automobile part data were selected for algorithm simulation verification.Experimental results show that compared with the traditional GA/IA algorithm for single laser cutting path planning,the laser cutting path planned by the proposed algorithm has a shorter empty travel and higher stability.