现代制造工程 ›› 2024, Vol. 526 ›› Issue (7): 26-34.doi: 10.16731/j.cnki.1671-3133.2024.07.004

• 增材制造 • 上一篇    下一篇

基于逐层生成的多轴机器人增材制造轨迹优化研究*

曲利红1, 景世沛2   

  1. 1 郑州电子信息职业技术学院,郑州 451450;
    2 河南牧业经济学院,郑州 450046
  • 收稿日期:2023-12-26 出版日期:2024-07-18 发布日期:2024-07-30
  • 作者简介:曲利红,本科,讲师,主要研究方向为计算机科学与技术及3D打印等。E-mail:317164970@qq.com
  • 基金资助:
    *国家自然科学基金项目(52065064);河南省科技厅科技攻关项目(212102210565)

Research on trajectory optimization method for multi axis robot additive manufacturing based on layer by layer generation

QU Lihong1, JING Shipei2   

  1. 1 Zhengzhou Professional Technical Institute of Electronics & Information,Zhengzhou 451450,China;
    2 Henan University of Husbandry and Economy,Zhengzhou 450046,China
  • Received:2023-12-26 Online:2024-07-18 Published:2024-07-30

摘要: 为解决增材制造生产旋转薄壁零件时出现的阶梯效应和沉积问题,提升零件制造的质量与精度,提高增材制造效率,提出了一种基于逐层生成的多轴机器人增材制造轨迹优化策略。首先,基于六轴机器人的逆几何模型构建方向向量,并从中提取方向参数定义不同层的目标函数和约束函数,提出逐层轨迹优化策略;其次,考虑同轴沉积系统产生的轴冗余,运用轴冗余改善机器人的运动轨迹,并将轨迹优化应用于旋转薄壁零件的实际制造;最后,为验证所提方法的有效性,选取逐点和非优化轨迹策略,并选择空心半球体、拉瓦尔喷嘴和进气漏斗构型进行实验验证。实验结果表明,所提方法能够提供更平滑的关节运动轨迹,提升零件的制造质量与制造效率,且与非优化轨迹策略相比,所提方法具有更高的几何精度,可有效改进增材制造工艺。

关键词: 多轴机器人, 增材制造, 多轴沉积, 轨迹优化, 逐层生成

Abstract: In order to solve the step effect and deposition problems in the production of rotating thin-walled parts by additive manufacturing,improve the quality and accuracy of parts manufacturing,and improve the efficiency of additive manufacturing,a multi-axis robot additive manufacturing trajectory optimization based on layer-by-layer generation was proposed. Firstly,the direction vector was constructed based on the inverse geometric model of the six-axis robot,and the direction parameters were extracted from it to define the objective function and constraint function of different layers,and a layer-by-layer trajectory optimization strategy was proposed.Secondly,considering the axis redundancy generated by the coaxial deposition system,the axis redundancy was used to improve the motion trajectory of the robot,and the trajectory optimization was applied to the actual manufacturing of rotating thin-walled parts.Finally,to verify the effectiveness of the proposed method,point-by-point and non-optimized trajectory strategies were selected,and the hollow hemisphere,Laval nozzle and intake funnel configurations were selected for experimental verification.The experimental results show that the proposed method can provide a smoother joint motion trajectory,improve the manufacturing quality and manufacturing efficiency of parts,and compared with the non-optimized trajectory strategy,the proposed method has higher geometric accuracy,which can effectively improve additive manufacturing.

Key words: multi-axis robot, additive manufacturing, multiaxial deposition, trajectory optimization, layer-by-layer generation

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