Adaptive remanufacturing for freeform surface parts based on linear laser scanner and robotic laser cladding

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Wei Ma , Tianliang Hu , Chengrui Zhang , Qizhi Chen
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引用次数: 0

Abstract

Freeform surface parts play a significant role in the aerospace industry, the mold- manufacturing industry and the automobile industry, and it is energy-saving, material-saving, time-saving and environmentally beneficial to remanufacture the damaged components to restore their functionality and performance. Due to the complex geometry of the freeform surface wear, the adaptive remanufacturing of freeform surface parts is confronted with challenges. In this paper, an adaptive remanufacturing method for freeform surface parts based on linear laser scanner and robotic laser cladding is proposed to realize the precise freeform surface measurement and optimized remanufacturing path generation. On the one hand, a systematic wear measurement and assessment method is proposed to precisely locate and quantify the wear. With the noncontact calibration of the laser scanner and industrial robot, the contour of the target surface is real-timely measured and the reverse model is efficiently constructed, which provides detailed 3D morphological information of the worn freeform surface for the latter wear analysis. Next, considering the considerable difference between the reverse model and the nominal model, a refined model aligning method weighted by surface wear segmentation is proposed to minimize the alignment error and, further, the difference entity to be additively manufactured is obtained by discrete model comparison. On the other hand, to cope with the unsatisfactory binding strength over the freeform surface basis and small fragments of the working path for the traditional plane or cylinder slicing method, a novel remanufacturing path generation method is proposed. Considering the curvature distribution of the freeform surface, an optimized equidistant freeform surface slicing method is especially proposed for the difference entity to realize the adaptive fitting to the freeform basin. Furthermore, based on the equivalent volume overlapping model of laser cladding, the cladding track filling method for the freeform surface slicing is designed with the optimized track-to-track distance, which can reduce surface waviness and improve remanufacturing efficiency. Finally, simulations and experiments for the remanufacturing scenario of the steam turbine blade are conducted to verify the validity and feasibility of the proposed adaptive remanufacturing method for freeform surface parts based on linear laser scanner and robotic laser cladding.

基于线性激光扫描仪和机器人激光熔覆的自由曲面零件自适应再制造技术
自由曲面零件在航空航天工业、模具制造业和汽车工业中发挥着重要作用,对损坏的零件进行再制造以恢复其功能和性能既节能、省料、省时又环保。由于自由曲面磨损的几何形状复杂,自由曲面零件的自适应再制造面临着挑战。本文提出了一种基于线性激光扫描仪和机器人激光熔覆的自由曲面零件自适应再制造方法,以实现精确的自由曲面测量和优化的再制造路径生成。一方面,提出了系统的磨损测量和评估方法,以精确定位和量化磨损。通过激光扫描仪和工业机器人的非接触校准,实时测量目标表面轮廓并高效构建反向模型,为后期磨损分析提供详细的磨损自由形态表面三维形态信息。接下来,考虑到反向模型与标称模型之间存在较大差异,提出了一种以表面磨损细分为权重的精细模型对准方法,以最小化对准误差,并进一步通过离散模型比较获得待添加制造的差异实体。另一方面,针对传统的平面或圆柱体切片方法在自由曲面基础上的结合强度不理想以及工作路径碎片较小的问题,提出了一种新型的再制造路径生成方法。考虑到自由曲面的曲率分布,特别针对差分实体提出了优化的等距自由曲面切片方法,以实现对自由曲面的自适应拟合。此外,基于激光熔覆等效体积重叠模型,设计了优化轨迹间距的自由曲面切片熔覆轨迹填充方法,可减少曲面波浪度,提高再制造效率。最后,针对汽轮机叶片的再制造场景进行了仿真和实验,验证了所提出的基于线性激光扫描仪和机器人激光熔覆的自由曲面零件自适应再制造方法的有效性和可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Robotics and Computer-integrated Manufacturing
Robotics and Computer-integrated Manufacturing 工程技术-工程:制造
CiteScore
24.10
自引率
13.50%
发文量
160
审稿时长
50 days
期刊介绍: The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.
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