Defect formation is a critical challenge for powder-based metal additive manufacturing(AM).Current understanding on the three important issues including formation mechanism,influence and control method of metal AM def...Defect formation is a critical challenge for powder-based metal additive manufacturing(AM).Current understanding on the three important issues including formation mechanism,influence and control method of metal AM defects should be updated.In this review paper,multi-scale defects in AMed metals and alloys are identified and for the first time classified into three categories,including geometry related,surface integrity related and microstructural defects.In particular,the microstructural defects are further divided into internal cracks and pores,textured columnar grains,compositional defects and dislocation cells.The root causes of the multi-scale defects are discussed.The key factors that affect the defect formation are identified and analyzed.The detection methods and modeling of the multi-scale defects are briefly introduced.The effects of the multi-scale defects on the mechanical properties especially for tensile properties and fatigue performance of AMed metallic components are reviewed.Various control and mitigation methods for the corresponding defects,include process parameter control,post processing,alloy design and hybrid AM techniques,are summarized and discussed.From research aspect,current research gaps and future prospects from three important aspects of the multi-scale AM defects are identified and delineated.展开更多
针对风力发电机叶片人工检测低效,缺陷诊断难的问题,提出一种基于无人机与图像处理的风力发电机叶片缺陷识别方法。通过Halcon 12与Visual Studio 2015的联合开发,实现图像处理流程、检测结果输出以及缺陷回放等功能,包括相机标定、通...针对风力发电机叶片人工检测低效,缺陷诊断难的问题,提出一种基于无人机与图像处理的风力发电机叶片缺陷识别方法。通过Halcon 12与Visual Studio 2015的联合开发,实现图像处理流程、检测结果输出以及缺陷回放等功能,包括相机标定、通过快速自适应加权中值滤波处理图像、动态阈值分割叶片图像缺陷特征,利用区域处理识别裂纹和砂眼等缺陷,并对缺陷进行分类与测量以及输出对叶片质量的分析报告等,实现风力发电机叶片表面缺陷的自动检测功能。通过实例验证了该方法在风力发电机叶片表面缺陷检测中的较高精确性与算法稳定性。展开更多
基金the funding support to this research via the projects of ZVMR,BBAT and ZE1W from The Hong Kong Polytechnic Universityproject#RNE-p2–21 of the Shun Hing Institute of Advanced EngineeringThe Chinese University of Hong Kong and the GRF projects(Nos.15223520 and 15228621)。
文摘Defect formation is a critical challenge for powder-based metal additive manufacturing(AM).Current understanding on the three important issues including formation mechanism,influence and control method of metal AM defects should be updated.In this review paper,multi-scale defects in AMed metals and alloys are identified and for the first time classified into three categories,including geometry related,surface integrity related and microstructural defects.In particular,the microstructural defects are further divided into internal cracks and pores,textured columnar grains,compositional defects and dislocation cells.The root causes of the multi-scale defects are discussed.The key factors that affect the defect formation are identified and analyzed.The detection methods and modeling of the multi-scale defects are briefly introduced.The effects of the multi-scale defects on the mechanical properties especially for tensile properties and fatigue performance of AMed metallic components are reviewed.Various control and mitigation methods for the corresponding defects,include process parameter control,post processing,alloy design and hybrid AM techniques,are summarized and discussed.From research aspect,current research gaps and future prospects from three important aspects of the multi-scale AM defects are identified and delineated.
文摘针对风力发电机叶片人工检测低效,缺陷诊断难的问题,提出一种基于无人机与图像处理的风力发电机叶片缺陷识别方法。通过Halcon 12与Visual Studio 2015的联合开发,实现图像处理流程、检测结果输出以及缺陷回放等功能,包括相机标定、通过快速自适应加权中值滤波处理图像、动态阈值分割叶片图像缺陷特征,利用区域处理识别裂纹和砂眼等缺陷,并对缺陷进行分类与测量以及输出对叶片质量的分析报告等,实现风力发电机叶片表面缺陷的自动检测功能。通过实例验证了该方法在风力发电机叶片表面缺陷检测中的较高精确性与算法稳定性。
