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基于电涡流传感器的激光粉末床熔覆零件缺陷检测优化

Eddy Current Sensors Optimization for Defect Detection in Parts Fabricated by Laser Powder Bed Fusion.

机构信息

Université Paris-Saclay, CEA, List, F-91120 Palaiseau, France.

出版信息

Sensors (Basel). 2023 Apr 27;23(9):4336. doi: 10.3390/s23094336.

Abstract

The production of parts by additive manufacturing is an important issue for the reduction in manufacturing costs and the creation of complex geometries. Optical inspection is often implemented in the machines during the manufacturing process in order to monitor the possible generated defects. However, it is also crucial to test the quality of the manufactured parts after their fabrication and monitor their health throughout their industrial lifetime. Therefore structural health monitoring (SHM) methods need to be studied or designed. In this paper, the eddy current method is used to control fabricated parts, as this technique is adapted to detect surface and shallow defects in conductive materials. Using simulations with the CIVA non-destructive testing software package, several sensors and their parameters were tested in order to determine the most optimal ones: a separate transmitter/receiver sensor and an isotropic sensor were finally designed. The comparison of these sensors' efficiency was made on the detection of notches and engraved letters based on simulation and experimental tests on parts fabricated by laser powder bed fusion (L-PBF) in order to determine the optimal sensor. The various tests showed that the isotropic sensor is the optimal one for the detection and characterization of defects.

摘要

通过增材制造生产零件对于降低制造成本和制造复杂几何形状非常重要。为了监测可能产生的缺陷,光学检测通常在制造过程中在机器中实现。然而,在制造完成后测试制造零件的质量并在整个工业寿命期间监测其健康状况也至关重要。因此,需要研究或设计结构健康监测 (SHM) 方法。在本文中,使用涡流方法来控制制造的零件,因为该技术适用于检测导电材料的表面和浅层缺陷。使用 CIVA 无损检测软件包进行模拟,测试了几种传感器及其参数,以确定最优化的传感器:最终设计了一个单独的发射器/接收器传感器和各向同性传感器。基于对通过激光粉末床融合 (L-PBF) 制造的零件进行模拟和实验测试,对这些传感器的效率进行了比较,以确定最佳传感器。各种测试表明,各向同性传感器是检测和表征缺陷的最佳传感器。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bf0/10181579/8d2fafe38cc2/sensors-23-04336-g001.jpg

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