Brierley N, Bellon C, Lazaro Toralles B
The Manufacturing Technology Centre, Pilot Way, Ansty Business Park, Coventry, CV7 9JU, UK.
Federal Materials Testing Institute/Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 87, 12205 Berlin, Germany.
Proc Math Phys Eng Sci. 2018 Aug;474(2216):20170319. doi: 10.1098/rspa.2017.0319. Epub 2018 Aug 29.
The inspection of complex-shaped components, such as those enabled by additive manufacturing, is a major challenge in industrial quality assurance. A frequently adopted approach to volumetric non-destructive evaluation is X-ray computed tomography, but this has major drawbacks. Two-dimensional radiography can overcome some of these problems, but does not generally provide an inspection that is as capable. Moreover, designing a detailed inspection for a complex-shaped component is a labour-intensive task, requiring significant expert input. In response, a computational framework for optimizing the data acquisition for an image-based inspection modality has been devised. The initial objective is to advance the capabilities of radiography, but the algorithm is, in principle, also applicable to alternative types of imaging. The algorithm exploits available prior information about the inspection and simulations of the inspection modality to allow the determination of the optimal inspection configuration, including specifically component poses with respect to the imaging system. As an intermediate output, spatial maps of inspection performance are computed, for understanding spatially varying limits of detection. Key areas of innovation concern the defect detectability evaluation for arbitrarily complex indications and the creation of an application-specific optimization algorithm. Initial trials of the algorithm are presented, with good results.
对复杂形状部件(如通过增材制造实现的部件)进行检测是工业质量保证中的一项重大挑战。体积无损检测常用的方法是X射线计算机断层扫描,但这存在重大缺陷。二维射线照相可以克服其中一些问题,但通常无法提供同样有效的检测。此外,为复杂形状部件设计详细检测是一项劳动密集型任务,需要大量专家投入。为此,已设计出一个计算框架,用于优化基于图像的检测方式的数据采集。最初目标是提升射线照相的能力,但该算法原则上也适用于其他类型的成像。该算法利用有关检测的可用先验信息以及检测方式的模拟,以确定最佳检测配置,包括部件相对于成像系统的具体姿态。作为中间输出,计算检测性能的空间图,以了解空间变化的检测极限。创新的关键领域涉及对任意复杂迹象的缺陷可检测性评估以及创建特定应用的优化算法。给出了该算法的初步试验结果,效果良好。