Computed Tomography

Nikon Metrology, Inc, and Hyundai Motor North America have announced that Hyundai has purchased and installed Nikon’s X-ray computed tomography (CT) system. This represents a significant investment in the success of Hyundai’s newly opened $51.4 million Safety Test and Investigation Laboratory (STIL) in Superior Township, Michigan, USA.

CactuX has introduced new product line, In-situ BOX, designed to enhance CT analysis for research organizations and companies. This solution enables dynamic experiments, which are increasingly valuable for uncovering new insights into the behaviour of internal structures. Dynamic experiments allow observing changes in internal structures under specific conditions, such as mechanical or thermal loads, over a defined time.

New process to improve the inspection of metal parts based on the same technology used to expose art forgeries will be first to deploy through GE’s new STAC facility and will support GE Aerospace’s efforts to meet increased services demand and reduce industry supply chain constraints. 

For several years, the Mercedes-Benz vehicle safety division has been researching the use of X-ray technology in crash tests together with colleagues from EMI. The decisive factor for the breakthrough was the use of a linear accelerator with 1 kHz technology as the radiation source.

The BIP inline funding project aims to systematically further develop the VCbattery by VisiConsult system portfolio in terms of inspection speeds, new defect detection requirements and new additional multimodal inspection approaches.

Industrial computed tomography (CT) has been firmly established in dimensional metrology for several years now. It is often the only way to check the geometric tolerance of components, particularly when determining complex geometries that are not accessible by other optical or tactile methods. A major advantage of the technology is that, in addition to the geometric inspection, the material of the component can also be checked for defects in order to avoid component failures.

If CT is integrated into the production process at an early stage, it can generally spot defects at a point when discarding rejects is less painful. Moreover, it can also be used to identify which production parameters have led to which quality defects – preventing rejects from occurring in the first place. This works by combining 3D scans of the batteries with neural networks that have been trained on the production parameter data using machine learning.

As the digital product-development pathway has become increasingly sophisticated, ease-of-use of the accompanying scan-data-based, quality-assurance software is key to its successful application in aerospace, automotive, electronic, medical, and other industries.

Lumafield’s industrial CT platform serves a dual purpose in this effort: diagnosing common 3D printing issues such as trapped powder, closed channels, and internal voids; and aiding in the reverse engineering and material research process.