Investigating the capability of microfocus x-ray computed tomography for areal surface analysis of additively manufactured parts

INTRODUCTION The ability to perform non-destructive areal surface analysis, for example of the internal surfaces of additively manufactured (AM) parts has potential advantages during product development and for production process control. This paper reports on the extraction of areal surface information from microfocus x-ray computed tomography (XCT) data. Using this novel technique a range of areal parameter values were generated from a surface section extracted from XCT scan data of an as-built (no post-processing) AlSi10Mg additively manufactured part. This was then compared with the parameter values generated from a focus variation scan of the same surface section. The data comparison method involving normalisation of data format to allow analysis using industry-standard software, such as MountainsMap (Digital Surf, Besançon, France) or SurfStand (The Centre for Precision Technologies UoH) is demonstrated. Importing the extracted surfaces into these powerful software packages allows one-click data filtering per ISO 25178-3 [1] and the generation of a comprehensive suite of areal surface parameter values. These include feature and field parameters, amplitude, spatial, hybrid and functional parameters, as defined in ISO 25178-2 [2]. A method for characterising the capability of XCT for areal surface measurement is demonstrated by comparing results obtained from samples taken from a Rubert comparator test panel, with sample surface Ra values between 0.8 μm and 50 μm

On characterising surface topography of metal powder bed fusion additive manufactured parts

Inherent to the somewhat uncontrolled nature of the additive process, the surfaces of metal powder bed fusion additively manufactured components tend to be very rough. Large isolated ‘bumps’, as one of the major defect features, are often present due to partially melted particles attached to the surface. An enhanced watershed segmentation method is proposed to separate these ‘bump’ features from the underlying surface texture such that the ‘bumps’ and underlying surface can be quantitatively analysed. The results show that the amplitude roughness parameters of the underlying surface are significantly less than the un-segmented surface and spatial roughness parameters differ between two surfaces. Characterising the extracted underlying surface and ‘bumps’ independently allows better correlation between surface measurements and additive system performance and hence aids in process optimization

Results from an interlaboratory comparison of areal surface texture parameter extraction from X-ray computed tomography of additively manufactured parts

This paper presents the results of the CT-STARR (CT-Surface Texture for Additive Round Robin) interlaboratory comparison. The study compares the results obtained for the extraction of areal surface texture data per ISO 25178-2 from five X-ray computed tomography (XCT) volume measurements from each of four laboratories. To reduce the number of process variables, all participants utilise a Nikon XCT machine, either an XT H 225 industrial CT or an MCT225 metrology CT. Measurement process parameters, such as physical X-ray filtering, acceleration voltage and filament current, are set at similar values for all machines. All data processing and computation to extract, align, crop, filter and generate surface texture parameter information and deviation analysis results from the measurement volumes is performed by one participant. Two Ti6Al4V ELI (extra low interstitial) components are included in each of the XCT acquisitions. The first component is an additively manufactured cube built on an Arcam Q10 electron beam melting machine. Surface texture data is extracted from XCT scans of this part. The second component is a machined artefact designed for XCT scaling and surface determination analysis and verification. The data extracted from XCT measurements of these components is compared with measurements from coordinate measuring machine, focus variation and stylus instruments. The effect of scaling correction and XCT surface determination on extracted surface texture data, as well as measurement repeatability and reproducibility, are discussed

Results from an interlaboratory comparison of areal surface texture parameter extraction from X-ray computed tomography of additively manufactured parts

This paper presents the results of the CT-STARR (CT-Surface Texture for Additive Round Robin) interlaboratory comparison. The study compares the results obtained for the extraction of areal surface texture data per ISO 25178-2 from five X-ray computed tomography (XCT) volume measurements from each of four laboratories. To reduce the number of process variables, all participants utilise a Nikon XCT machine, either an XT H 225 industrial CT or an MCT225 metrology CT. Measurement process parameters, such as physical X-ray filtering, acceleration voltage and filament current, are set at similar values for all machines. All data processing and computation to extract, align, crop, filter and generate surface texture parameter information and deviation analysis results from the measurement volumes is performed by one participant. Two Ti6Al4V ELI (extra low interstitial) components are included in each of the XCT acquisitions. The first component is an additively manufactured cube built on an Arcam Q10 electron beam melting machine. Surface texture data is extracted from XCT scans of this part. The second component is a machined artefact designed for XCT scaling and surface determination analysis and verification. The data extracted from XCT measurements of these components is compared with measurements from coordinate measuring machine, focus variation and stylus instruments. The effect of scaling correction and XCT surface determination on extracted surface texture data, as well as measurement repeatability and reproducibility, are discussed

System-wide approaches to antimicrobial therapy and antimicrobial resistance in the UK:the AMR-X framework

Antimicrobial resistance (AMR) threatens human, animal, and environmental health. Acknowledging the urgency of addressing AMR, an opportunity exists to extend AMR action-focused research beyond the confines of an isolated biomedical paradigm. An AMR learning system, AMR-X, envisions a national network of health systems creating and applying optimal use of antimicrobials on the basis of their data collected from the delivery of routine clinical care. AMR-X integrates traditional AMR discovery, experimental research, and applied research with continuous analysis of pathogens, antimicrobial uses, and clinical outcomes that are routinely disseminated to practitioners, policy makers, patients, and the public to drive changes in practice and outcomes. AMR-X uses connected data-to-action systems to underpin an evaluation framework embedded in routine care, continuously driving implementation of improvements in patient and population health, targeting investment, and incentivising innovation. All stakeholders co-create AMR-X, protecting the public from AMR by adapting to continuously evolving AMR threats and generating the information needed for precision patient and population care.</p

System-wide approaches to antimicrobial therapy and antimicrobial resistance in the UK:the AMR-X framework

Antimicrobial resistance (AMR) threatens human, animal, and environmental health. Acknowledging the urgency of addressing AMR, an opportunity exists to extend AMR action-focused research beyond the confines of an isolated biomedical paradigm. An AMR learning system, AMR-X, envisions a national network of health systems creating and applying optimal use of antimicrobials on the basis of their data collected from the delivery of routine clinical care. AMR-X integrates traditional AMR discovery, experimental research, and applied research with continuous analysis of pathogens, antimicrobial uses, and clinical outcomes that are routinely disseminated to practitioners, policy makers, patients, and the public to drive changes in practice and outcomes. AMR-X uses connected data-to-action systems to underpin an evaluation framework embedded in routine care, continuously driving implementation of improvements in patient and population health, targeting investment, and incentivising innovation. All stakeholders co-create AMR-X, protecting the public from AMR by adapting to continuously evolving AMR threats and generating the information needed for precision patient and population care.</p

Factors affecting the accuracy of areal surface texture data extraction from X-ray CT

The ability to perform non-destructive areal surface analysis of the internal surfaces of additively manufactured (AM) components would be advantageous during product development, process control and product acceptance. Currently industrial X-ray computed tomography (XCT) is the only practical method for imaging the internal surfaces of AM components. A viable method of extracting useable areal surface texture data from XCT scans has now been developed and this paper reports on three measurement and data processing factors affecting the value of areal parameters per ISO 25178-2 generated from XCT volume data using this novel technique