Enhancing Manufacturing Processes through XRPD Analysis: A Comprehensive Review

經過 John Duffy, Friday, 23rd June 2023

The 1980s is an iconic decade for many reasons – but it wasn’t all big hair and neon fashion statements. This vibrant decade also gave us the earliest form of additive manufacturing (AM), the innovation that would become what many know today as ‘3D printing’. It might seem like a slow start – but technology is quickly catching up to AM’s potential.

Process control is vital in Additive Manufacturing

Increasingly, AM is being used to create spare parts and fixtures for manufacturing machines, revolutionizing custom tooling. AM also has great potential to create critical parts for aerospace and medical applications. But any flaws in these parts could have catastrophic consequences, especially in safety critical applications. Process control couldn’t be more crucial – especially materials analysis when working with metals.

The solution here might seem obvious to anyone with a background in metallurgy: X-ray diffraction, the industry standard, is a tried-and-true method for characterizing materials. Unfortunately, there’s a catch.

The particle statistics problem

X-ray diffraction (XRD) analysis relies on measuring the angle of diffraction as X-rays pass through a sample, characterizing its crystalline structures. But the ideal sample for XRD has a grain or crystallite size of less than 1 µm, while AM often produces parts with grain sizes of 100 µm and larger.

At this size, XRD can struggle to give useful data. This is illustrated below in Figure 1 below, which shows (left) that the standard error increase with crystallite size, and (right) that larger crystallite sizes tend to give a spotty diffraction pattern instead of a continuous arc of intensity. Note a grain and crystallite are not necessarily the same (a grain can contain several crystallites) but larger grains generally have larger crystallites.

The problem, often known as ‘particle statistics’, is that XRD only measures a small number of crystallites in a sample – they aren’t all oriented correctly to ‘catch’ the radiation. The bigger the grains and crystallites, the fewer there are overall; so in a sample with large crystallites sizes, fewer will contribute to the measurement. This makes both peak intensity and peak position measurements much less accurate, producing errors in lattice parameter and residual stress calculations.

Fig.1: (left) standard error as a function of crystallite size calculated for instruments with different goniometer radii; (right) comparison of 2D and 1D XRPD diffraction patterns between sand specimens with 8 and 27 μm crystallite size.

Traditional solutions to this issue often assume you’re working with a powder, which could be ground more finely or sieved. But when analyzing AM parts, techniques suitable for powders aren’t an option. Spinning or oscillating the sample can help, but this is difficult when the specimen is large or has a complex shape – which is often the case, as AM specializes in complex shapes!

Better data from the same resources

So, the question is how to improve the results with common resources. Our new white paper ‘…..’ shows that that the quality of data produced can be significantly improved through a couple of techniques available to standard instruments. By using a wider divergence slit to increase the irradiated area and the rocking angle of crystallites, and using a linear or area detector in scanning mode, unusable data can be improved to become useful.

Fig. 2: Comparison of data collected using static {top} and scanning {bottom} 2D detector to measure Fe-3 wt.% Si annealed at 1000°C in H2 for 4 h, which has average grain size > 500 μm. 1 D plots show intensity integration along 2theta and gamma directions

One of the most noticeable improvements is when a larger X-ray beam is used, as it directly improves the crystallite (particle) statistics. However, when this is not a viable option, using a scanning detector mode increases the effective crystallite rocking angle and improves the crystallite statistics. In effect, the result is the same as oscillating the sample. Both solutions reduce peak errors, giving a clearer picture overall.

In times of change, versatility is key

These solutions may need some creativity, but they don’t call for entirely new resources. The study was carried out on one of our Empyrean X-ray diffractometers and made use of its in-built functionality to test each solution. The Empyrean range is known for its versatility – its modular design and multipurpose features have made it popular in both research and process control – and is created to last much longer than a single project or industry trend.

As AM and wider industry practices continue to evolve as part of Industry 4.0, these adaptable solutions will be the key to making the transition smoother. At Malvern Panalytical, we specialize in analytical instruments that prepare you for the future – while improving your current processes too. We can’t wait to see the full potential of AM as it develops, and we’ll be there to support the industry every step of the way!

To read the study on X-ray powder diffraction in additive manufacturing, click here.

To find out how our Empyrean instruments can streamline your processes, contact us.

Alternatively, take a look at the Empyrean range and download a brochure here.

Further reading

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