University of Bristol research in ultrasonic sensing technology for enhanced component integrity

May 8, 2024

May 8, 2024

Elastic shear wavefront depiction moving in the (x1,x3) plane with displacement vector u=(0,u2(x1,x3),0)T. The arrows depict the orientation of the slowness surface associated with the underlying crystalline material (Courtesy Taylor& Francis Group/University of Bristol)
Elastic shear wavefront depiction moving in the (x1,x3) plane with displacement vector u=(0,u2(x1,x3),0)T. The arrows depict the orientation of the slowness surface associated with the underlying crystalline material (Courtesy Taylor& Francis Group/University of Bristol)

In the study ‘A probabilistic approach to modelling ultrasonic shear wave propagation in locally anisotropic heterogeneous media’ published in the journal Waves in Random and Complex Media, researchers from the University of Bristol, UK, have devised a formula that is able to inform the design boundaries for a given component’s geometry and material microstructure.

The authors state that a commercially viable sensing technology and associated imaging algorithm to assess the quality of such components does not currently exist. However, if Additive Manufacturing of metallic components could satisfy industry safety and quality standards, there could be significant commercial advantages in the manufacturing sector.

The key breakthrough in this study is noted as the use of ultrasonic array sensors, which are essentially the same as those used in medical imaging. However, these new laser-based versions would not require the sensor to be in contact with the material.

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Author Professor Anthony Mulholland, head of the School of Engineering Maths and Technology, explained, “There is a potential sensing method using a laser-based ultrasonic array and we are using mathematical modelling to inform the design of the this equipment ahead of its in-situ deployment.”

The team built a mathematical model that incorporated the physics of ultrasonic waves propagating through a layered (as additively manufactured) metallic material, which took into account the variability one gets between each manufactured component.

The mathematic formula is made up of the design parameters associated with the ultrasonic laser and the nature of the particular material. The output is a measure of how much information will be produced by the sensor to enable the mechanical integrity of the component to be assessed. The input parameters can then be varied to maximise this information content.

It is hoped that this discovery will accelerate the design and deployment of this proposal solution to this manufacturing opportunity.

“We can then work with our industry partners to produce a means of assessing the mechanical integrity of these safety critical components at the manufacturing stage,” Professor Mullholland added. “This could then lead to radically new designs (by taking full advantage of 3D printing), quicker and more cost effective production processes, and significant commercial and economic advantage to UK manufacturing.”

Now, the team plans to use the findings to help their experimental collaborators who are designing and building the laser-based ultrasonic arrays.

These sensors will then be deployed in situ by robotic arms in a controlled Additive Manufacturing environment. They are intended to maximise the information content in the data produced by the sensor and create bespoke imaging algorithms to generate tomographic images of the interior of components supplied by their industry partners. Destructive means will then be employed to assess the quality of the tomographic images produced.

“Opening up 3D printing in the manufacture of safety critical components, such as those found in the aerospace industry, would provide significant commercial advantage to UK industry,” Professor Mullholland concluded. “The lack of a means of assessing the mechanical integrity of such components is the major blockage in taking this exciting opportunity forward. This study has built a mathematical model that simulates the use of a new laser based sensor, that could provide the solution to this problem, and this study will accelerate the sensor’s design and deployment.”

To read the full study, click here.

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May 8, 2024

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