A Novel In-line Approach for Detecting Texture of Aerospace Components During the Machining Process

Aerospace manufacturing is precise and expensive, so being able to quickly detect substandard raw materials through Forge Feedback Analysis (FFA) allows machinists to reduce cost and waste by optimising cutting parameters, preventing tool damage.

Etched sample of Titanium, image courtesy of Dennis Premoli
Etched sample of Titanium, image courtesy of Dennis Premoli

Current characterisation methods tend to have significant drawbacks in one or more areas including speed, representativeness, non-destructiveness, process compatibility and cost. State-of-the-art methods of surface texture analysis, such as Electron Backscatter Diffraction (EBSD), while extremely precise, require a lab setting, are only applicable to specific sized samples, and are time- consuming. Other methods, such as visual inspections or optical microscopy, while more robust, are not as accurate and struggle to quickly and correctly identify crystallographic texture. Therefore, FFA aims to position itself as a capable, representative, cost-effective and robust solution that can seamlessly be integrated into the current production processes with little-to-no adjustments.

To prove FFA as the most efficient and effective solution for in-process analysis, its capabilities were validated against state-of-the-art methods. To demonstrate its ability to detect macrozones in Ti-64, uni-directional rolled plate with specifically designed geometries was used (due to its grain segmentation and crystallographic texture) and embedded in Ti-64 powder before being sintered into a solid disc to be machined and analysed. The data obtained was validated against large-scale EBSD to ensure the macrozones detected in the FFA process were correctly identified.

FFA successfully detected both the presence of the rolled plate among sub-transus sintered powder, and its rolling direction. It also distinguished clear macrozone-like features from mm to 50-100 μm within the rolled plate itself.

Whilst there is further testing to be done to ascertain accuracy in other factors, this project has generated great interest with Rolls-Royce for future work focussing on industrially relevant alloys and machining equipment.

Through the development of FFA maps, we are now able to use a visual representation of the forces involved, allowing machinists to identify variations and microstructure in the material being machined. FFA has been proven capable of detecting features down to a 200 μm, as well as multi-alloy transitions, and residual manufacturing stresses.

Using the FCT HP D25 Field Assisted Sintering Technology (FAST) at the Royce Discovery Centre has enabled the creation of specific microstructure to develop and validate FFA as an industrially relevant technology.

The ability to quickly design and implement new alloys, multi-alloy components, and structures for each machining trial has proven essential to maintain pace with the accelerated development that FFA has seen over the last few years, going from design of experiment (DOE) stage, to sample creation, to machining in just a few days.

Machining is the most costly processing step for titanium components and in many cases up to 90% of the forging is machined away to waste (swarf). Over the last several years, the team at Royce at the University of Sheffield have developed an approach that detects microstructural features from the reaction force data between the machining tool and workpiece. A fingerprint of the microstructure can be generated from this force data in a matter of seconds. As this technique is an in-process approach, it could eliminate costly non-destructive evaluation (NDE) steps and procedures and provide a digital passport of the material for component lifing teams.

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