The work within the first year of the project can be summarised as follows:
- UoI and SUN performed a comprehensive state of the art of the shape memory alloy (SMA) and related technologies, which concluded that Nickel-Titanium based alloy Nitinol is the best candidate for SMA coating on metallic substrates due to its outstanding properties.
- A detailed review of SMA processing, training and heat treatment together with simulations using novel models for polycrystalline SMAs and SMA coating-elastic structure system was done by UoI resulted in proposition as for the way of deposition of the SMA coating and enabled optimisation of the setup for application of SMA coatings on metallic substrates.
- SUN has evaluated deposition techniques of SMA on metallic substrate. The High Velocity Oxygen Fuel (HVOF) spray method was proposed as the best technique, resulting in good adhesion strength of the obtained coating. Suitable techniques were also identified for the characterization of uncoated and coated samples with and without training.
- EXIS has done an extensive literature review with testing benchmarks for selecting appropriate finite element modelling (FE) software. An extensive analysis and benchmarking resulted in selection of ANSYS Mechanical and Simulia ABAQUS for future tasks.
During the second period, the work was carried out for investigating the physical nature of the SMA elements and physical properties of SMA materials (WP2), for creating and characterizing the SMA coating (WP3), design of the manipulating device and control software (WP4) as well as development of algorithms for defect detection and characterisation (WP5).
The main findings of UoI and SUN concerning the physical nature of the SMA elements in WP2 are as follows:
- For specific SMA applications, such as actuators, cyclic training is required in order for the material to stabilize its response and memorize high and low temperature shapes without any applied force. For structural applications, however, such as SMA coating deposited on metallic substrates, no SMA training is required.
- Ni-Ti coatings were considered in two different compositions: (a) equiatomic, and (b) Ni-rich. It was found that Ni- rich composition is preferable since it offers tuneable transformation metrics, adequate resistance to TRIP, although its transformation strain is lower.
- It was found that while Ni-rich would be the material of choice for coating application, but equiatomic Ni-Ti still remains an alternative option, in case if the Ni-rich material fails to achieve all set requirements.
- A two-phase coating deposition process was proposed for good SME for SMA coating: depositing the SMA coating on the compressive side of a bended substrate and heating the structure at the final stage.
- Appropriate aging heat treatment is of paramount importance for the coating to obtain a shape memory effect. Optimal treatment conditions of Ni-rich SMAs have been identified.
The activities carried out by SUN in WP3 allowed to:
- Investigate experimentally effects of the spraying parameters on the SMA coating properties.
- Characterize the SMA coating applied by using HVOF deposition.
- Assess the shape memory effect of the sprayed NiTinol coating.
The work done in WP3 allowed to achieve:
- The best coating conditions, maximizing hardness and adhesions and minimizing roughness.
- Absence of chemical composition variation for the SMA coating deposited by HVOF technology.
- Absence of oxides and burning-out during HVOF spraying SMA coating.
- Preservation of the shape memory effect of the sprayed NiTinol coating.
Cranfield’s work in WP3 resulted in the following:
- Laser profilometer (LP) test rig consisted of scanCONTROL 2900-10/BL LP, linear magnetic encoder to capture high resolution data synchronous with movement of programmable traversing mechanism for provision of required position accuracy was developed. Final LP test rig was validated using standardised calibration samples and coated samples produced by consortium.
- Algorithm for surface roughness estimation from the scanned data was developed and implemented in LabVew for reading the LP data and performing the surface roughness estimation, featuring selectable detrending options for preventing errors in roughness estimation due to surface curvature.
The final design of the manipulating device was performed by EXIS in WP4, including all mechanical and electronic units and software. Requirements and technical specification for manipulator were determined resulting in its ability to integrate all necessary functions (clean, inspect, spray) in single device.
The novel image processing algorithm for defect detection in the coating based on higher order spectral estimator was developed in WP5 by Cranfield. The effectiveness of the proposed technique in defect detection in coatings is based on introduction of transience (non-linearity) to the scanned data by defects, which can be picked by the higher order spectral technique (one of its strength). This technique is also having a low sensitivity to noise, always present in the scanned data, providing an additional gain in efficiency in comparison to other techniques.
The novel decision making and defect mapping technique was also implemented by Cranfield for identifying and mapping defects on the original image. The technique is based on anomaly detection approach, which does not require training data from the materials with defects.
Trial tests of the developed algorithms have been performed on the composite material specimens with surface defects and demonstrated high sensitivity of the developed algorithm to the presence of the surface defects.
