Discipline:
Field of study: Computer and Information Sciences, General (11.0101), Mechanical Engineering (14.1901), Materials Science (40.1001), Materials Engineering (14.1801),
-d

Completed years of study:

3

Language REQUIRED for training: (1 - excellent, 2 - good, 3 - fair)

Other requirements: Laptop

Kind of work:

Work description: Project: MAGE ANALYSIS AND MODELLING TO ASSESS MICROSTRUCTURES OF COATINGS: A MACHINE LEARNING APPROACH Project description:There exists a renewed interest in the use of coatings for wear resistance to extend the lifetime of machine components. Companies, such as Caterpillar, Cummins, Boeing, Airbus, among others, are interested in standardized procedures for quality control of coating products developed by spray shops all over the world. Similarly, in Ecuador, hydropower plants, Oil&Gas and mining industries demand the implementation of certified procedures to assure reliability on their applications. This demands the utilization of structural materials of enhanced tolerance to mechanical or chemical damage (wear/erosion or corrosion). In particular, thermal sprayed (TS) coatings allow to reinforce surfaces with dense and hard coatings with minimal heating effects during processing and high adhesion strength. In this project, we use additive manufacturing techniques (HVOF, high-velocity-oxygen-fuel) to deposit overlays of WC-Co with 9%Co, 12%Co and 17%Co over gray cast iron substrates. One sample of each composition will be mounted in epoxy resin and polished to determine the microstructure of the coating, hardness and stress-strain curves. The microstructure characterization includes SEM, EDS and XRD analysis. On the other hand, both microhardness and stress-strain curves of coatings are determined by indentation methods. Measurements of micro-hardness of each sample are carried out using a Wilson Hardness Vickers 432 SVD equipment using HV 0.3 kg. of load and a diamond indenter. The stress-strain curves will be obtained using Tabor´s method described for coating applications in (Choi et. al). The coatings will be indented in the cross section using a spherical indenter of 1/16 of an inch in diameter at loads of 3, 5, 10, 20 and 30 Kg: using the same instrument. The phases and features of the images are identified through Python codification based on its gray scale map. A mask and a binary opening are carried out. Activities: The main objective of this project is to develop an image analysis procedure using control vision and machine learning to identify key features on WC-Co coating microstructures by HVOF. The processed images will be used to predict mechanical properties by the finite element method. The comparison of the property prediction and the actual experimental measurements will assess the quality of the method of image analysis. The student will be part of a broader team of experts. To measure mechanical properties by indentation methods. To prepare samples by polishing methods. To conduct image analysis to identify phases: carbides, binder; and, pores and cracks, To model hardness measurement via the Finite Element Method FEM using ABAQUS onto the OOF processed images (desired).

Working environment:

Office work

Number of weeks offered:

min: 9 - max: 10

Within the time period:

01/06/2024 00:00:00 - 31/10/2024 00:00:00

Gross pay:

450.0 USD Monthly

Expected deductions:

0

Lodging will be arranged by:

IAESTE ECUADOR

Expected type of accommodation:

No data

Payment Method:

Frequency of Payment:

Canteen available at work:

Yes

Estimated cost of lodging:

250.0 USD NoData

Estimated cost of living (incl. lodging):

450.0 USD Monthly