Optimization of laser beam machining process parameters to minimize recast layer

This project focuses on improving the component quality after Laser Beam Machining (LBM) for HS188 (AMS5608), a superalloy critical in aerospace and power generation due to its high strength and corrosion resistance. LBM often leaves a recast layer, a residue of resolidified molten material on the side edges, which weakens the material’s strength and corrosion properties which are critical in such sensitive applications. To tackle this, the project aims to explore the use of Artificial Neural Networks (ANN) to optimize the LBM process settings, minimizing the recast layer. This involves experimenting with various LBM settings and analyzing outcomes with advanced tools like Scanning Electron Microscopes (SEM) to predict the optimum combination of settings. The expected benefits include enhanced component durability, reduced manufacturing costs, and improved product quality and reliability in high-stress applications

Faculty Supervisor:

Olanrewaju Ojo

Student:

Partner:

Magellan Aerospace

Discipline:

Engineering

Sector:

Advanced Manufacturing; Aerospace; Technology

University:

University of Manitoba

Program: