Effect of chemical composition on machining of Inconel 625/718 components by machine learning and microstructural analysis

APN in Quebec City faces a high fluctuation of the lifespan of tools in machining operations such as milling and turning. This hampers production planning, since the number of necessary tools for a job as well as their lifetime cannot be well predicted. Previous investigation on this challenge could not determine a reason for this phenomenon. Therefore, the objective of this project is to identify a correlation between the available process data such as material properties and machining parameters with the length of tool life. A special focus lies in the chemical composition of the materials used for the machining of the parts, superalloys Inconel 625 and Inconel 718, and its effect on machinability. In addition, different Machine Learning algorithms will be trained on historic data to allow predictions on tool changing intervals in the future.

Faculty Supervisor:

Stephen Yue;Yaoyao Fiona Zhao

Student:

Christina Maria Katsari

Partner:

APN Inc

Discipline:

Engineering

Sector:

Manufacturing

University:

McGill University