Date of Award
2014
Publication Type
Master Thesis
Degree Name
M.A.Sc.
Department
Mechanical, Automotive, and Materials Engineering
Keywords
Artificial Neural Networks, Design of Experiments, K- Mean Classification, Laser Cladding, Response Surface Methodology, Sensitivity Approach
Supervisor
Urbanic, Ruth
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Abstract
Laser cladding is an additive manufacturing technique involving deposition of powdered clad metal in successive 2D layers onto a substrate thereby creating surface coatings with enhanced material properties. Process and shape parameters contribute in defining the geometry of the clad bead; however, due to the highly coupled nature of the process, it is difficult to determine the relationship between parameters. This research predicts such parameters through development of a cognitive artificial intelligence system using artificial neural networks. A robust experimentation design process applying response surface methodology technique is adopted to collect the bead geometry data for various process configurations. Furthermore, the research identifies the extent of contribution of each factor and the impact of their interactions on the model output through ANOVA and sensitivity analysis. Lastly, a K-mean clustering algorithm is incorporated to identify optimal number of clusters present in the collected dataset on the basis of bead shape characteristics.
Recommended Citation
Aggarwal, Kush, "Investigation of Laser Clad Bead Geometry to Process Parameter Settings for Effective Parameter Selection, Simulation, and Optimization" (2014). Electronic Theses and Dissertations. 5223.
https://scholar.uwindsor.ca/etd/5223