Process Capability Analysis of Additive Manufacturing Process through Predictive Model of Dimensional and Geometric Errors based on Machine Learning, M.Sc. Thesis Sharif University of Technology ; Khodaygan, Saeed (Supervisor)
Abstract
Additive manufacturing (AM)has gained extensive industrial and research attention in recent years. Reducing manufacturing waste, lead-time and costs and the ability to build surfaces and parts with complex shapes, assemblies all at once or parts with internal features are some benefits of AM. However, complex error generation mechanisms underlying AM digital physical chains are likely to result in geometrical inaccuracies of the final product, thus posing significant challenges to design and tolerancing for AM. Therefore, predictive modeling of shape deviations is critical for AM. With increasing volumes and varieties of data, machine learning has gained extraordinary popularity due to its...
Cataloging briefProcess Capability Analysis of Additive Manufacturing Process through Predictive Model of Dimensional and Geometric Errors based on Machine Learning, M.Sc. Thesis Sharif University of Technology ; Khodaygan, Saeed (Supervisor)
Abstract
Additive manufacturing (AM)has gained extensive industrial and research attention in recent years. Reducing manufacturing waste, lead-time and costs and the ability to build surfaces and parts with complex shapes, assemblies all at once or parts with internal features are some benefits of AM. However, complex error generation mechanisms underlying AM digital physical chains are likely to result in geometrical inaccuracies of the final product, thus posing significant challenges to design and tolerancing for AM. Therefore, predictive modeling of shape deviations is critical for AM. With increasing volumes and varieties of data, machine learning has gained extraordinary popularity due to its...
Find in contentBookmark
|
|