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Defect Detection For Additive Manufacturing With Machine Learning And Markov Decision Process, Rui Li May 2022

Defect Detection For Additive Manufacturing With Machine Learning And Markov Decision Process, Rui Li

Doctoral Dissertations

Additive Manufacturing (AM) is a quickly evolving manufacturing technique in recent years. One of the most essential steps is the quality control of it. This involves the defect detection of the products, which is one of the bottlenecks that affects the high quality of AM products. One promising solution to this problem is to detect the defects in-situ and make decisions on the fly. We adopted Machine Learning (ML) algorithms for defect detection and develop a Markov Decision Process (MDP) model to make decisions for AM process. Our main purpose is to save costs and time through early termination or …


Sustainability Analysis For New Technologies Of Biofuels And Additive Manufacturing, Nelson A. Granda Marulanda Aug 2017

Sustainability Analysis For New Technologies Of Biofuels And Additive Manufacturing, Nelson A. Granda Marulanda

Doctoral Dissertations

This work is a compendium of different sustainability analysis on new technologies. The first chapter performed a Life Cycle Analysis of a biofuel process where pine woodchips are used to create SynGas. The study was done using a 10 tons gasifier, the mass balance of the reaction and an input/output analysis. All the data obtained was input into a freeware LCA software and the results were compared to the use of regular produced natural gas. The second chapter creates a benchmark of energy intensity and presents a case using binder jetting technology. Different Additive Manufacturing technologies energy intensity was reported, …


Moldable Items Packing Optimization, Sima Maleki May 2013

Moldable Items Packing Optimization, Sima Maleki

Doctoral Dissertations

This research has led to the development of two mathematical models to optimize the problem of packing a hybrid mix of rigid and moldable items within a three-dimensional volume. These two developed packing models characterize moldable items from two perspectives: (1) when limited discrete configurations represent the moldable items and (2) when all continuous configurations are available to the model. This optimization scheme is a component of a lean effort that attempts to reduce the lead-time associated with the implementation of dynamic product modifications that imply packing changes.

To test the developed models, they are applied to the dynamic packing …