Digital Commons Network^™

Hybrid Theory-Machine Learning Methods For The Prediction Of Afp Layup Quality, Christopher M. Sacco

Theses and Dissertations

The advanced manufacturing capabilities provided through the automated fiber placement (AFP) system has allowed for faster layup time and more consistent production across a number of different geometries. This contributes to the modern production of large composite structures and the widespread adaptation of composites in industry in general and aerospace in particular. However, the automation introduced in this process increases the difficulty of quality assurance efforts. Industry available tools for predicting layup quality are either limited in scope, or have extremely high computational overhead. With the advent of automated inspection systems, direct capture of semantic inspection data, and therefore complete …

Prediction Of Residual Stress And Distortion In Laser Powder Bed Fusion Additive Manufacturing, Bhanuprakash Sairam Kosaraju

Theses and Dissertations

Additive Manufacturing (AM) has its proven advantages to unlock the design space and manufacturing capabilities for complex geometries with lightweights. Distortion is one of the most common defects that occur in Laser Powder Bed Fusion Additive Manufacturing (LPBFAM), which is caused by the significant residual stress during the printing process. This can lead to numerous process iterations to achieve the requisite form and fit tolerances.

In this study, Finite Element (FE) model that utilizes the element birth approach was developed to predict the residual stress and distortion in the LPBFAM process. The methodology leverages a simplified approach where the detailed …

Manufacturing Feature Recognition With 2d Convolutional Neural Networks, Yang Shi

Theses and Dissertations

Feature recognition is a critical sub-discipline of CAD/CAM that focuses on the design and implementation of algorithms for automated identification of manufacturing features. The development of feature recognition methods has been active for more than two decades for academic research. However, in this domain, there are still many drawbacks that hinder its practical applications, such as lack of robustness, inability to learn, limited domain of features, and computational complexity. The most critical one is the difficulty of recognizing interacting features, which arises from the fact that feature interactions change the boundaries that are indispensable for characterizing a feature. This research …