Degree Type

Dissertation

Date of Award

2020

Degree Name

Doctor of Philosophy

Department

Mechanical Engineering

Major

Mechanical Engineering; Computer Engineering

First Advisor

Adarsh Krishnamurthy

Abstract

Cybermanufacturing (CM) is a modern concept involving predictive analytic operations and information technology to aid the manufacturing industry in better decision making for design and manufacturing processes. This thesis presents a data-driven intelligent Cybermanufacturing framework for the effortless design and manufacturing of a product.

While traditional manufacturing systems are iterative and especially require skilled operators in the process, CM systems alleviate this issue by making intelligent predictions without specialists' involvement. CM systems operate with a network and data-rich environment involving interaction within and between virtual and physical spaces resulting in an effective decision support system. The broad objective of this research is to define and establish a framework of a cyber-physical system consisting of such virtual and physical systems to confront various departments of a manufacturing process.

The first stage in most of the iterative manufacturing processes is a product design that is compliant with certain design specifications and requirements. However, this is not a one-stop solution; to realize the final design, a product goes through multiple iterations between design and manufacturing stages to be compliant with the existing manufacturing paradigm. To tackle this issue, we have developed data-driven decision support for an intelligent design for manufacturing (DFM) framework using a volumetric representation (voxels) of 3D CAD models and deep neural networks to make high-quality predictions of the manufacturability of a part or product without requiring domain expertise of the user. We have developed a manufacturing process planning framework that detects such features irrespective of its size by hierarchically representing 3D CAD models as volumes on multiple scale levels (multi-level voxels) and facilitating scale-variant feature learning through the implementation of a multi-level Deep Neural Network to make decisions from hierarchical data.

Along with virtual decision support systems for design and manufacturing, CM systems also involve actual manufacturing in the physical space using machines and robotic environments. We have developed an automated manufacturing module that includes an algorithm for direct 3D printing from voxels and optimization based robust reinforcement algorithm.

DOI

https://doi.org/10.31274/etd-20200902-49

Copyright Owner

Sambit Ghadai

Language

en

File Format

application/pdf

File Size

165 pages

Available for download on Saturday, August 28, 2021

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