Content

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   Table of contents
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   List of figures
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   List of tables
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   List of abbreviations
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   I Introduction
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   1 Introduction
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   1.1 Background
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   1.2 Motivation
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   1.3 Thesis Approach
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   1.4 Problem Statement
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   1.4.1 Manufacturability Analysis
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   1.4.2 3D Component Recognition
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   1.5 Solution Concepts
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   1.5.1 Manufacturability Analysis
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   1.5.2 3D Component Recognition
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   1.6 Overview of Disseminations
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   1.7 Structure of this thesis
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   II Additive Manufacturing
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  2 Foundations of Additive Manufacturing
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  2.1 Additive Manufacturing
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   2.1.1 Working Principle
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   2.1.2 Technologies
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   2.1.3 Advantages
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   2.1.4 Disadvantages
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   2.1.5 Applications
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   2.2 Process Chain of AM Service Providers
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   2.2.1 Individual Pre-processing
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   2.2.2 Batch Processing
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   2.2.3 Individual Post-processing
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  3 Problem Description
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  3.1 Manufacturability Analysis
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   3.1.1 Definition of Manufacturability
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   3.1.2 Current State and emerging issues
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   3.1.3 Solution Requirements
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   3.2 3D Component Recognition
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   3.2.1 Current State and emerging issues
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   3.2.2 Solution Requirements
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   III Foundations and Related Work
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   4 Foundations of Machine Learning and Computer Vision
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   4.1 Machine Learning
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   4.1.1 What is Machine Learning?
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   4.1.2 Types of Machine Learning
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   4.1.3 Structure of ML Algorithms
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   4.1.4 Algorithms
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   4.1.5 Transfer Learning
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   4.1.6 Learning from Synthetic Data
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   4.1.7 Data augmentation
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   4.2 Deep Learning
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   4.2.1 General Structure
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   4.2.2 Convolutional Neural Networks
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   4.2.3 Transfer Learning for Deep Learning
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   4.3 Explainable Artificial Intelligence
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   4.3.1 Necessity of Explainability
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   4.3.2 Methods for Explainability
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   4.3.3 Explainability of Deep Neural Networks
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  4.4 Computer Vision
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   4.4.1 Traditional Computer Vision
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   4.4.2 Machine Learning-based Computer Vision
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  5 Related Work
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   5.1 Manufacturability Analysis
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   5.1.1 Design Rule-based Analysis for AM
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   5.1.2 Simulation-based Analysis for AM
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   5.1.3 Machine Learning-based Analysis
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  5.2 3D Object Recognition
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   5.2.1 Object Recognition in Real-World Applications
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   5.2.2 3D Component Recognition in the AM Domain
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   IV Solution
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   6 Solution: Manufacturability Analysis
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   6.1 Solution Architecture
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   6.2 DL model: Spatial Hashing-based CNN
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   6.2.1 Perfect Spatial Hashing
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   6.2.2 CNN Operations with PSH
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   6.2.3 Memory consumption
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   6.3 Feedback System
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   6.3.1 Layerwise Relevance Propagation
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   6.4 Data Preprocessing
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   6.4.1 Data Augmentation
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   6.5 Decision Calculation
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   7 Solution: 3D Component Recognition
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   7.1 Solution Architecture
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   7.2 DL model: RotationNet
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   7.3 Physical Recognition Station
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   7.4 Training Data Generation
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   7.4.1 Virtual Environment
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   7.4.2 Physically Sound Orientations
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   7.4.3 Image Rendering
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   7.4.4 Image Augmentation
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   7.5 Training
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   7.6 Inference Data Pre-processing
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   V Realization, Evaluation and Conclusion
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  8 Realization and Evaluation: Manufacturability Analysis
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   8.1 Realization
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   8.1.1 Base Data Set
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   8.1.2 Evaluation Criterion
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   8.1.3 Data Set Labeling
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   8.1.4 Experimental Setup
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  8.2 Evaluation
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   8.2.1 Classification Accuracy
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   8.2.2 Visual Feedback
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   8.2.3 Secondary Requirements
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   8.3 Discussion
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   8.4 Summary
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   9 Realization and Evaluation: 3D Component Recognition
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  9.1 Realization
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   9.1.1 Base Data Sets
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   9.1.2 Training Data Generation
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   9.1.3 Experimental Setup
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   9.2 Evaluation
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   9.2.1 Recognition Rate
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   9.2.2 Secondary Requirements
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   9.3 Discussion
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   9.4 Summary
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  10 Conclusion and Future Work
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   10.1 Conclusion
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   10.1.1 Manufacturability Analysis
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   10.1.2 3D Component Recognition
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  10.2 Future Work
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   10.2.1 Manufacturability Analysis
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   10.2.2 3D Component Recognition
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   References