Philipp Schlieper, M. Sc.,
03/2020 – 08/2020
Ambiguity in regard to classification problems often occurs in manufacturing due to the large number of mechanical components involved . Recent work in industry investigated the utilisation of deep learning to identify components based on images . Deep learning frameworks are able to learn feature representations and perform classification automatically from images . However, a large dataset is crucial to the performance of a deep learning model . Presently, most publicly available datasets are obtained from real scenes . However, manual annotation of image datasets is time-consuming. Moreover, understanding a component category not only depends on the shape of its elements, but also on its usage within the product . This encourages the use of synthetic data due to the controlled setting process and ability to automatically produce annotations along with data. For decades, synthetic data has been used for benchmarking purposes . With recent progress in deep learning, synthetic data has become increasingly popular for training models [8, 9, 10, 11]. Also, a GAN-based network trained on synthetic and real images has been shown to deliver outstanding performance when simulating real images (Shrivastava et al. ). Papon et al.  consider the estimation of the semantic pose problem in indoor scenes. In their work, the deep network trained on randomly generated synthetic indoor scenes produced excellent results when transferred to real test data. There is also a large body of work that used synthetic data for object detection [14, 15, 16]. From the state of the art mentioned above, there is still a lack of synthetic mechanical components datasets for training image classification models. The widespread use of CAD tools and the large datasets of component models they produce makes it possible to accelerate the collection of synthetic data of components . Therefore, this thesis considers a practical approach to augmenting training data for mechanical components classification tasks by utilising synthetic images of 3D printable components during the training process. To achieve this, large-scale datasets of real and synthetic images of 3D printable components will be produced and evaluated by deep learning classifiers. Based on the evaluation, the classifier performance in regard to minimising the amount of real images during the training process can be optimised by reweighting the ratio of real and synthetic samples or improving the realism of synthetic images using generative approaches.
The work will consist of the following main steps: 1. Reviewing the literature of synthetic data for machine learning frameworks.
2. Collecting and printing publicly available 3D printable models of simple mechanical components (e.g., screws, nuts, and washers)
3. Recording real dataset of the 3D printable components. Utilising the Blender 3D software toolset to produce synthetic images of the same instant of components and annotating them automatically.
4. Evaluating classifier (e.g., Classifier A offered by Schaeffler AG, Xception ) performance on different distributions of real and synthetic images.
5. Optimising classifier performance regarding minimising the amount of real images in the training process by reweighting samples or using SimGAN  or other generative models to improve the realism of synthetic images.
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