Quality and efficiency are pivotal indicators of a manufacturing company. Many companies are suffering from shortage of experienced workers across the production line to perform complex assembly tasks such as assembly of an aircraft engine. This could lead to a significant financial loss. In order to further reduce time and error in an assembly, a smart system consisting of multi-modal Augmented Reality (AR) instructions with the support of a deep learning network for tool detection is introduced. The multi-modal smart AR is designed to provide on-site information including various visual renderings with a fine-tuned Region-based Convolutional Neural Network, which is trained on a synthetic tool dataset. The dataset is generated using CAD models of tools augmented onto a 2D scene without the need of manually preparing real tool images. By implementing the system to mechanical assembly of a CNC carving machine, the result has shown that the system is not only able to correctly classify and localize the physical tools but also enables workers to successfully complete the given assembly tasks. With the proposed approaches, an efficiently customizable smart AR instructional system capable of sensing, characterizing the requirements, and enhancing worker\u27s performance effectively has been built and demonstrated --Abstract, page iii
