A Comparative Study of Visual Identification Methods for Highly Similar Engine Tubes in Aircraft Maintenance, Repair and Overhaul.

Unique identification of machine parts is critical to production and maintenance, repair and overhaul (MRO) processes in the aerospace industry. Despite recent advances in automating these identification processes, many are still performed manually. This is time-consuming, labour-intensive and prone to error, particularly when dealing with visually similar objects that lack distinctive features or markings or when dealing with parts that lack readable identifiers due to factors such as dirt, wear and discolouration. Automation of these processes has the potential to alleviate these problems. However, due to the high visual similarity of components in the aerospace industry, commonly used object identifiers are not directly transferable to this domain. This work focuses on the challenging component spectrum engine tubes and aims to understand which identification method using only object-inherent properties can be applied to such problems. Therefore, this work investigates and proposes a comprehensive set of methods using 2D image or 3D point cloud data, incorporating digital image processing and deep learning approaches. Each of these methods is implemented to address the identification problem. A comprehensive benchmark problem is presented, consisting of a set of visually similar demonstrator tubes, which lack distinctive visual features or markers and pose a challenge to the different methods. We evaluate the performance of each algorithm to determine its potential applicability to the target domain and problem statement. Our results indicate a clear superiority of 3D approaches over 2D image analysis approaches, with PointNet and point cloud alignment achieving the best results in the benchmark.

Towards Recognition of Human Actions in Collaborative Tasks with Robots: Extending Action Recognition with Tool Recognition Methods.

This paper presents a novel method for online tool recognition in manual assembly processes. The goal was to develop and implement a method that can be integrated with existing Human Action Recognition (HAR) methods in collaborative tasks. We examined the state-of-the-art for progress detection in manual assembly via HAR-based methods, as well as visual tool-recognition approaches. A novel online tool-recognition pipeline for handheld tools is introduced, utilizing a two-stage approach. First, a Region Of Interest (ROI) was extracted by determining the wrist position using skeletal data. Afterward, this ROI was cropped, and the tool located within this ROI was classified. This pipeline enabled several algorithms for object recognition and demonstrated the generalizability of our approach. An extensive training dataset for tool-recognition purposes is presented, which was evaluated with two image-classification approaches. An offline pipeline evaluation was performed with twelve tool classes. Additionally, various online tests were conducted covering different aspects of this vision application, such as two assembly scenarios, unknown instances of known classes, as well as challenging backgrounds. The introduced pipeline was competitive with other approaches regarding prediction accuracy, robustness, diversity, extendability/flexibility, and online capability.

Load-Oriented Nonplanar Additive Manufacturing Method for Optimized Continuous Carbon Fiber Parts.

The process of the additive manufacturing (AM) of carbon-fiber-reinforced polymer (CFRP) parts based on the process of fused deposition modeling (FDM) has seen considerable research in recent years, which amplifies the importance of adapted slicing and pathplanning methods. In particular, load-oriented techniques are of high interest when employing carbon fiber materials, as classical methods, such as tape-laying and laminating, struggle with highly curved and complex geometries and require the costly production of molds. While there have been some promising propositions in this field, most have restricted themselves to a planar slicing approach, which severely limits the ability to place the fibers along stress paths. In this paper, a nonplanar slicing approach is presented that utilizes principal stress directions to construct optimized nonplanar constituting layers on which pathplanning can be carried out. These layers are oriented such that the effect of the weak interlayer adhesion is minimized. Support material is adaptively generated to enable the use of arbitrary part geometry. Furthermore, a continuous pathplanning method and post-processor are applied to yield manufacturing instructions. The approach is verified for its viability of application through experimental investigation on a multi-axis robotic 3D printer. This constitutes an important step in allowing the fabrication of CFRP parts to further utilize the possibilities of additive manufacturing.