An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing

We present a novel unsupervised deep learning approach that utilizes an encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed to not only detect whether there exists an anomaly at a given time step, but also to predict what will happen next in the (sequential) process. We demonstrate our approach on a dataset collected from a real-world Additive Manufacturing (AM) testbed. The dataset contains infrared (IR) images collected under both normal conditions and synthetic anomalies. We show that our encoder-decoder model is able to identify the injected anomalies in a modern AM manufacturing process in an unsupervised fashion. In addition, our approach also gives hints about the temperature non-uniformity of the testbed during manufacturing, which was not previously known prior to the experiment

An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing

We present a novel unsupervised deep learning approach that utilizes an encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed to not only detect whether there exists an anomaly at a given time step, but also to predict what will happen next in the (sequential) process. We demonstrate our approach on a dataset collected from a real-world Additive Manufacturing (AM) testbed. The dataset contains infrared (IR) images collected under both normal conditions and synthetic anomalies. We show that our encoder-decoder model is able to identify the injected anomalies in a modern AM manufacturing process in an unsupervised fashion. In addition, our approach also gives hints about the temperature non-uniformity of the testbed during manufacturing, which was not previously known prior to the experiment

Geometrical Analysis of Simple Contours Deposited by a 3D Printing Hexacopter

Current limitations in vertical and horizontal mobility for ground robots in 3D printing of medium to large-scale objects have recently led to the development of a 3D printing hexacopter testbed at the University of Texas at Austin. This testbed can fly to a desired location and deposit polylactic acid on flat surfaces. A previous study has shown the feasibility of this approach but has not yet quantified the testbed's printing capabilities. In this paper, we quantify the printing capabilities. We print square contours of different sizes and quantify the printed results based on their geometric dimensions. We also quantify the testbed's trajectory tracking to assess the testbed's positioning accuracy during printing. In quantifying the testbed, we lay the groundwork for using aerial robots in printing applications of medium to large-scale objects, such as concrete printing.Mechanical Engineerin

Samenverzeichnis der Ernte 1976

Der Samenkatalog der Botanischen Gärten der Universität Bonn für das Jahr 1976. Botanische Gärten aus der ganzen Welt können im Rahmen des internationalen, kostenlosen Samentausch-Programms daraus bestellen

Samenverzeichnis der Ernte 1972

Der Samenkatalog der Botanischen Gärten der Universität Bonn für das Jahr 1972. Botanische Gärten aus der ganzen Welt können im Rahmen des internationalen, kostenlosen Samentausch-Programms daraus bestellen