Conference Proceeding
Refine
Year of publication
- 2022 (7)
- 2021 (6)
- 2020 (6)
- 2019 (12)
- 2018 (9)
- 2017 (11)
- 2016 (9)
- 2015 (14)
- 2014 (4)
- 2013 (3)
- 2012 (16)
- 2011 (9)
- 2010 (12)
- 2009 (16)
- 2008 (11)
- 2007 (10)
- 2006 (14)
- 2005 (4)
- 2004 (5)
- 2003 (7)
- 2002 (2)
- 2001 (1)
- 2000 (3)
- 1999 (1)
- 1997 (2)
- 1995 (1)
- 1994 (3)
- 1993 (2)
- 1992 (1)
- 1989 (2)
- 1986 (1)
- 1985 (2)
- 1984 (1)
Document Type
- Conference Proceeding (207) (remove)
Keywords
- Gamification (3)
- Additive manufacturing (2)
- Digital Twin (2)
- IO-Link (2)
- L-PBF (2)
- 10BASE-T1L (1)
- 3D-printing (1)
- Adaptive Systems (1)
- Additive Manufacturing (1)
- Arduino (1)
- Assessment (1)
- Asset Administration Shell (1)
- Augmented Reality (1)
- Binder Jetting (1)
- Business Simulations (1)
- Digital Twins (1)
- Directed Energy Deposition (1)
- Error Recovery (1)
- Ethernet (1)
- Fertigungsprozess (1)
Institute
- Fachbereich Maschinenbau und Mechatronik (207) (remove)
Pandaboard, TurtleBot, Kinect und Co. : Low-Cost Hardware im Lehreinsatz für die mobile Robotik.
(2012)
Mit freundlicher Genehmigung der Autoren und des Oldenbourg Industrieverlags https://www.oldenbourg-industrieverlag.de/de/9783835633223-33223 erschienen als Beitrag im Tagungsband zur AALE-Tagung 2012. 9. Fachkonferenz 4.-5. Mai 2012, Aachen, Fachhochschule. ISBN 9783835633223 S 8-1 S. 229-238 Original-Abstract des Autors: "Die mobile Robotik wird durch den Einsatz von Low-Cost Hardware einem breiten Publikum zugänglich. Bis vor kurzem basierte eine erschwingliche Hardware meist auf Mikrocontrollern mit den entsprechenden Leistungseinschränkungen z.B. im Bereich der Bildverarbeitung. Die Wahrnehmung einer 3D-Umgebung und somit die Möglichkeit zur autonomen Navigation wurde mit relativ kostenintensiver Hardware, z.B. Stereo-Vision-Systemen und Laserscannern gelöst. Die zur Auswertung der Sensorik notwendige Rechenleistung stand - entweder aufgrund des Stromverbrauchs oder der Performance meist für mobile Plattformen (lokal) - nicht zur Verfügung. Durch Einsatz von leistungsfähigen Prozessoren aus dem Bereich der Mobilgeräte (Smartphones, Tablets) und neuartigen Sensoren des Consumer-Bereichs, wie der Kinect, können mobile Roboter kostengünstig für den Einsatz in der Lehre aufgebaut werden.
Laser-based Additive Manufacturing (AM) processes for the use of metals out of the powder bed have been investigated profusely and are prevalent in industry. Although there is a broad field of application, Laser Powder Bed Fusion (LPBF), also known as Selective Laser Melting (SLM) of glass is not fully developed yet. The material properties of glass are significantly different from the investigated metallic material for LPBF so far. As such, the process cannot be transferred, and the parameter limits and the process sequence must be redefined for glass. Starting with the characterization of glass powders, a parameter field is initially confined to investigate the process parameter of different glass powder using LPBFprocess. A feasibility study is carried out to process borosilicate glass powder. The effects of process parameters on the dimensional accuracy of fabricated parts out of borosilicate and hints for the post-processing are analysed and presented in this paper.
Wir stellen hier exemplarisch STACK Aufgaben vor, die frei von der Problematik sind, welche sich durch diverse Kommunikationswege und (webbasierte) Computer Algebra Systeme (CAS) ergibt. Daher sind sie insbesondere für eine Open-Book Online Prüfung geeignet, da eine faire Prüfungssituation gewährleistet werden kann.
Objekt-orientierte Modellierung hybrider Systeme mit heuristischen und analytischen Merkmalen
(1994)
Additive Manufacturing (AM) of metallic workpieces faces a continuously rising technological relevance and market size. Producing complex or highly strained unique workpieces is a significant field of application, making AM highly relevant for tool components. Its successful economic application requires systematic workpiece based decisions and optimizations. Considering geometric and technological requirements as well as the necessary post-processing makes deciding effortful and requires in-depth knowledge. As design is usually adjusted to established manufacturing, associated technological and strategic potentials are often neglected. To embed AM in a future proof industrial environment, software-based self-learning tools are necessary. Integrated into production planning, they enable companies to unlock the potentials of AM efficiently. This paper presents an appropriate methodology for the analysis of process-specific AM-eligibility and optimization potential, added up by concrete optimization proposals. For an integrated workpiece characterization, proven methods are enlarged by tooling-specific figures.
The first stage of the approach specifies the model’s initialization. A learning set of tooling components is described using the developed key figure system. Based on this, a set of applicable rules for workpiece-specific result determination is generated through clustering and expert evaluation. Within the following application stage, strategic orientation is quantified and workpieces of interest are described using the developed key figures. Subsequently, the retrieved information is used for automatically generating specific recommendations relying on the generated ruleset of stage one. Finally, actual experiences regarding the recommendations are gathered within stage three. Statistic learning transfers those to the generated ruleset leading to a continuously deepening knowledge base. This process enables a steady improvement in output quality.