Journal: International Journal of Automation Technology
Loading...
Abbreviation
Int. J. Automation Technol
Publisher
Fuji Technology Press
16 results
Search Results
Publications 1 - 10 of 16
- Orientation smoothing for 5-axis machining using quasi-redundant degrees of freedomItem type: Journal Article
International Journal of Automation TechnologySellmann, Florian; Haas, Titus; Nguyen, Hop D.; et al. (2016) - Increased productivity for redundant laser scanners using an optimal trajectory separation methodItem type: Journal Article
International Journal of Automation TechnologyHaas, Titus; Warhanek, Maximilian; Dietlicher, Michael; et al. (2016) - MPCC-based set point optimisation for machine toolsItem type: Journal Article
International Journal of Automation TechnologyHaas, Titus; Weikert, Sascha; Wegener, Konrad (2019) - Influence of the Anode Material and the Flushing Gas on the Dry Electrical Discharge Machining ProcessItem type: Journal Article
International Journal of Automation TechnologyRoth, Raoul; Beck, Lukas; Balzer, Hartmi; et al. (2013) - Special Issue on Advanced Material Driven Design of Machine ToolsItem type: Other Journal Item
International Journal of Automation TechnologyWegener, Konrad; Matsubara, Atsushi (2020) - Machine Tool Energy Efficiency – A Component Mapping-Based ApproachItem type: Journal Article
International Journal of Automation TechnologySchudeleit, Timo; Züst, Simon; Weiss, Lukas; et al. (2016) - Geometry optimisation for 2D cutting: A quadratic programming approachItem type: Journal Article
International Journal of Automation TechnologySellmann, Florian; Haas, Titus; Nguyen, Hop D.; et al. (2016) - Automatic Characterization of WEDM Single Craters Through AI Based Object DetectionItem type: Journal Article
International Journal of Automation TechnologyGonzalez Sanchez, Eduardo; Saccardo, Davide; Borges Esteves, Paulo; et al. (2024)Wire electrical discharge machining (WEDM) is a process that removes material from conductive workpieces by using sequential electrical discharges. The morphology of the craters formed by these discharges is in-fluenced by various process parameters and affects the quality and efficiency of the machining. To understand and optimize the WEDM process, it is essential to iden-tify and characterize single craters from microscopy images. However, manual labeling of craters is tedious and prone to errors. This paper presents a novel approach to detect and segment single craters using state-of-the-art computer vision techniques. The YOLOv8 model, a convolutional neural network-based object detection technique, is fine-tuned on a custom dataset of WEDM craters to locate and enclose them with tight bounding boxes. The segment anything model, a vision transformer-based instance segmentation technique, is applied to the cropped images of individual craters to delineate their shape and size. Geometric analysis of the segmented craters reveals significant variations in their contour and area depending on the energy set-ting, while the wire diameter has minimal influence. - Towards Decentralized Production: A Novel Method to Identify Flexibility Potentials in Production Sequences Based on Flexibility GraphsItem type: Journal Article
International Journal of Automation TechnologyBochmann, Lennart; Gehrke, Lars; Böckenkamp, Adrian; et al. (2015) - Thermally Caused Location Errors of Rotary Axes of 5-Axis Machine ToolsItem type: Journal Article
International Journal of Automation TechnologyGebhardt, Michael; Schneeberger, Alexander; Weikert, Sascha; et al. (2014)
Publications 1 - 10 of 16