Tag Archives: deep learning

Advances in Metal Casting Technology: A Review of State of the Art, Challenges and Trends—Part II: Technologies New and Revived

By user06/16/2024Aluminium-E, Technical Data-EAlloying elements, aluminum alloy, aluminum alloys, Aluminum Casting, Aluminum Die casting, ANOVA, Applications, AZ91D, CAD, CFD, Computer simulation, convolutional neural network, deep learning, Die casting, Efficiency, Forced convection stirring process, High pressure die casting, High pressure die casting (HPDC), IGES, Magnesium alloys, Mechanical Property, Microstructure, Review, Rheo-HPDC, Salt Core, Sand casting, Segment, STEP

by Dirk Lehmhus Fraunhofer Institute for Manufacturing Technology and Advanced Materials IFAM, Wiener Straße 12, 28359 Bremen, GermanyMetals 2024, 14(3), 334; https://doi.org/10.3390/met14030334Submission received: 25 February 2024 / Accepted: 8 March 2024 / Published: 14 March 2024(This article belongs to the Special Issue Advances in Metal Casting Technology) 1. Introduction It is a platitude that science and technology do not necessarily evolve along straight paths.

Casting Microstructure Inspection Using Computer Vision: Dendrite Spacing in Aluminum Alloys

By user05/31/2021Aluminium-E, Technical Data-EAlloying elements, aluminum alloy, aluminum alloys, Aluminum Casting, casting microstructure inspection, convolutional neural network, deep learning, Die casting, Microstructure, secondary dendrite arm spacing, Segment

by Filip Nikolić 1,2,3,Ivan Štajduhar 4,* andMarko Čanađija 1,*1Department of Engineering Mechanics, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia2Research and Development Department, CIMOS d.d. Automotive Industry, 6000 Koper, Slovenia3CAE Department, Elaphe Propulsion Technologies Ltd., 1000 Ljubljana, Slovenia4Department of Computer Engineering, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia*Authors to whom correspondence should be addressed. Abstract This paper