Tag Archives: deep learning

Data extension-based analysis and application selection of process-composition-properties of die casting aluminum alloy

By user09/29/2024Aluminium-E, Technical Data-Ealuminum alloy, aluminum alloys, Aluminum Die casting, Applications, deep learning, Die casting, Draft, Mechanical Property, Microstructure, Review, STEP

Jian Yang ab, Bo Liu ab, Yunbo Zeng c, Yiben Zhang ab, Haiyou Huang de, Jichao Hong bShow moreAdd to MendeleyShareCite https://doi.org/10.1016/j.engappai.2024.108514Get rights and content Abstract This research aims to provide a solution to the scarcity and fragmentation of industrial data on die casting aluminum alloys. Quantifying the coupling between die casting process-composition-properties of aluminum alloys through small datasets, is a critical step in predicting part properties and optimizing process selection. To

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.