convolutional neural network

Automated Defect Detection through Flaw Grading in Non-Destructive Testing Digital X-ray Radiography

By user04/08/2025Aluminium-E, Technical Data-Ealuminum alloy, Aluminum Casting, Aluminum Die casting, CAD, convolutional neural network, deep learning, Die casting, Quality Control, Segment

This introduction paper is based on the paper “Automated Defect Detection through Flaw Grading in Non-Destructive Testing Digital X-ray Radiography” published by “MDPI”. 1. Overview: 2. Abstract: Process automation utilizes specialized technology and equipment to automate and enhance production processes, leading to higher manufacturing efficiency, higher productivity, and cost savings. The aluminum die casting industry

Improving Die-Casting Part Classification using Transfer Learning with Deep Convolutional Neural Networks

By user04/03/2025Aluminium-E, Technical Data-Ealuminum alloy, Aluminum Casting, Applications, CAD, convolutional neural network, deep learning, Die casting, Efficiency, Quality Control

This introduction paper is based on the paper “Improving Die-Casting Part Classification using Transfer Learning with Deep Convolutional Neural Networks” published by “IEOM Society International”. 1. Overview: 2. Abstract: Product quality is a crucial factor in the manufacturing process today, as it determines the company’s competitive advantages and the consumer’s requirements. The problem arises from

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

By user04/02/2025Aluminium-E, Technical Data-EAlloying elements, aluminum alloy, aluminum alloys, Aluminum Casting, CAD, casting microstructure inspection, convolutional neural network, deep learning, Die casting, Microstructure, Quality Control, secondary dendrite arm spacing, Segment

This introduction paper is based on the paper “Casting Microstructure Inspection Using Computer Vision: Dendrite Spacing in Aluminum Alloys” published by “Metals”. 1. Overview: 2. Abstract: This paper investigates the determination of secondary dendrite arm spacing (SDAS) using convolutional neural networks (CNNs). The aim was to build a Deep Learning (DL) model for SDAS prediction

Evaluation of the Microstructure and Properties of As-Cast Magnesium Alloys with 9% Al and 9% Zn Additions

By user03/19/2025Aluminium-E, Technical Data-EAlloying elements, Applications, CAD, convolutional neural network, Die casting, Electric vehicles, Magnesium alloys, Microstructure, Sand casting

This article introduces the paper “Evaluation of the Microstructure and Properties of As-Cast Magnesium Alloys with 9% Al and 9% Zn Additions” presented in Materials, MDPI. 1. Overview: Title: Evaluation of the Microstructure and Properties of As-Cast Magnesium Alloys with 9% Al and 9% Zn AdditionsAuthors: Lechosław Tuz, Vít Novák, and František TatíčekPublication Year: 2025Publishing

Predicting Die Cracking in Die-Cast Products Using a Surrogate Model Based on Geometrical Features

By user03/13/2025Aluminium-E, automotive-E, Technical Data-ECAD, convolutional neural network, deep learning, Die casting, Die Casting Congress, Efficiency, High pressure die casting, High pressure die casting (HPDC), temperature field

This introductory paper is the research content of the paper [“Predicting Die Cracking in Die-Cast Products Using a Surrogate Model Based on Geometrical Features”] published by [Computer-Aided Design & Applications] 1. Overview: 2. Abstracts or Introduction This paper explores the development and application of a surrogate model for predicting die cracks in die-cast products, focusing

Multi-Objective Optimization of Plate-Fin Heat Exchangers via Non-Dominated Sequencing

By user01/24/2025Aluminium-E, Technical Data-EApplications, CFD, cold plate, Computational fluid dynamics (CFD), convolutional neural network, Efficiency, Review, 자동차, 해석

This article introduces the paper “Multi-Objective Optimization of Plate-Fin Heat Exchangers via Non-Dominated Sequencing”. 1. Overview: 2. Research Background: Plate-fin heat exchangers are widely used for heat dissipation in automotive engines due to their compact and lightweight structure, excellent heat transfer performance, and low production cost. Serrated staggered fins are commonly employed to enhance the

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.

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