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

Lehmhus, Dirk

doi:10.3390/met14030334

The platform will undergo maintenance on Sep 14 at about 9:30 AM EST and will be unavailable for approximately 1 hour.

Metals

2024

DOI: 10.3390/met14030334

|View full text |Cite

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

Dirk Lehmhus

Abstract: The present text is the second part of an editorial written for a Special Issue entitled Advances in Metal Casting Technology [...]

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 297 publications

(342 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

The Formation of a Low-Carbon Steel/Ni-Cr-W Alloy Bimetallic Material via Liquid–Solid Compound Casting with a Laser Assisted Solid Surface

Salii,

Golovko,

Kaglyak

et al. 2024

Machines

View full text Add to dashboard Cite

The aim of this study was to develop a new manufacturing process for bimetallic materials by combining laser treatment with traditional casting methods. This process involves laser-treating nickel alloy-grade UNS 6230 plates to create a regular macro-relief on their surface. These treated plates are then placed in a sand mold, and molten non-alloy steel (S235JRG2) is poured into the mold to create bimetallic layered castings. The experimental procedure focuses on optimizing the melt-to-solid phase ratios and pouring temperatures to achieve a uniform microstructure and strong mechanical properties in the bimetals. The produced bimetallic castings are suitable for applications in the oil refining and chemical industries and heavy machinery sector. The quantitative results indicate that the optimized process parameters lead to a high-quality transition zone with minimal defects, characterized by the diffusion of alloying elements from the nickel alloy to the steel. The microstructure, chemical, and phase compositions were evaluated using XRD and SEM with EDS, confirming the formation of a robust metallurgical bond. Key findings include a significant improvement in the hardness and strength of the transition layer, with the optimal pouring temperature being 1600 °C. The resulting bimetallic materials demonstrate an improved performance in demanding industrial environments.

show abstract

The Formation of a Low-Carbon Steel/Ni-Cr-W Alloy Bimetallic Material via Liquid–Solid Compound Casting with a Laser Assisted Solid Surface

Salii,

Golovko,

Kaglyak

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

Automated Porosity Characterization for Aluminum Die Casting Materials Using X-ray Radiography, Synthetic X-ray Data Augmentation by Simulation, and Machine Learning

Bosse,

Lehmhus,

Kumar

2024

Sensors

View full text Add to dashboard Cite

Detection and characterization of hidden defects, impurities, and damages in homogeneous materials like aluminum die casting materials, as well as composite materials like Fiber–Metal Laminates (FML), is still a challenge. This work discusses methods and challenges in data-driven modeling of automated damage and defect detectors using measured X-ray single- and multi-projection images. Three main issues are identified: Data and feature variance, data feature labeling (for supervised machine learning), and the missing ground truth. It will be shown that simulation of synthetic measuring data can deliver a ground truth dataset and accurate labeling for data-driven modeling, but it cannot be used directly to predict defects in manufacturing processes. Noise has a significant impact on the feature detection and will be discussed. Data-driven feature detectors are implemented with semantic pixel Convolutional Neural Networks. Experimental data are measured with different devices: A low-quality and low-cost (Low-Q) X-ray radiography, a typical industrial mid-quality X-ray radiography and Computed Tomography (CT) system, and a state-of-the-art high-quality μ-CT device. The goals of this work are the training of robust and generalized data-driven ML feature detectors with synthetic data only and the transition from CT to single-projection radiography imaging and analysis. Although, as the title implies, the primary task is pore characterization in aluminum high-pressure die-cast materials, but the methods and results are not limited to this use case.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

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

Abstract: The present text is the second part of an editorial written for a Special Issue entitled Advances in Metal Casting Technology [...]

Cited by 2 publications

References 297 publications

The Formation of a Low-Carbon Steel/Ni-Cr-W Alloy Bimetallic Material via Liquid–Solid Compound Casting with a Laser Assisted Solid Surface

The Formation of a Low-Carbon Steel/Ni-Cr-W Alloy Bimetallic Material via Liquid–Solid Compound Casting with a Laser Assisted Solid Surface

Automated Porosity Characterization for Aluminum Die Casting Materials Using X-ray Radiography, Synthetic X-ray Data Augmentation by Simulation, and Machine Learning

Contact Info

Product

Resources

About