Machine learning in additive manufacturing: State-of-the-art and perspectives
Additive manufacturing (AM) has emerged as a disruptive digital manufacturing technology.
However, its broad adoption in industry is still hindered by high entry barriers of design for …
However, its broad adoption in industry is still hindered by high entry barriers of design for …
Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences
Artificial intelligence (AI) and Machine learning (ML) train machines to achieve a high level
of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily …
of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily …
Data-driven materials research enabled by natural language processing and information extraction
Given the emergence of data science and machine learning throughout all aspects of
society, but particularly in the scientific domain, there is increased importance placed on …
society, but particularly in the scientific domain, there is increased importance placed on …
Big-data science in porous materials: materials genomics and machine learning
By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …
[HTML][HTML] Applying neural-network-based machine learning to additive manufacturing: current applications, challenges, and future perspectives
Additive manufacturing (AM), also known as three-dimensional printing, is gaining
increasing attention from academia and industry due to the unique advantages it has in …
increasing attention from academia and industry due to the unique advantages it has in …
A state-of-the-art review on machine learning-based multiscale modeling, simulation, homogenization and design of materials
Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …
computational technology as well as engineering tools in material modeling and material …
Current challenges and opportunities in microstructure-related properties of advanced high-strength steels
This is a viewpoint paper on recent progress in the understanding of the microstructure–
property relations of advanced high-strength steels (AHSS). These alloys constitute a class …
property relations of advanced high-strength steels (AHSS). These alloys constitute a class …
Overview: Computer vision and machine learning for microstructural characterization and analysis
Microstructural characterization and analysis is the foundation of microstructural science,
connecting materials structure to composition, process history, and properties …
connecting materials structure to composition, process history, and properties …
Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization
Homogenization is a technique commonly used in multiscale computational science and
engineering for predicting collective response of heterogeneous materials and extracting …
engineering for predicting collective response of heterogeneous materials and extracting …
Few-shot steel surface defect detection
Deep learning-based algorithms have been widely employed to build reliable steel surface
defect detection systems, which are important for manufacturing. The performance of deep …
defect detection systems, which are important for manufacturing. The performance of deep …