AIMC Topic: Materials Testing

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Predicting grain growth kinetic in steels using machine learning and XAI for mechanical properties.

PloS one
Understanding and controlling grain growth kinetics in steels is crucial for optimizing mechanical properties during thermomechanical processing. However, traditional empirical models often fail to account for the complex, nonlinear interactions betw...

Hybrid additive manufacturing and data-guided design optimization for graded anterior cruciate ligament engineering.

Biomedical materials (Bristol, England)
Interface tissues, such as the enthesis connecting ligaments to bone, present multiphasic architectures with continuous gradients in structure, composition, and mechanics. Engineering such complex transitions remains a major challenge in biofabricati...

Machine Learning for the Prediction of Size and Encapsulation Efficiency of mRNA-Loaded Lipid Nanoparticles Following a Postencapsulation Approach.

ACS applied bio materials
Lipid nanoparticles (LNPs) have gained significant attention thanks to their ability to encapsulate and deliver mRNA. Exploring a variety of lipid compositions and different preparation processes is essential for a better understanding of the mRNA en...

A collaborative approach of finite element method and machine learning algorithms for biomechanical analysis of implants used in tibial shaft fractures.

BMC musculoskeletal disorders
BACKGROUND: Tibial fractures are among the most common complex orthopedic injuries. The mechanical strength and biomaterial properties of implants used in the treatment of such fractures directly affect the healing process. In this study, the mechani...

Recellularization of scaffolds derived from precision-cut kidney slices.

Biomedical materials (Bristol, England)
The global rise in chronic kidney disease necessitates innovative solutions for end-stage renal disease that can help to overcome the limitations of the only available treatment options, transplantation and dialysis. Tissue engineering presents a pro...

Advancing sustainable concrete with bacterial self-healing technology and Kuhn-Tucker condition.

Scientific reports
This research investigates the self-healing potential of Bacillus subtilis in concrete due to its high capacity for calcium carbonate precipitation. Mathematical modelling and machine learning methods, i.e., Random Forest Method (RFM) and Kuhn-Tucker...

Analysis and prediction of the axial compression properties of desert sand concrete with steel tube restraint based on an improved BP neural network model.

PloS one
Accurate analysis and prediction of axial compression are important for ensuring the construction quality and safety of desert sand recycled aggregate concrete confined by steel tubes. In this study, the axial compressive strength and elastic modulus...

Machine learning driven optimization of compressive strength of 3D printed bio polymer composite material.

PloS one
3D printing has brought significant changes to manufacturing sectors, making it possible to produce intricate, multi-layered designs with greater ease. This study focuses on optimizing the compressive strength (CS) of functionally graded multi-materi...

Estimation of compressive strength of ultra-high performance lightweight concrete (UHPLC) using neural network.

PloS one
High strength and lightweight are key trends in concrete development. Achieving a balance between these properties to produce high structural efficiency (strength-to-weight ratio) concrete is challenging due to the complex relationship between compre...

An enhanced UHMWPE wear particle detection approach based on YOLOv9.

Medical engineering & physics
Ultra-high molecular weight polyethylene (UHMWPE) has been widely used in total joint arthroplasty for orthopedic and spinal implants. However, the biological response to UHMWPE wear particles has been identified as a major contributor to inflammator...