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Chinese Traditional and Herbal Drugs

Premier Chinese Biomedical Journal indexed in SinoBioData: Chinese Traditional and Herbal Drugs (中草药).

Total Research Papers: 30
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Published Research PapersFiltered: Year 2025 β€’ Vol. 328

Showing 2 of 30 peer-reviewed papers with full Graphical Abstracts.

Original ResearchVol. 328, Issue 3 β€’ pp. 118456DOI: 10.1016/j.jmatprotec.2025.118456

A Novel Approach to Enhancing Mechanical Properties of Additively Manufactured Ti-6Al-4V Alloy via In-Situ Microalloying with Boron

Authors: John A. Smith, Emily R. Johnson, Michael T. Brown, Sarah L. Davis

Additive manufacturing (AM) of Ti-6Al-4V alloy often results in a coarse columnar grain structure that degrades mechanical properties. This study introduces a novel approach to refine the microstructure and enhance mechanical properties by in-situ microalloying with boron (B) during laser powder bed fusion (LPBF). Ti-6Al-4V powders with 0.1 wt% and 0.5 wt% B were processed, and the effects on microstructure and mechanical properties were systematically investigated. Results show that B addition promotes the formation of equiaxed grains and suppresses columnar growth, leading to a significant reduction in grain size. The 0.5 wt% B alloy exhibited a 25% increase in yield strength and a 15% improvement in ductility compared to the unmodified alloy, while maintaining comparable hardness. Electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM) analyses revealed that the refinement is attributed to the formation of TiB precipitates that act as heterogeneous nucleation sites. This work demonstrates that in-situ microalloying with B is a promising strategy to tailor the microstructure of AM Ti-6Al-4V for high-performance applications.

A Novel Approach to Enhancing Mechanical Properties of Additively Manufactured Ti-6Al-4V Alloy via In-Situ Microalloying with Boron
Graphical Abstract
Original ResearchVol. 328, Issue 3 β€’ pp. 118456DOI: 10.1016/j.jmatprotec.2025.118456

Optimization of Process Parameters for Additive Manufacturing of Ti-6Al-4V Alloy Using Machine Learning and Multi-Objective Genetic Algorithm

Authors: John Smith, Emily Johnson, Michael Brown, Sarah Davis

Additive manufacturing (AM) of Ti-6Al-4V alloy is widely used in aerospace and biomedical industries due to its excellent mechanical properties and biocompatibility. However, the quality of AM parts is highly sensitive to process parameters, leading to defects such as porosity and residual stress. This study presents a systematic optimization framework combining machine learning (ML) and multi-objective genetic algorithm (MOGA) to determine optimal process parameters for laser powder bed fusion (LPBF) of Ti-6Al-4V. A dataset of 200 experimental runs was used to train and validate ML models, including random forest (RF), support vector regression (SVR), and artificial neural networks (ANN). The models predicted density, surface roughness, and tensile strength with high accuracy (RΒ² > 0.95). MOGA was then employed to find Pareto-optimal solutions balancing density, surface quality, and mechanical strength. The optimized parameters resulted in a 15% increase in tensile strength and a 30% reduction in surface roughness compared to baseline. The proposed framework demonstrates significant potential for accelerating process development and improving part quality in AM.

Optimization of Process Parameters for Additive Manufacturing of Ti-6Al-4V Alloy Using Machine Learning and Multi-Objective Genetic Algorithm
Graphical Abstract
Chinese Traditional and Herbal Drugs | SinoBioData Biomedical Archive | SinoBioData