Improving the Young’s modulus of Mg via alloying and compositing–A short review

Lightweight,high-modulus structural materials are highly desired in many applications like aerospace,automobile and biomedical instruments.As the lightest metallic structural material,magnesium(Mg)has great potential but is limited by its low intrinsic Young’s modulus.This paper reviews the investigations on high-modulus Mg-based materials during the last decades.The nature of elastic modulus is introduced,and typical high-modulus Mg alloys and Mg matrix composites are reviewed.Specifically,Mg alloys enhance Young’s modulus of pure Mg mainly by introducing suitable alloying elements to promote the precipitation of high-modulus second phases in the alloy system.Differently,Mg matrix composites improve Young’s modulus by incorporating high-modulus particles,whiskers and fibers into the Mg matrix.The modulus strengthening effectiveness brought by the two approaches is compared,and Mg matrix composites stand out as a more promising solution.In addition,two well-accepted modulus prediction models(Halpin-Tsai and Rule of mixtures(ROM))for different Mg matrix composites are reviewed.The effects of reinforcement type,size,volume fraction and interfacial bonding condition on the modulus of Mg matrix composites are discussed.Finally,the existing challenges and development trends of high-modulus Mg-based materials are proposed and prospected.

PREDICTION OF MECHANICAL PROPERTY OF WHISKER REINFORCED METAL MATRIX COMPOSITE: PART-II. VERIFICATION & APPLICATION

The present paper continues the discussion in Part I. Model and Formulation. Based on the theory proposed in Part I, the formulae predicting stiffness moduli of the composites in some typical cases of whisker orientations and loading conditions are derived and compared with theoretical representatives in literatures, experimental measurement and commonly used empirical formulae. It seems that (1) with whisker reinforcing and matrix hardening considered, the present prediction is in well agreement with the experimental measurement; (2) the present theory can predict accurate moduli with the proper pre calculated parameters; (3) the upper bound and lower bound of the present theory are just the predictions of equal strain theory and equal stress theory; (4) the present theory provides a physical explanation and theoretical base for the present commonly used empirical formulae. Compared with the microscopic mechanical theories, the present theory is competent for modulus prediction of practical engineering composite in accuracy and simplicity. [WT5”HZ]

Machine learning-based prediction of soil compression modulus with application of ID settlement

The compression modulus(Es)is one of the most significant soil parameters that affects the compressive deformation of geotechnical systems,such as foundations.However,it is difficult and sometime costly to obtain this parameter in engineering practice.In this study,we aimed to develop a non-parametric ensemble artificial intelligence(AI)approach to calculate the Es of soft clay in contrast to the traditional regression models proposed in previous studies.A gradient boosted regression tree(GBRT)algorithm was used to discern the non-linear pattern between input variables and the target response,while a genetic algorithm(GA)was adopted for tuning the GBRT model's hyper-parameters.The model was tested through 10-fold cross validation.A dataset of 221 samples from 65 engineering survey reports from Shanghai infrastructure projects was constructed to evaluate the accuracy of the new model5 s predictions.The mean squared error and correlation coefficient of the optimum GBRT model applied to the testing set were 0.13 and 0.91,respectively,indicating that the proposed machine learning(ML)model has great potential to improve the prediction of Es for soft clay.A comparison of the performance of empirical formulas and the proposed ML method for predicting foundation settlement indicated the rationality of the proposed ML model and its applicability to the compressive deformation of geotechnical systems.This model,however,cannot be directly applied to the prediction of Es in other sites due to its site specificity.This problem can be solved by retraining the model using local data.This study provides a useful reference for future multi-parameter prediction of soil behavior.

Machine Learning and Data Fusion Approach for Elastic Rock Properties Estimation and Fracturability Evaluation

Accurate rock elastic property determination is vital for effective hydraulic fracturing,particularly Young’s modulus due to its link to rock brittleness.This study integrates interdisciplinary data for better predictions of elastic modulus,combining data mining,experiments,and calibrated synthetics.We used the microstructural insights extracted from rock images for geomechanical facies analysis.Additionally,the petrophysical data and well logs were correlated with shear wave velocity(Vs)and Young’s modulus.We developed a machine-learning workflow to predict Young’s modulus and assess rock fracturability,considering mineral composition,geomechanics,and microstructure.Our findings indicate that artificial neural networks effectively predict Young’s modulus,while K-Means clustering and hierarchical support vector machines excel in identifying rock and geomechanical facies.Utilizing Microscale thin section analysis in conjunction with fracture modeling enhances our understanding of fracture geometries and facilitates fracturability assessment.Notably,fracturability is controlled by specific geomechanical facies during initiation and propagation and influenced by continuity of geomechanical facies in small depth intervals.In conclusion,this study demonstrates data mining and machine learning potential for predicting rock properties and assessing fracturability,aiding hydraulic fracturing design optimization through diverse data and advanced methods.