Prediction Of Mechanical Properties Research Articles

An integrated computation framework for predicting mechanical performance of single-phase alloys manufactured using laser powder bed fusion: A case study of CoCrFeMnNi high-entropy alloy

Establishing a process-structure-property (PSP) relationship is crucial for understanding and optimizing the mechanical properties of components via laser powder bed fusion (L-PBF) additive manufacturing (AM). However, the parameters pertinent to the PSP relationship are encapsulated in a “black box” with a high dimension, resulting in relying on trial-and-error approaches to elucidate hidden associations becoming impracticable, while the acquisition of microscopic data proves financially burdensome. Thus this paper presents an integrated computation framework based on machine learning (ML) for predicting the tensile yield strengths and post-yield hardening rates of single-phase alloys fabricated through L-PBF, for efficient process optimization purposes. Research herein focuses on single-phase alloys to be built via L-PBF, taking CoCrFeMnNi high-entropy alloy as a study case, and provide an efficient integrated computation solution. The methodology proposed in this paper generates as-built microstructures associated with the varying L-PBF process parameters via cellular automata (CA). Subsequently, the mechanical responses of these microstructures are estimated by employing crystal plasticity finite element (CPFE) simulations. In addition, the approach raised in the paper leverages particle-level microstructure descriptors instead of average feature descriptors for the entire representative volume element (RVE) structure in an ML framework to predict the characteristics of the newly randomly growing grains. This research demonstrate the practicality and instructiveness of the methodology for modeling realistic grains. Then, trained ML model exhibits promising accuracy for the prediction of mechanical properties. This work showcases an integrated computation framework combining advanced materials science and ML techniques for enhancing understanding of the L-PBF process and offering valuable insights into process optimization for L-PBF-built diverse materials.

Prediction of mechanical properties of high‐performance concrete and ultrahigh‐performance concrete using soft computing techniques: A critical review

AbstractA cement‐based material that meets the general goals of mechanical properties, workability, and durability as well as the ever‐increasing demands of environmental sustainability is produced by varying the type and quantity of individual constituents in high‐performance concrete (HPC) and ultrahigh‐performance concrete (UHPC). Expensive and time‐consuming laboratory experiments can be used to estimate the properties of concrete mixtures and elements. As an alternative, these attributes can be approximated by means of predictive models created through the application of artificial intelligence (AI) methodologies. AI approaches are among the most effective ways to solve engineering problems due to their capacity for pattern recognition and knowledge processing. Machine learning (ML) and deep learning (DL) are a subfield of AI that is gaining popularity across many scientific domains as a result of its many benefits over statistical and experimental models. These include, but are not limited to, better accuracy, faster performance, greater responsiveness in complex environments, and lower economic costs. In order to assess the critical features of the literature, a comprehensive review of ML and DL applications for HPC and UHPC was conducted in this study. This paper offers a thorough explanation of the fundamental terms and ideas of ML and DL algorithms that are frequently used to predict mechanical properties of HPC and UHPC. Engineers and researchers working with construction materials will find this paper useful in helping them choose accurate and appropriate methods for their needs.

Theoretical prediction of phase transition, mechanical and electronic properties of manganese diboride under pressure

Theoretical prediction of phase transition, mechanical and electronic properties of manganese diboride under pressure

Prediction of mechanical properties of LPBF built part based on process monitoring and Gaussian process regression

As a highly promising technology in additive manufacturing, the laser powder bed fusion has only limited application due to its low reproducibility. In this study, the image information of the 316L specimen after laser scanning and powder paving of each layer was acquired by a complementary metal–oxide–semiconductor industrial camera. The important features were selected, extracted and quantificated by analyzing the tensile test results. Finally, combined with the laser power, the quantified features were as input of a Gaussian process regression model based on optimization algorithm of grid search to predict the tensile strength of 316L specimen. The results show that the quantized image features have a significant improvement on the regression effect, and the coefficient of determination (R 2) is improved from 63% to 90.57% compared to using only the laser power as input.

Predicting altered bone biomechanics in juvenile mice: insights from microgravity simulation, loading interventions, and Raman Spectroscopy

BackgroundMicrogravity, a condition experienced in a spatial environment, poses unique challenges to the skeletal system, particularly in juvenile organisms. This study aimed to investigate alterations in bone biomechanics of juvenile mice due to unloading – that simulates microgravity in the laboratory—and the effects of a bone-loading intervention. We compared bone compositional and mechanical properties between 21-six-week-old C57Bl/6 from a control group (wild type) and a group that underwent a tail-suspension unloading protocol to mimic microgravity (MG). The second group (MG) experienced additional in vivo loading protocol (MG + LDG) on the right hind leg, where dynamic compressive loading was applied to the right knee using a custom-built loading device.ResultsOur results show that after two weeks, we successfully induced bone alterations by (i) decreasing the energy dissipated before fracture and (ii) decreasing the yield and maximum stress. In addition, we showed that Mineral to matrix component [ν1PO4/Amide I], Carbonate to Amide [CO3/Amide I], and Crystallinity [1/FWHM(ν1PO4)] are strongly linked in physiological bone but not in microgravity even after loading intervention. While Crystallinity is very sensitive to bone deformation (strain) alterations coming from simulated microgravity, we show that Carbonate to Amide [CO3/Amide I] – a common marker of turnover rate/remodeling activity—is a specific predictor of bone deformation for bone after simulated microgravity. Our results also invalidate the current parameters of the loading intervention to prevent bone alterations entirely in juvenile mice.ConclusionsOur study successfully induced bone alterations in juvenile mice by using an unloading protocol to simulate microgravity, and we provided a new Raman Spectroscopy (RS) dataset of juvenile mice that contributes to the prediction of cortical bone mechanical properties, where the degree of interrelationship for RS data for physiological bone is improved compared to the most recent evidence.

A model predicting mechanical properties of asphalt mixtures considering void ratio and loading conditions

The void ratio is an essential parameter in asphalt mixture design, which significantly affects asphalt mixtures' mechanical properties and road performance. The prediction model for mechanical properties will be established based on the Computed tomography (CT) scanning, strength, and fatigue test results in this research to reveal the relationship between the asphalt mixtures' mechanical properties and void ratio. Firstly, the voids' transverse and longitudinal distribution characteristics in asphalt mixtures of different gradations were investigated based on CT scanning. Then, the influences of void ratio, loading rates on the strength, and the impacts of void ratio and stress levels on the fatigue life were investigated by conducting indirect tensile tests. Finally, the mechanical properties prediction model was established for void ratio and loading conditions. Further, the feasibility of the model is verified in conjunction with existing research results. The results show that the void ratio calculated based on CT scanning is about 1.15 times the measured void ratio in the water weight method. The number of voids was uniformly distributed in the range of 15 mm∼50 mm in height and 10 mm∼40 mm in radius of the specimen. The asphalt mixtures' strength decreases with increasing void ratio and increases with increasing loading rate. The asphalt mixtures' fatigue life declines with increasing void ratio and stress level. The correlation coefficients of the established strength and fatigue prediction models were 0.963 and 0.976, respectively. The relative error between the measured strength, fatigue life, and predicted results is about 10.75% and 11.27%, respectively. This research provides a basis for designing high-performance asphalt mixtures by establishing the relationship between asphalt mixtures' microscopic and mechanical characteristics.

Temperature distribution analysis and mechanical property prediction of wire loops in the Stelmor air-cooling system

During the Stelmor air-cooling process, the temperature distribution has a significant impact on the wire loops' final mechanical properties. The temperature distribution during air-cooling process is accurately solved by establishing three-dimensional model and Computational Fluid Dynamics (CFD) simulation. It is also found that the heat transfer coefficient on the cross section of the wire loop is very different, with a difference of 70–100 Wm−2K−1. The average value of the surface heat transfer coefficient of the wire loops section is used to replace the surface heat transfer coefficient of the wire loops, which improves the temperature distribution calculation accuracy, which is not considered by previous scholars. The finite difference method is used to calculate the mathematical model of the temperature distribution during the Stelmor air-cooling process. It is found that the maximum temperature difference of the wire during the cooling process is 90–110 K. The prediction model of mechanical property of wire after air-cooling was established, and distribution of mechanical property of the wire loops was obtained, and the reason for the uneven distribution of mechanical property of the wire loops was found.

A robust finite strain isogeometric solid-beam element

In this work, an efficient and robust isogeometric three-dimensional solid-beam finite element is developed for large deformations and finite rotations with merely displacements as degrees of freedom. The finite strain theory and hyperelastic constitutive models are considered and B-Spline and NURBS are employed for the finite element discretization. Similar to finite elements based on Lagrange polynomials, also NURBS-based formulations are affected by the non-physical phenomena of locking, which constrains the field variables and negatively impacts the solution accuracy and deteriorates convergence behavior. To avoid this problem within the context of a solid-beam formulation, the Assumed Natural Strain (ANS) method is applied to alleviate membrane and transversal shear locking and the Enhanced Assumed Strain (EAS) method against Poisson thickness locking. Furthermore, the Mixed Integration Point (MIP) method is used to make the formulation more efficient and robust. The proposed novel isogeometric solid-beam element is tested on several single-patch and multi-patch benchmark problems, and it is validated against classical solid finite elements and first-order Lagrange solid-beam element. The results show that the proposed formulation can alleviate the locking effects and significantly improve the performance of the isogeometric solid-beam element. With the developed element, efficient and accurate predictions of mechanical properties of lattice-based structured materials can be achieved. The proposed solid-beam element inherits both the merits of solid elements e.g. flexible boundary conditions and of the beam elements i.e. higher computational efficiency.

Prediction of mechanical properties and fatigue life of nanosilver slurry in chip interconnection

This article investigates the failure mode and mechanism of copper pillar solder joints in temperature cycling experiments, focusing on a Cu/nano Ag/Cu solder joint structure. The hot pressing bonding condition at 300 °C with insulation for 10 s is chosen for the experiments. Based on the life test results, the thermal cycling fatigue life of the nanosilver solder joint is determined to be 2050 cycles. To gain further insights, finite element software ANSYS is employed to simulate nanosilver solder joints in flip chips, revealing the stress–strain distribution within the solder joints. The simulation utilizes the Anand viscoplastic constitutive model for the solder joint, providing a reasonable representation of the stress–strain behavior under thermal cycling load. Notably, the simulation highlights that the maximum stress and strain occur in the contact area between the solder joint and the copper column. To enhance accuracy, the calculation equation is refined using relevant experience, resulting in a prediction of the thermal fatigue life of nanosilver solder joints. This prediction aligns closely with the experimental results. The research outcomes not only contribute valuable insights into the behavior of nanosilver solder but also serve as a reference for its application in electronic packaging.

A brief review of machine learning-assisted Mg alloy design, processing, and property predictions

Owing to the hexagonal close-packed (HCP) crystal structure inherent in Mg alloys, strong basal texture can readily be induced through the processes of rolling or extrusion. The anisotropy of the texture of Mg alloys impacts their stamping and forming capabilities, limiting their use in certain applications. Microalloying and shear deformation are currently the most common methods of weakening the texture of Mg alloys. Many shearing processes have been extensively studied, and given that they require complex equipment and make it difficult to achieve mass production, major attention has turned to studying the design of microalloys. Traditional trial-and-error approaches for developing micro-alloying confront many challenges, including longer test cycles and increasing expenses. The rapid advancement of big data and artificial intelligence opens up a new channel for the efficient advancement of metallic materials, specifically the application of machine learning to aid in the design of Mg alloys. ML modeling can be used to find correlations between features and attributes in data, allowing for the development and design of high-performance Mg alloys. The article provides an extensive overview of machine learning applications in Mg alloys. These include the discovery of high-performance alloys, the selection of coating designs, the design of Mg matrix composites, the prediction of second phases, the microstructure modification, optimization of rolling or extrusion parameters, and the prediction of mechanical and corrosion properties. In conclusion, challenges and prospects for the rational design of alloys with machine learning support were discussed.

μ2mech: A software package combining microstructure modeling and mechanical property prediction

We have developed a graphical user interface (GUI) based package μ2mech to perform phase-field simulation for predicting microstructure evolution. The package can take inputs from ab initio calculations and CALPHAD (Calculation of Phase Diagrams) tools for quantitative microstructure prediction. The package also provides a seamless connection to transfer output from the mesoscale phase field method to the microscale finite element analysis for mechanical property prediction. Such a multiscale simulation package can facilitate microstructure-property correlation, one of the cornerstones in accelerated materials development within the integrated computational materials engineering (ICME) framework.

Machine learning assisted mechanical properties prediction of fine denier polyester fiber

In recent years, more and more attention has been attracted by fine denier polyester fibers in electronic information industry due to their unique structural and excellent mechanical properties. As known, the mechanical properties of fine denier polyester fibers have a close relationship with their melt-spinning process parameters, thus, it is worth further exploring the influence of process parameters on fiber mechanical properties (namely breaking strength, elongation at break and CV of breaking strength). This study aims to develop a novel prediction model based on the artificial neural network (ANN) to predict the key mechanical properties of fine denier polyester fibers. For this purpose, firstly, different specifications of fine denier polyester fibers with different mechanical properties are produced by adjusting the key melt-spinning parameters, namely spinning temperature, take-roll speed and metering pump speed. Then the experimental data is collected, analyzed and augmented with effect to be used for modeling. Next, a novel prediction model based on ANN is developed by importing the two-streams structure and two attention matrices to improve the feature abstraction for mechanical properties prediction. Lastly, different predictive performance indicators, namely root mean square error (RMSE), mean absolute percent error (MAPE) and the coefficient of determination (R2) are imported to evaluate the proposed model. For the total of three mechanical properties, the predicted results of the proposed model in terms of RMSE, MAPE and R2 are respectively 0.3499, 0.0906 and 2.9516.

Morphology and properties of Mg2Si phase modified by Ge in Mg-Si alloys

The modification effects of Ge on Mg2Si phase in a Mg-2.5Si alloy were studied by computational simulation and experimental investigation. Based on first-principles structural prediction and electronic structure calculation, Mg2(SixGe1-x) phases were structurally stable and had sufficient hardness and inherent toughness as a reinforcement phase. With the increase of Ge content, due to the preferential adsorption of Ge atoms on the {100} planes of Mg2Si crystal, the morphology of primary Mg2Si crystal changed from equilateral-dendrite to polygonal, and finally to quadrilateral. In Mg-2.5Si-0.5Ge alloy, Ge was almost completely dissolved in the Mg2Si phase, but a small amount of dispersed solute segregation with a size of 10–20 nm can be observed. In Mg-2.5Si-1Ge alloy, many Mg2Ge precipitates with a small amount of Si dissolved having a size of 20–50 nm and a lattice constant a of 0.644 nm were uniformly distributed in primary Mg2Si, and only one orientation relationship (OR) in form of [001]Mg2Ge || [001]Mg2Si, (001) Mg2Ge || (001)Mg2Si with a tiny rotational angle of 0.39° was found. The stiffness and hardness of Mg2Si phase decreased with the increase of Ge content. Despite Mg2(SixGe1-x) crystal models only considering the solid solution of Ge and neglecting segregation and precipitation, the theoretical predictions of mechanical properties remained consistent with experimental results. This agreement was primarily due to the unique OR between Mg2Ge precipitates and Mg2Si phase.

Modeling study on the mechanical performance of CFRP/Al single-lap rivet joints under hygrothermal environmental conditions

Carbon Fiber Reinforced Polymers (CFRP) and aluminum alloys, owing to their exceptional mechanical properties and lightweight attributes, are extensively used in aircraft manufacturing. However, prolonged exposure to a hygrothermal environment can compromise the mechanical integrity of composite joint structures. In this paper, accelerated aging experiments were designed to test the mechanical properties of CFRP after hygrothermal aging, a novel mechanical property prediction model tailored for the hygrothermal coupled environment is presented. The model accurately predicts the modulus and strength of CFRP after hydrothermal aging. A three-dimensional finite element model for the CFRP interference riveted structure, considering a tri-coupled state of moisture, temperature, and force was established by the application of subroutine and field superposition. The precision of this finite element model has been affirmed through accelerated aging tests. By integrating finite element analysis with experimental methods, this research delves into the failure modes and mechanisms of CFRP and its joints under hygrothermal conditions. It was discerned that the hygrothermal environment undermines the bond between fibers and the matrix, resulting in pronounced interlaminar delamination and shear failure in CFRP. The failure forms gradually change from “flaky” at lower levels of aging to “filamentary” at higher levels of aging. For CFRP riveted structures, the hygrothermal conditions influence their loadbearing capability and shift the primary positions of failure.

Artificial neural network and ANFIS approaches for mechanical properties prediction and optimization of a turbine blade treated by laser shock peening

This research article covers a comprehensive investigation on the prediction and optimization of residual stresses, plastic deformations and damage induced by laser shock peening (LSP) on a thin Ti-6Al-4 V leading edge sample of a turbine blade. The study is based on a two-part approach: the first part involves a numerical simulation of the LSP process and the characterization of its effects on the material. The finite element analysis (FEA) is used to simulate the complex interactions between laser parameters, material properties and mechanical response. This numerical analysis generates valuable insights into the residual stress distribution, the plastic deformation and the potential damage within the sample being treated. Based on the numerical results, the second part highlights the novel application of the artificial neural network (ANN) and the adaptive neuro-fuzzy inference system (ANFIS) methods. By taking advantage of the strength of ANN and ANFIS, the models are able to learn accurately from all the data generated by numerical simulations, allowing them to precisely predict and optimize the effects induced by LSP: residual stresses, plastic strains and damage. This paper provides a significant contribution to the field of materials science and engineering, by offering an in-depth comprehension of the LSP process impact on Ti-6Al-4 V leading-edge turbine blade specimens. The combined utilization of numerical analysis and artificial intelligence AI-driven techniques offers a comprehensive approach to optimize the LSP process, leading to the fatigue resistance improvement and service life extension of turbine blades and other critical aerospace components. The proven capability of ANN and ANFIS enables innovative solutions in aerospace engineering and surface treatment technologies, promoting the AI implementation in materials processing and design.

Prediction and modeling of mechanical properties of concrete modified with ceramic waste using artificial neural network and regression model

Prediction and modeling of mechanical properties of concrete modified with ceramic waste using artificial neural network and regression model

Prediction of Physical and Mechanical Properties of Heat-Treated Wood Based on the Improved Beluga Whale Optimisation Back Propagation (IBWO-BP) Neural Network

The physical and mechanical properties of heat-treated wood are essential factors in assessing its appropriateness for different applications. While back-propagation (BP) neural networks are widely used for predicting wood properties, their accuracy often falls short of expectations. This paper introduces an improved Beluga Whale Optimisation (IBWO)-BP model as a solution to this challenge. We improved the standard Beluga Whale Optimisation (BWO) algorithm in three ways: (1) use Bernoulli chaos mapping to explore the entire search space during population initialization; (2) incorporate the position update formula of the Firefly Algorithm (FA) to improve the position update strategy and convergence speed; (3) apply the opposition-based learning based on the lens imaging (lensOBL) mechanism to the optimal individual, which prevents the algorithm from getting stuck in local optima during each iteration. Subsequently, we adjusted the weights and thresholds of the BP model, deploying the IBWO approach. Ultimately, we employ the IBWO-BP model to predict the swelling and shrinkage ratio of air-dry volume, as well as the modulus of elasticity (MOE) and bending strength (MOR) of heat-treated wood. The benefit of IBWO is demonstrated through comparison with other meta-heuristic algorithms (MHAs). When compared to earlier prediction models, the results revealed that the mean square error (MSE) decreased by 39.7%, the root mean square error (RMSE) by 22.4%, the mean absolute percentage error (MAPE) by 9.8%, the mean absolute error (MAE) by 31.5%, and the standard deviation (STD) by 18.9%. Therefore, this model has excellent generalisation ability and relatively good prediction accuracy.

Enhancing understanding metal matrix composites through molecular dynamics simulation: A comprehensive review

Metal-matrix composites (MMCs) attracts have gained significant attention in recent years and are extensively utilized in aerospace field due to their exceptional properties. Molecular dynamics simulation are suitable for describing interface behavior and predicting the mechanical properties of MMCs. This paper provides a review of mechanical properties prediction, physical processes and strengthen mechanisms of MMCs through molecular dynamics simulations. Firstly, the potential functions and establishment methods of composite models are introduced. Subsequently, commonly used simulation software and post-processing methods are discussed. Various mechanical predictions are summarized to evaluate the effect of microstructure on the mechanical properties, and the physical processes and functionalized composites formed through self-assembly and encapsulation are introduced. Additionally, dislocation evolution and crack propagation are analyzed to investigate the strengthening mechanisms of MMCs. The reviews also covers multi-scale simulations that elucidate structure-property relationship, and proposes research direction for atomic-scale simulation.

Performance prediction and analysis of engineered cementitious composites based on machine learning

This study presents the implementation of machine learning (ML) techniques for mechanical properties prediction and analysis of polyethylene fiber-reinforced ECC (PE-ECC). A comprehensive database including different mechanical properties of PE-ECC was first constructed, with total 50 compressive strengths, 123 tensile strengths and 123 tensile strain capacities being assembled. Grey relational analysis was used to investigate the sensitivity of the critical parameters of PE-ECC’s mechanical properties. The evaluation results showed that the supplementary cementitious materials-to-binder ratio, water-to-binder ratio, sand-to-binder ratio, and fiber reinforcing index have significant effects on the mechanical properties of PE-ECC. Three representative ML techniques were utilized and demonstrated good predictive performance. A parametric study was further undertaken to quantify the effects of the selected parameters on the mechanical properties of PE-ECC based on the ML models. This study aims to help researchers and engineers estimate material properties of PE-ECC more effectively and provide supports for ECC design.

Prediction and design of mechanical properties of origami-inspired braces based on machine learning

In order to rapidly and accurately evaluate the mechanical properties of a novel origami-inspired tube structure with multiple parameter inputs, this study developed a method of designing origami-inspired braces based on machine learning models. Four geometric parameters, i.e., cross-sectional side length, plate thickness, crease weakening coefficient, and plane angles, were used to establish a mapping relationship with five mechanical parameters, including elastic stiffness, yield load, yield displacement, ultimate load, and ultimate displacement, all of which were calculated from load-displacement curves. Firstly, forward prediction models were trained and compared for single and multiple mechanical outputs. The parameter ranges were extended and refined to improve the predicted results by introducing the intrinsic mechanical relationships. Secondly, certain reverse prediction models were established to obtain the optimized design parameters. Finally, the design method of this study was verified in finite element methods. The design and analysis framework proposed in this study can be used to promote the application of other novel multi-parameter structures.