Determination of gradient residual stress for elastoplastic materials by nanoindentation

Shen, Ziyi; Dong, Ruipeng; Li, Jiao; Su, Yutai; Long, Xu

doi:10.1016/j.jmapro.2023.10.030

Cited by 53 publications

(3 citation statements)

References 42 publications

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“…As the melted material tries to solidify, it contracts inward, causing the surrounding material to compress along with it, resulting in an inward distortion of the metallic plate. The solid-state phase transition mechanism involves multiple phase transitions during the solidification process [22]. Depending on the cooling rate of certain materials, the ultimately solidified material may not have the same phase as it initially started with, resulting in the formation of residual stresses [23].…”

Section: Basics Of Residual Stress and Its Formationmentioning

confidence: 99%

Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review

Wu,

Tariq,

Joy

et al. 2024

Materials

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In recent decades, laser additive manufacturing has seen rapid development and has been applied to various fields, including the aerospace, automotive, and biomedical industries. However, the residual stresses that form during the manufacturing process can lead to defects in the printed parts, such as distortion and cracking. Therefore, accurately predicting residual stresses is crucial for preventing part failure and ensuring product quality. This critical review covers the fundamental aspects and formation mechanisms of residual stresses. It also extensively discusses the prediction of residual stresses utilizing experimental, computational, and machine learning methods. Finally, the review addresses the challenges and future directions in predicting residual stresses in laser additive manufacturing.

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Section: Basics Of Residual Stress and Its Formationmentioning

confidence: 99%

Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review

Wu,

Tariq,

Joy

et al. 2024

Materials

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“…In the realm of aluminium matrix composites, stir casting stands out as the preferred manufacturing method. Renowned for its affordability, adaptability, and scalability, stir casting remains a top choice due to its economic viability and capacity for large-scale production [10][11][12][13]. Stir casting stands as a competitive option among various casting methods in terms of pricing.…”

Section: Introductionmentioning

confidence: 99%

Tribological impact on borosilicate glass powder reinforced Al-MMC by variation of sliding velocity and the study of its machinability

Bhowmik,

Bhattacharjee,

et al. 2024

Phys. Scr.

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Now-a-days, metal matrix composites based on pure aluminium are widely used as a replacement for a wide range of applications thanks to their high strength-to-weight ratio, ductility, and thermal conductivity. Micron-sized (10-3 mm) borosilicate (mixer of Silica (SiO2) and Boron Oxide (B2O3)) glass particles were used to create a dispersion in an aluminium matrix composite (Al7075- borosilicate glass MMC) utilizing a stir casting technique while in a liquid condition. The present study examines the effect of borosilicate glass particle content (0, 3, 6, and 9 wt. %) and changes in sliding velocity (0.5, 1, 1.5, and 2 m/s) on the wear behaviour of a composite rubbing against an EN31 steel disc at a constant load of 20N and a sliding distance of 1000 m. Analysis of wear showed that the presence of borosilicate glass increased the wear rate and countered the trend shown in the coefficient of friction. In a similar vein, the sliding velocity of the counter plate rotated faster resulted in a higher coefficient of friction and a higher wear rate. A reliable mathematical model is developed to find the best combination of machining parameters for Al7075-borosilicate glass metal matrix composite (MMC). This model will consider important performance measures like surface smoothness and material removal rate. The authors will also use a sophisticated statistical method called the Taguchi L9 orthogonal array design of experiments (DOE) to understand how different machining settings affect how easily the composite can be machined. By analyzing the results from the DOE, it can optimize the machining parameters for efficient and cost-effective processing. This work can lead to manufacturing innovation in the future.

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“…Experimental trials provide firsthand, real-world data but can be time-consuming and expensive. Finite element analysis allows for virtual testing and optimization yet requires specialized software and extensive computational resources due to the iterative nature of this method [21,22]. Analytical simulation method is advantageous compared to finite element analysis but may result in over simplified solution [23,24].…”

Section: Introductionmentioning

confidence: 99%

3D fractal model with experimental analysis for assessing surface topography in EDM

Mahmood,

Ishfaq,

Sana

et al. 2024

Surf. Topogr.: Metrol. Prop.

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This work presents a novel three-dimensional fractal model designed specifically for morphological analysis of specimens made by the EDM. Within the 3D fractal framework, an algorithm is developed to estimate fractal parameters such as fractal dimension and periodic length. AISI 316 stainless steel, dielectric media, electrode materials, and powder variations were used in experimental trials to validate the 3D fractal model. Fixed pulse time ratios were used to control the EDM process. The specimen having the lowest fractal dimension, the shortest periodic length, the least amount of surface roughness, and the least amount of ten-point height was discovered to be the one machined using kerosene oil dielectric, brass electrode, graphite powder, and 1.0 pulse ratio time. On the other hand, the specimen machined using copper electrode, graphite powder, transformer oil dielectric, and 1.5 pulse ratio time produced the largest periodic length, maximum surface roughness, fractal dimension, and ten-point height. The developed 3D fractal model evaluates the EDM process well and provides insightful information on how to improve surface properties.

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Determination of gradient residual stress for elastoplastic materials by nanoindentation

Cited by 53 publications

References 42 publications

Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review

Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review

Tribological impact on borosilicate glass powder reinforced Al-MMC by variation of sliding velocity and the study of its machinability

3D fractal model with experimental analysis for assessing surface topography in EDM

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