Computer experiment on superposition of strengthening effects of different particles

Zhu, A. W.; Csontos, A.; Starke, E.A.

doi:10.1016/s1359-6454(99)00077-4

Cited by 75 publications

(29 citation statements)

References 18 publications

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“…9) Table 4 includes the overall CRSS contributed by these four obstacles based on the linear (t o1 ) and Pythagorean (t o2 ) addition rules. Previous rigorous studies have tried the addition rule with an adjustable exponent q, or o q ¼ 1 q þ 2 q , 9,13,14) and q ¼ 1 and 2 would yield eqs. (3) and (4), respectively.…”

Section: Strengthening Analysesmentioning

confidence: 99%

Strengthening and Toughness of AZ61 Mg with Nano SiO<SUB>2</SUB> Particles

Hung

Huang

et al. 2006

Mater. Trans.

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The strengthening mechanisms and bending toughness of the AZ61 Mg based composites reinforced by nano SiO 2 particles are examined. The composites were prepared either by spray forming, ingot metallurgy, or powder metallurgy, followed by severe hot extrusion. The spray formed composites exhibit the best nano particle distribution and toughness, but the volume fraction of the nano particles that can be inserted is limited. The nano composites fabricated through the powder metallurgy method possess the highest strength due to the extra strengthening effect from the MgO phase. Strengthening analysis based on the Orowan strengthening mechanism can predict well the composite strength provided that the nano particles are in reasonably uniform dispersion. For composites containing higher nano particle volume fractions greater than 3%, the experimental strength data fall well below the theoretical predictions, suggesting poor dispersion of the reinforcement.

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Section: Strengthening Analysesmentioning

confidence: 99%

Strengthening and Toughness of AZ61 Mg with Nano SiO<SUB>2</SUB> Particles

Hung

Huang

et al. 2006

Mater. Trans.

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“…[24]), the modelling of strength of crystalline materials using theory of dislocation movement and dislocation pinning (by precipitates, grain boundaries) (e.g. [1,2,25]), as well as finite element (FE) modelling of mechanical properties (e.g. for composite material).…”

Section: 2mentioning

confidence: 99%

“…Hence, there exists a considerable body of work on modelling independent contributions of strength in alloys and composites but predictive modelling of properties of complex alloys and composites requires superposition of these contributions. This type of complex strength modelling has been performed, to a varying degree of detail, for several monolithic precipitation hardened alloys [3,4,5,25,52,54,55], and recently a detailed model of strengthening in precipitation hardened metal matrix composites (MMCs) has been reported by the present authors [56,57,58]. In the strength modelling approach adopted in the present paper, the strengthening of MMCs and monolithic alloys are ascribed to five mechanisms: i. Precipitation strengthening, which involves strengthening of grains due to GPB zones, δ' (Al 3 Li) phase and S′ (Al 2 CuMg) phase [59,60], with a small contribution due to β′ (Al 3 Zr) dispersoids.…”

Section: Physically-based Modellingmentioning

confidence: 99%

Predicting the Structural Performance of Heat-Treatable Al-Alloys

Starink

Sinclair

Reed

et al. 2000

MSF

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Advances in physically-based and adaptive numeric modelling (ANM) have lead to a significant elevation of the role of modelling in commercial alloy development and process optimisation. Within these two categories of modelling a wide variety of techniques exists, whilst hybrid approaches, taking advantage of the benefits of the both methods, are also being developed. In this paper various ANM techniques and physically-based models relevant for modelling and predicting the properties of heat treatable Al-based alloys are reviewed, and case studies involving the modelling of proof stress, toughness and conductivity of a range of Al-based alloys are presented. These case studies illustrate that successful modelling requires an in-depth understanding of the various modelling options. Selection of the optimum modelling approach is driven by a range of factors, such as size of database available (large databases are best analysed using adaptive numeric modelling approaches), and availability of sound physical understanding (the latter benefiting physically-based approaches). 1.Introduction Throughout the history of alloy development, the modelling of properties and microstructure-property relationships has lagged behind the commercialisation of new products and processes. In terms of agehardened Al-alloys, commercial aerospace applications were established by 1919, however the first basic models of age hardening, based on dislocation movement and dislocation pinning, were not developed until the 1940's [1,2], whilst the first detailed models of strengthening in multi-component Al-based alloys were only published in the 1980/90s [3,4,5]. In recent years, this time lag has been reduced, with modelling preceding or directly leading product development in some cases. A specific example is the formulation of new compositions of Ni-based superalloys by Rolls-Royce Plc. which are based on thermodynamic modelling, and the recent development of 7449 and 7040 Al-alloy plate which has been influenced by microstructure-property modelling [6]. The use of well-founded modelling approaches has clear advantages in directing product development in a cost effective manner.Whilst the above examples concern modelling through physical understanding of microstructures and microstructure development, modelling using adaptive numeric (AN) approaches such as artificial neural networks has recently made important advances. Current state-of-the-art adaptive numeric modelling combines flexible, adaptive algorithms for model construction with a rigorous mathematical/statistical basis [7,8].The purpose of the present paper is to highlight recent advances in physically-based and AN modelling, and to consider the relative benefits of the two approaches as well as hybrid approaches which use elements of both. In exploring this wide ranging and continuous field of modelling approaches we will start by introducing some of the better known techniques. On one side of the spectrum of modelling

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“…The critical angle between the arms of the dislocation corresponding to each obstacle was used as the breakaway criterion. Zhu et al 12,13 studied the strengthening effects due to unshearable spherical, rodlike, and platelike obstacles. In their simulation they introduced the criterion to determine the instability of the dislocation configuration in terms of the spacing of any two face-to-face bowed-out dislocation segments.…”

Section: Introductionmentioning

confidence: 99%

Effect of particle size distribution on strength of precipitation-hardened alloys

et al. 2004

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Aging of precipitation hardened alloys results in particle coarsening, which in turn affects the strength. In this study, the effect of particle size distribution on the strength of precipitation-hardened alloys was considered. To better represent real alloys, the particle radii were distributed using the Wagner and Lifshitz and Slyozov (WLS) particle size distribution theory. The dislocation motion was simulated for a range of mean radii and the critical resolved shear stress (CRSS) was calculated in each case. Results were also obtained by simulating the dislocation motion through the same system but with the glide plane populated by equal strength particles, which represent mean radii for each of the aging times. The CRSS value with the WLS particle distribution tends to decrease for lower radii than it does for the mean radius approach. The general trend of the simulation results compares well with the analytical values obtained using the equation for particle shearing and the Orowan equation.

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Computer experiment on superposition of strengthening effects of different particles

Cited by 75 publications

References 18 publications

Strengthening and Toughness of AZ61 Mg with Nano SiO<SUB>2</SUB> Particles

Strengthening and Toughness of AZ61 Mg with Nano SiO<SUB>2</SUB> Particles

Predicting the Structural Performance of Heat-Treatable Al-Alloys

Effect of particle size distribution on strength of precipitation-hardened alloys

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