A new constitutive model to describe evolving elastoplastic behaviours of hexagonal close-packed sheet metals

This study develops a new phenomenological constitutive model to capture the unique evolving cyclic elastoplastic behaviours of hexagonal close-packed (HCP) sheet metals. This new constitutive model is developed by adopting the concepts of multiple-yield surface approaches. Four phenomenological deformation modes, including Monotonic Compression (MC), Monotonic Tension (MT), Reverse Compression (RC), and Reverse Tension (RT), are considered to represent the hardening evolution of the materials, including the twining/untwining behaviours. Reference flow stress equations are introduced, and a Cazacu-Barlat 2004 (CB2004) type yield surface is employed to each deformation mode. In addition, the RT hardening parameters are defined as functions of plastic pre-strains to mitigate the interpolation error caused by parameter determination processes of existing models. For validation, the calculated stress–strain curves of AZ31B magnesium alloy are compared with experimental curves available from literature. Moreover, to show the accuracy of the proposed analytical model, the reproduced stress–strain curves are compared with those of an existing model—the modified homogeneous anisotropic hardening (HAH) model. The obtained results show that the new constitutive model can successfully reproduce experimental Tension–Compression-Tension (TCT) and Compression-Tension–Compression (CTC) stress–strain curves of HCP sheet metals with considerably less percentage errors

Effects of tension-compression asymmetry on bending of steels

Stainless steels (SUS) and dual-phase (DP) steels have tension-compression asymmetry (TCA) in mechanical responses to full loading cycles. This phenomenon can significantly influence sheet metal forming of such metals, however, it is difficult to describe this behaviour analytically. In this research, a novel analytical method for asymmetric elastic-plastic pure bending using the Cazacu–Barlat 2004 asymmetric yield function is proposed. It only uses material parameters in tension along with an asymmetry coefficient related to the yield function. Bending operations of SUS304 and DP980 are investigated as two case studies. In the pure bending for both SUS304 and DP980, moment–curvature diagrams are analytically obtained. Furthermore, linear and nonlinear springback behaviours of SUS304 are analytically investigated. Moreover, using the analytical model as a user-defined material, a numerical model is developed for both steels under pure bending. In the V-bending case of SUS304 with and without TCA effects, the springback behaviours of the material are investigated numerically. In addition, considering friction effects, the analytical method is further modified for predicting springback behaviours in the V-bending of 16 types of SUS304 with various strengths are determined. All the analytical and numerical results have good agreement with those experimental results from literature for validation

Examination of expense and investment policy for centrally managed items in the Air Force and Navy

Approved for public release, distribution unlimitedMBA Professional ReportA basic principle of fiscal law is the identification of an object of expenditure as either an investment or expense; the identification then determines the proper appropriation and means through which the item is acquired. Part of the decision logic for an investment/expense determination is whether the items are centrally managed. The policies and practices surrounding central management of items varies across military departments and sometimes varies within a military department. This report documents various processes, as they exist today, and chronicles changes that occurred recently in the U.S. Air Force. Analyzing those processes indicates unclear policy direction, which leads to nonstandard implementation and problems with compliance. The presence of centralized information technology seems to lessen confusion and aid standardization of practices. Recommendations are offered for policy makers who may be considering changing policies

Numerical modelling of additive manufacturing process for stainless steel tension testing samples

Nowadays additive manufacturing (AM) technologies including 3D printing grow rapidly and they are expected to replace conventional subtractive manufacturing technologies to some extents. During a selective laser melting (SLM) process as one of popular AM technologies for metals, large amount of heats is required to melt metal powders, and this leads to distortions and/or shrinkages of additively manufactured parts. It is useful to predict the 3D printed parts to control unwanted distortions and shrinkages before their 3D printing. This study develops a two-phase numerical modelling and simulation process of AM process for 17-4PH stainless steel and it considers the importance of post-processing and the need for calibration to achieve a high-quality printing at the end. By using this proposed AM modelling and simulation process, optimal process parameters, material properties, and topology can be obtained to ensure a part 3D printed successfully

Engineering two-dimensional metal oxides and chalcogenides for enhanced electro- and photocatalysis

Two-dimensional (2D) metal oxides and chalcogenides (MOs & MCs) have been regarded as a new class of promising electro- and photocatalysts for many important chemical reactions such as hydrogen evolution reaction, CO2 reduction reaction and N2 reduction reaction in virtue of their outstanding physicochemical properties. However, pristine 2D MOs & MCs generally show the relatively poor catalytic performances due to the low electrical conductivity, few active sites and fast charge recombination. Therefore, considerable efforts have been devoted to engineering 2D MOs & MCs by rational structural design and chemical modification to further improve the catalytic activities. Herein, we comprehensively review the recent advances for engineering technologies of 2D MOs & MCs, which are mainly focused on the intercalation, doping, defects creation, facet design and compositing with functional materials. Meanwhile, the relationship between morphological, physicochemical, electronic, and optical properties of 2D MOs & MCs and their electro- and photocatalytic performances is also systematically discussed. Finally, we further give the prospect and challenge of the field and possible future research directions, aiming to inspire more research for achieving high-performance 2D MOs & MCs catalysts in energy storage and conversion fields

Material anisotropy in additively manufactured polymers and polymer composites : a review

Additive manufacturing (AM) is a sustainable and innovative manufacturing technology to fabricate products with specific properties and complex shapes for additive manufacturable materials including polymers, steels, titanium, copper, ceramics, composites, etc. This technology can well facilitate consumer needs on products with complex geometry and shape, high strength and lightweight. It is sustainable with having a layer-by-layer manufacturing process contrary to the traditional material removal technology—subtractive manufacturing. However, there are still challenges on the AM technologies, which created barriers for their further applications in engineering fields. For example, materials properties including mechanical, electrical, and thermal properties of the additively manufactured products are greatly affected by using different ways of AM methods and it was found as the material anisotropy phenomenon. In this study, a detailed literature review is conducted to investigate research work conducted on the material anisotropy phenomenon of additively manufactured materials. Based on research findings on material anisotropy phenomenon reported in the literature, this review paper aims to understand the nature of this phenomenon, address main factors and parameters influencing its severity on thermal, electrical and mechanical properties of 3D printed parts, and also, explore potential methods to minimise or mitigate this unwanted anisotropy. The outcomes of this study would be able to shed a light on improving additive manufacturing technologies and material properties of additively manufactured materials

Finite element modelling of high-porosity open-cell metal foams using a digital framework

Recently, metal foams are becoming popular in engineering due to their superior material properties, such as high stiffness with low specific weight and high compression strength with good energy absorption characteristics. Metal foams can be characterised by their cells or pores geometrically, such as their size, shape, spatial distribution, and regular/irregular/random arrangement. Apart from experimental study on metal foams, numerical modelling and simulations have been widely used to represent, fabricate, and characterise metal foams digitally in a material design sense. Most of metal foams have randomly-distributed structures, which create barriers for numerical modelling of such materials and their finite element analysis. As a result, various representative structures have been developed to model them. In this research, 2-D hexogen and 3-D Weaire–Phelan models are developed to model the open cell foams with random cell distributions. This devised 2-/3-D digital framework includes digital material representation and fabrication, finite element model generation and finite element analysis-based characterisation. For validation, the numerical results obtained from the numerical models are compared with those from experimental work and good agreements are found which demonstrates the effectiveness of the digital framework developed for metal foams

A machine learning model for predicting noise limits of motor vehicles in UNECE R51 regulations

It is vital to greatly reduce traffic noises emitted by motor vehicles during accelerating through determining limit values of noises and further improve technical specifications and comforts of these automobiles for automotive manufacturers. The United Nations Economic Commission for Europe (UNECE) R51 regulations define the noise limits for all vehicle categories, which are kept updating, and these noise limits are implemented by governments all over the world; however, the automobile manufactures need to estimate future values of noise limits for developing their next-generation vehicles. In this study, a machine learning model using the back-propagation neural network (BPNN) approach is developed to determine noise limits of a vehicle during accelerating by using historic data and predict its noise limits for future revisions of the UNECE R51 regulations. The proposed prediction model adopts the Levenberg-Marquardt algorithm which can automatically adapt its learning rate to train the model with input data, and at the same time randomly select the validation data and test data to verify the correlation and determine the accuracy of the prediction results. To showcase the proposed prediction model, acceleration noise limits from six historic data are used for training the model, and the noise limits at the seventh version can be predicted and validated. As the results achieve a required accuracy, vehicle noise limits in the next revision as the future eighth version can be predicted based on these data. It can be found that the obtained prediction results are much close to those noise limits defined in current regulations and negative error ratios are reduced significantly, compared to those values obtained using a quadratic regression model. As a result, the proposed BPNN model can predict future noise limits for the next revision of the UNECE R51automotive noise limit regulations

A theoretical study on pure bending of hexagonal close-packed metal sheet

Hexagonal close-packed (HCP) metals have quite different mechanical behaviours in comparison to conventional cubic metals such as steels and aluminum alloys [1, 2]. They exhibit a significant tension-compression asymmetry in initial yielding and subsequent plastic hardening. The reason for this unique behaviour can be attributed to their limited symmetric crystal structure, which leads to twining deformation [3-5]. This unique behaviour strongly influences sheet metal forming of such metals, especially for roll forming, in which the bending is dominant. Hence, it is crucial to represent constitutive relations of HCP metals for accurate estimation of bending moment-curvature behaviours. In this paper, an analytical model for asymmetric elastoplastic pure bending with an application of Cazacu-Barlat asymmetric yield function [6] is presented. This yield function considers the asymmetrical tension-compression behaviour of HCP metals by using second and third invariants of the stress deviator tensor and a specified constant, which can be expressed in terms of uniaxial yield stresses in tension and compression. As a case study, the analytical model is applied to predict the moment-curvature behaviours of AZ31B magnesium alloy sheets under uniaxial loading condition. Furthermore, the analytical model is implemented as a user-defined material through the UMAT interface in Abaqus [7, 8] for conducting pure bending simulations. The results show that the analytical model can reasonably capture the asymmetric tension-compression behaviour of the magnesium alloy. The predicted moment-curvature behaviour has good agreement with the experimental results. Furthermore, numerical results show a better accuracy by the application of the Cazacu-Barlat yield function than those using the von-Mises yield function, which are more conservative than analytical results