Imperial College London

DrNanLi

Faculty of EngineeringDyson School of Design Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 8853n.li09 Website

 
 
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Location

 

1M03Royal College of ScienceSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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75 results found

Wang H, Ding Z, Chen X, Liu H, Li Net al., 2024, Experimental characterisation and constitutive modelling of the intra-ply tensile and shear properties of unidirectional fibre reinforced thermoplastics (UD FRTPs) under solid-state stamp forming conditions, Composites Part A: Applied Science and Manufacturing, Vol: 179, Pages: 108034-108034, ISSN: 1359-835X

Journal article

Attar HR, Lei Z, Li N, 2023, Deep learning enabled tool compensation for addressing shape distortion in sheet metal stamping, 14th International Conference on the Technology of Plasticity, Publisher: Springer, Cham

This paper presents a novel deep learning-based platform for addressing shape distortion in sheet metal stamping (e.g., springback, thermal distortion) by tool compensation. Conventional approaches to tool compensation involve computationally expensive Finite Element (FE) simulations to update tool geometries. In contrast, the proposed platform uses a generator network to create 3D tool geometries and an evaluator network to predict the resulting shape distortion and post-stamping thinning. The generated tool geometries are iteratively updated by a gradient-based optimisation technique in the direction of minimising shape distortion in the resulting component. The platform is demonstrated on a cold stamped U-channel component case study, which experiences severe shape distortion in the form of springback. The optimisation problem was formulated to find the optimum tool geometry that enables a desired U-channel geometry to be formed after springback by tool compensation, while meeting a maximum thinning constraint. The platform successfully optimised the tool geometry to compensate for springback in this setting, showcasing its effectiveness in improving manufacturing outcomes and product quality. The presented approach offers a superior method for addressing shape distortion in stamping processes, as compared to conventional FE simulation iterations or trial-and-error methods. This approach can efficiently and effectively compensate for arbitrarily complex tool geometries without requiring extensive process expertise.

Conference paper

Attar HR, Foster A, Li N, 2023, Implicit neural representations of sheet stamping geometries with small-scale features, Engineering Applications of Artificial Intelligence, Vol: 123, Pages: 1-21, ISSN: 0952-1976

Geometric deep learning models, like Convolutional Neural Networks (CNNs), show promise as surrogate models for predicting sheet stamping manufacturability but lack design variables essential for inverse problems like geometric optimisation. Recent developments in deep learning have enabled geometry generation from compact latent spaces that are suitable for optimisation. However, current methods do not accurately model small-scale geometric features that are crucial for stamping performance. This study proposes a new deep learning-based method to address this limitation and generate detailed stamping geometries for optimisation. Specifically, neural networks are trained to generate Signed Distance Fields (SDFs) for stamping geometries, where the zero-level-set of each SDF implicitly represents the generated geometry. A new training approach is proposed for generating SDFs of stamping geometries, which involves supervising geometric properties of the SDFs. A novel loss function is introduced that directly acts on the zero-level-set and places high emphasis on learning small-scale features. This approach is compared with the state-of-the-art approach DeepSDF by Park et al. (2019), which explicitly supervises SDF values using ground truth data. The geometry generation performance of networks trained using both approaches is evaluated quantitatively and qualitatively. The results demonstrate significantly greater geometric accuracy with the proposed approach, which can faithfully generate small-scale features. Further analysis of the new approach reveals an organised learned latent space and varying the network input generates high-quality geometries from this space. By integrating with CNN-based manufacturability surrogate models by Attar et al. (2021), this work could enable the first-ever manufacturability-constrained optimisation of arbitrary sheet stamping geometries, potentially reducing geometry design time and cost.

Journal article

Attar HR, Foster A, Li N, 2023, Development of a deep learning platform for sheet stamping geometry optimisation under manufacturing constraints, Engineering Applications of Artificial Intelligence, Vol: 123, Pages: 1-23, ISSN: 0952-1976

Sheet stamping is a widely adopted manufacturing technique for producing complex structural components with high stiffness-to-weight ratios. However, designing such components is a non-trivial task that requires careful consideration of manufacturing constraints to avoid introducing defects in the final product. To address this challenge, this research introduces a novel deep-learning-based platform that optimises 3D component designs by considering manufacturing capabilities. This platform was realised by developing a methodology to combine two neural networks that handle non-parametric geometry representations, namely a geometry generator based on Signed Distance Fields (SDFs) and an image-based manufacturability surrogate model. This combination enables the optimisation of complex geometries that can be represented using various parameterisation schemes. The optimisation approach implemented in the platform utilises gradient-based techniques to update the inputs to the geometry generator based on manufacturability information from the surrogate model. The platform is demonstrated using two geometry classes, Corners and Bulkheads, each having three geometry subclasses, with four diverse case studies conducted to optimise these geometries under post-stamped thinning constraints. The case studies demonstrate how the platform enables free morphing of complex geometries, while also guiding manufacturability-driven geometric changes in a direction that leads to significant improvements in component quality. For instance, one of the cases shows that optimising the complex component geometry can reduce the maximum thinning from 45% to satisfy the thinning constraint of 10%. By utilising the proposed platform, designers can identify optimal component geometries that ensure manufacturing feasibility for sheet stamping, reducing design development time and design costs.

Journal article

Zhu L, Li N, 2023, Springback prediction for sheet metal cold stamping using convolutional neural networks, 2022 Workshop on Electronics Communication Engineering (WECE 2022), Publisher: SPIE, Pages: 1-6

Springback is a crucial factor in cold stamping that causes geometric inaccuracy of the stamped component after removal of tools. This study, for the first time, presents a novel application of a Convolutional Neural Network (CNN) based surrogate model to predict the thinning and springback behaviours for cold stamping. Datasets were created based on two cold stamping case studies, i.e., a U-bending case and an outer car door panel stamping case. The datasets were then applied to train the CNN-based surrogate models. The results show that the surrogate models can achieve near indistinguishable full-field predictions in real-time when compared with the FE simulation results. The application of CNN in efficient springback prediction can be adopted in industrial settings to aid both conceptual and final component designs for designers without having manufacturing knowledge.

Conference paper

Zheng K, He Z, Qu H, Chen F, Han Y, Zheng J-H, Li Net al., 2023, A novel quench-form and in-die creep age process for hot forming of 2219 thin aluminum sheets with high precision and efficiency, Journal of Materials Processing Technology, Vol: 315, Pages: 1-13, ISSN: 0924-0136

This work introduces a novel forming process, named Quench-form and In-die Creep Age (short for QICA). It integrates a hot stamping by warm dies (named quench-form) with a fast in-die creep age to significantly improve the dimensional accuracy of the formed part with maintained post-form mechanical properties and reduced total processing time. The process principle and underlying mechanisms were revealed by experimental forming tests, tensile tests, TEM observations. The optimum processing temperature range has been determined, and the processing window for industrial significance has been discussed. Results showed that under a heat treatment condition of 240 °C × 5 min followed by 175 °C × 4 h, the panel spring-back is significantly reduced by 75%, from 21′ to 5′, compared to that under the T6 forming condition. The total processing time is also dramatically reduced from the conventional 12–4.5 h, increasing the overall production efficiency by 62.5%. From industry perspective, an 80 °C temperature operation window, from 160 °C to 240 °C, is suggested to guarantee a yield strength of > 280 MPa (93% of the T6 strength). The effectiveness of the process has also been verified by forming an aircraft engine inlet lip part. The maximum clearance between the formed part and die was reduced from 7.00 mm to 2.01 mm by replacing the HFQ® technique with the QICA technique. The proposed novel process delivers an alternative energy-efficient method to produce high-accurate extra-large parts that are beneficial for aircraft and aerospace OEMs.

Journal article

Zhu L, Wang X, Sun L, Hu Q, Li Net al., 2022, Optimisation of selective laser melted Ti6Al4V functionally graded lattice structures accounting for structural safety, Materials, Vol: 15, Pages: 1-26, ISSN: 1996-1944

This paper presents a new framework for lightweight optimisation of functionally graded lattice structures (FGLSs) with a particular focus on enhancing and guaranteeing structural safety through three main contributions. Firstly, a design strategy of adding fillets to the joints of body-centred cubic (BCC) type lattice cells was proposed to improve the effective yield stress of the lattices. Secondly, effective properties of lattice metamaterials were experimentally characterised by conducting quasi-static uniaxial compression tests on selective laser melted specimens of both Ti6Al4V BCC and filleted BCC (BCC-F) lattices with different relative densities. Thirdly, a yield stress constraint for optimising FGLSs was developed based on surrogate models quantifying the relationships between the relative density and the effective properties of BCC and BCC-F lattices developed using experimental results assisted by numerical homogenisation. This framework was tested with two case studies. Results showed that structural safety with respect to avoiding yield failure of the optimised FGLSs can be ensured and the introduction of fillets can effectively improve the strength-to-weight ratio of the optimised FGLSs composed of BCC type lattices. The BCC-F FGLS achieved 14.5% improvement in weight reduction compared with BCC FGLS for the Messerschmitt-Bölkow-Blohm beam optimisation case study.

Journal article

Attar HR, Foster A, Li N, 2022, Optimisation of panel component regions subject to hot stamping constraints using a novel deep-learning-based platform, The 19th International Conference on Metal Forming, Publisher: IOP Publishing, Pages: 1-11, ISSN: 1757-8981

The latest hot stamping processes can enable efficient production of complex shaped panel components with high stiffness-to-weight ratios. However, structural redesign for these intricate processes can be challenging, because compared to cold forming, the non-isothermal and dynamic nature of these processes introduces complexity and unfamiliarity among industrial designers. In industrial practice, trial-and-error approaches are currently used to update non-feasible designs where complicated forming simulations are needed each time a design change is made. A superior approach to structural redesign for hot stamping processes is demonstrated in this paper which applies a novel deep-learning-based optimisation platform. The platform consists of the interaction between two neural networks: a generator that creates 3D panel component geometries and an evaluator that predicts their post-stamping thinning distributions. Guided by these distributions the geometry is iteratively updated by a gradient-based optimisation technique. In the application presented in this paper, panel component geometries are optimised to meet imposed constraints that are derived from post-stamping thinning distributions. In addition, a new methodology is applied to select arbitrary geometric regions that are to be fixed during the optimisation. Overall, it is demonstrated that the platform is capable of optimising selective regions of panel component subject to imposed post-stamped thinning distribution constraints.

Conference paper

Brooks R, Wang H, Ding Z, Xu J, Song Q, Liu H, Dear J, Li Net al., 2022, A review on stamp forming of continuous fibre-reinforced thermoplastics, International Journal of Lightweight Materials and Manufacture, Vol: 5, Pages: 411-430, ISSN: 2588-8404

Continuous fibre-reinforced thermoplastics (FRTPs) are replacing metals in certain applications in the aerospace industry due to their superior properties e.g., high strength-to-weight ratio and good fatigue resistance. Adopting these lightweight materials in vehicles is a solution for improving vehicle efficiency across the transport industry. Among various manufacturing techniques for FRTP parts, stamp forming is one of the most advantageous when small structures and mass production are targeted. However, a significant barrier for this technique is the quality control of manufacturing. The current paper reviews the development of stamp forming technology, benefits of using such technology and the typical quality issues in stamp forming of FRTP parts. First, advantages of stamp forming, compared to other thermoforming techniques, are discussed, followed by a review of the historical development of the process. Second, deformation mechanisms of FRTPs during stamp forming are examined, with particular focuses on the frictional behaviour and testing thereof. Third, the main defects associated with stamp forming are considered, alongside suggestions towards reducing their presence. Finally, an extensive survey of the effect of process parameters on the mechanical properties of formed parts is included, with generally expected trends highlighted and methodologies for finding optimum conditions presented. Based on the thorough review of state-of-the-art stamp forming, future trends and research gaps to be tackled for widening the applicability of FRTP stamp forming are suggested.

Journal article

Attar HR, Li N, 2022, Optimisation of deep drawn corners subject to hot stamping constraints using a novel deep-learning-based platform, IOP Conference Series: Materials Science and Engineering, Vol: 1238, Pages: 012066-012066, ISSN: 1757-8981

State-of-the-art hot stamping processes offer improved material formability and therefore have potential to successfully form challenging components. The feasibility of components to be formed through these processes is dependent on their geometric design and its complex interactions with the hot stamping environment. In industrial practice, trial-and-error approaches are currently used to update non-feasible designs where simulation runs are needed each time a design change is made. These approaches make the design process resource intensive and require considerable numerical and process expertise. To demonstrate a superior approach, this study presents a novel application of a deep-learning-based optimisation platform which adopts a non-parametric geometric modelling strategy. Here, deep drawn corner geometries from different geometry subclasses were optimised to minimise wasted volume due to radii while avoiding excessive post-stamping thinning. A neural network was trained to generate families of deep drawn corner geometries where each geometry was conditioned on an input latent vector. Another neural network was trained to predict the thinning distributions obtained from forming these geometries through a hot stamping process. Guided by these distributions, the latent vector, and therefore geometry, was iteratively updated by a new gradient-based optimisation technique. Overall, it is demonstrated that the platform is capable of optimising geometries, irrespective of complexity, subject to imposed post-stamped thinning constraints.

Journal article

Zhu L, Sun L, Wang X, Li Net al., 2021, Optimisation of three-dimensional hierarchical structures with tailored lattice metamaterial anisotropy, Materials and Design, Vol: 210, ISSN: 0264-1275

This paper presents a new framework for optimising three-dimensional hierarchical structures with tailored relative densities and anisotropy of lattice metamaterials. The effective properties of the lattice metamaterials are characterised with numerical homogenisation. Artificial neural network based surrogate models are developed to quantitatively relate lattice struts radii with the effective properties of the lattice metamaterials to improve the computational efficiency of the framework. A new platform integrating user-defined functions with multiple robust and efficient commercial software is developed to implement the proposed optimisation framework. The framework and its implementation are tested using three case studies featuring multiple lattice types and configurations. Case study results show that, compared with results from classical topology optimisation and optimising quasi-isotropic lattice metamaterials, optimised structures composed of tailored anisotropic lattice metamaterials achieved superior structural efficiency. This is attributed to the concurrent optimisation of the intermediate relative densities and anisotropy in the lattice metamaterials. The optimised struts radii distributions approximately align with the paths of the principal stresses. It is also found that the orthogonal struts and diagonal struts especially contribute to the bending and torsion resistance of beams, respectively.

Journal article

Attar H, Li N, Foster A, 2021, A method for determining equivalent hardening responses to approximate sheet metal viscoplasticity, MethodsX, Vol: 8, Pages: 1-20, ISSN: 2215-0161

Recently developed elevated temperature metal forming technologies improve material formability and address the springback issues of cold forming. However, the behaviour of alloys at elevated temperatures is viscoplastic and therefore considerably more complex than under cold forming conditions. This complex behaviour creates a barrier for industrial designers when designing for elevated temperature processes, and therefore leads to these processes often being overlooked. To overcome this barrier, a new method is proposed here to determine simpler strain hardening responses that are equivalent to the viscoplastic responses of alloys at elevated temperatures. The equivalent hardening responses are expressed by single material hardening curves and their hardening exponents are taken as parameters to approximate sheet metal viscoplasticity. This method therefore makes it possible to develop early-stage design guidelines that consider different materials and stamping processes. The method was successfully applied to two viscoplastic alloys under hot stamping conditions to determine equivalent hardening responses. The feasibility of the method has been assessed through comparing finite element simulations using the determined equivalent material models with ones using viscoplastic models. The result showed that the thinning distributions obtained were consistent in both cases, providing evidence that the method can be applied to a range of component designs.•The proposed equivalent material models are simpler than existing viscoplastic material models and can be derived directly from experimental stress-strain data.•Creating design guidelines from equivalent hardening exponents enables a straightforward way to compare between cold and hot stamping capabilities. This comparison makes it possible to make manufacturing process and material selection decisions quickly and effectively at the onset of a design process.•Design guidelines enabled by the proposed m

Journal article

Xu Q, Nie Z, Xu H, Zhou H, Attar HR, Li N, Xie F, Liu X-Jet al., 2021, SuperMeshing: a new deep learning architecture for increasing the mesh density of physical fields in metal forming numerical simulation, Journal of Applied Mechanics, Vol: 89, Pages: 1-10, ISSN: 0021-8936

In stress field analysis, the finite element method is a crucial approach, in which the mesh-density has a significant impact on the results. High mesh density usually contributes authentic to simulation results but costs more computing resources. To eliminate this drawback, we propose a data-driven mesh-density boost model named SuperMeshingNet that uses low mesh-density as inputs, to acquire high-density stress field instantaneously, shortening computing time and cost automatically. Moreover, the Res-UNet architecture and attention mechanism are utilized, enhancing the performance of SuperMeshingNet. Compared with the baseline that applied the linear interpolation method, SuperMeshingNet achieves a prominent reduction in the mean squared error (MSE) and mean absolute error (MAE) on the test data. The well-trained model can successfully show more excellent performance than the baseline models on the multiple scaled mesh-density, including 2X, 4X, and 8X. Enhanced by SuperMeshingNet with broaden scaling of mesh density and high precision output, FEA can be accelerated with seldom computational time and cost.

Journal article

Reza Attar H, Li N, Foster A, 2021, A new strategy for developing component design guidelines for aluminium alloy corners formed through cold and hot stamping processes, Materials & Design, Vol: 207, ISSN: 0264-1275

In recent sheet metal forming research, efforts have been largely focused on determining optimum processing parameters, while intuitive guidelines to efficiently develop feasible component geometries are rarely considered. Consequently, there are currently no suitable design support tools that can guide component designers using the most recent, and therefore unfamiliar, sheet metal manufacturing technologies. This paper aims to address this bottleneck by proposing a strategy for the creation of early stage manufacturing design guidelines for the common limiting design requirement of deep corners. Aluminium alloys formed under both cold, and elevated temperature working conditions are considered. A new methodology to simplify the analysis of complex viscoplastic behaviour of aluminium alloys at elevated temperatures into an equivalent strain hardening response is presented. The effects and trends of the deep corner geometry and simplified material hardening characteristics on the post-form thinning distribution are identified. New equation sets are proposed which model the identified trends and enable the development of intuitive design maps. Following the approach proposed in this paper, an awareness of the available design envelope can be created at the early stages of a design process to guide component design, material, and manufacturing process selection decisions for deep corner geometries.

Journal article

Wang X, Zhu L, Sun L, Li Net al., 2021, Optimization of graded filleted lattice structures subject to yield and buckling constraints, Materials & Design, Vol: 206, Pages: 1-17, ISSN: 0264-1275

To reduce the stress concentration and ensure structural safety for lattice structure designs, in this paper, a new optimization framework is developed for the optimal design of graded lattice structures, innovatively integrating fillet designs as well as yield and buckling constraints. Both relative strut radii and fillet parameters are defined as design variables, for BCC and PC lattices. Numerical homogenization is employed to characterize the effective elastic constants and yield stresses of the lattice metamaterials. Metamaterial models are developed to represent the relationships between the metamaterial effective properties and lattice geometric variables. Yield and buckling constraints, based on modified Hill’s yield criterion as well as Euler and Johnson buckling formulae respectively, are developed as functions of lattice geometric variables. A new optimization framework is proposed with both yield and buckling constraints integrated. A case study on minimizing the compliance of a Messerschmitt-Bolkow-Blohm beam, composed of either BCC or PC lattices, is conducted. The yield and buckling constraints guarantee the structural safety of the optimized lattice beams. The optimized beams composed of filleted lattices, compared with non-filleted lattices in the corresponding type, show reduced proportions subject to high modified Hill’s stress (

Journal article

Attar HR, Zhou H, Foster A, Li Net al., 2021, Rapid feasibility assessment of components to be formed through hot stamping: A deep learning approach, Journal of Manufacturing Processes, Vol: 68, Pages: 1650-1671, ISSN: 1526-6125

The novel non-isothermal Hot Forming and cold die Quenching (HFQ) process canenable the cost-effective production of complex shaped, high strength aluminiumalloy panel components. However, the unfamiliarity of designing for the newprocess prevents its widescale adoption in industrial settings. Recent researchefforts focus on the development of advanced material models for finite elementsimulations, used to assess the feasibility of new component designs for theHFQ process. However, FE simulations take place late in design processes,require forming process expertise and are unsuitable for early-stage designexplorations. To address these limitations, this study presents a novelapplication of a Convolutional Neural Network (CNN) based surrogate as a meansof rapid manufacturing feasibility assessment for components to be formed usingthe HFQ process. A diverse dataset containing variations in component geometry,blank shapes, and processing parameters, together with corresponding physicalfields is generated and used to train the model. The results show that nearindistinguishable full field predictions are obtained in real time from themodel when compared with HFQ simulations. This technique provides an invaluabletool to aid component design and decision making at the onset of a designprocess for complex-shaped components formed under HFQ conditions.

Journal article

Wang H, Liu H, Ding Z, Li Net al., 2021, Experimental and constitutive modelling studies of semicrystalline thermoplastics under solid-state stamp forming conditions, Polymer, Vol: 228, Pages: 1-17, ISSN: 0032-3861

Experimental characterisation and constitutive modelling studies on the thermomechanical behaviour of thermoplastics, under solid-state stamp forming conditions, are required for understanding and optimising the stampforming process. In this paper, two semicrystalline thermoplastics, Polyamide 6 (PA6, or Nylon 6) and PolyEther-Ether-Ketone (PEEK) are studied via uniaxial tensile tests at temperatures between their glass transitiontemperatures (Tg ) and melting temperatures (Tm), and at different strain rates (0.001–50 /s). The temperatureand strain rate effects are analysed quantitatively to further understand the thermomechanical response of thesesemicrystalline thermoplastics. The results show that temperature has significant effects on the thermomechanical behaviour of thermoplastic polymers, while the strain rate effects are relatively marginal in theinvestigated strain rate range. In addition, a new physically-based viscoelastic-viscoplastic constitutive model isproposed to simulate the thermomechanical behaviour of both materials. The model shows good predictionaccuracy on tensile stress responses and provides an insight into the microstructural evolution of the semicrystalline thermoplastics; thus, it can be used to analyse solid-state stamp forming of pure semicrystallinethermoplastics and thermoplastic polymer matrix composites (TPMCs).

Journal article

Attar H, Zhou H, Li N, Foster Aet al., 2021, Rapid feasibility assessment of components to be formed through hot stamping: A deep learning approach, Journal of Manufacturing Processes, ISSN: 1526-6125

Journal article

Zhou H, Xu Q, Nie Z, Li Net al., 2021, A study on using image-based machine learning methods to develop surrogate models of stamp forming simulations, Journal of Manufacturing Science and Engineering, Vol: 144, Pages: 1-41, ISSN: 1087-1357

In design for forming, it is becoming increasingly significant to develop surrogate models of high-fidelity finite element analysis (FEA) simulations of forming processes, to achieve effective component feasibility assessment as well as process and component optimizations. However, surrogate models using traditional scalar-based machine learning methods (SBMLMs) fall short on accuracy and generalizability. This is because SBMLMs fail to harness the location information available from the simulations. To overcome this shortcoming, the theoretical feasibility and practical advantages of innovatively applying image-based machine learning methods (IBMLMs) in developing surrogate models of sheet stamp forming simulations are explored in this study. To demonstrate the advantages of IBMLMs, the effect of the location information on both design variables and simulated physical fields is firstly proposed and analyzed. Based on a sheet steel stamping case study, a Res-SE-U-Net IBMLM surrogate model of stamping simulations is then developed and compared with a baseline multi-layer perceptron (MLP) SBMLM surrogate model. The results show that the IBMLM model is advantageous over the MLP SBMLM model in accuracy, generalizability, robustness, and informativeness. This paper presents a promising methodology in leveraging IBMLMs as surrogate models to make maximum use of information from stamp forming FEA results. Future prospective studies that are inspired by this paper are also discussed.

Journal article

Attar HR, Zhou H, Li N, 2021, Deformation and thinning field prediction for HFQ® formed panel components using convolutional neural networks, International Deep-Drawing Research Group Conference (IDDRG 2021), Publisher: IOP Publishing, Pages: 1-11, ISSN: 1757-8981

The novel Hot Forming and cold die Quenching (HFQ®) process can provide cost-effective and complex deep drawn solutions through high strength aluminium alloys. However, the unfamiliarity of the new process prevents its widescale adoption in industrial settings, while accurate Finite Element (FE) simulations using the most advanced material models take place late in design processes and require forming process expertise. Machine learning technologies have recently been proven successful in learning complex system behaviour from representative examples and have the potential to be used as design support tools for new forming technologies such as HFQ®. This study, for the first time, presents a novel application of a Convolutional Neural Network (CNN) based surrogate to predict the deformation and thinning fields for variable deep drawn geometries formed using HFQ® technology. A dataset based on deep drawn geometries and corresponding FE results is generated and used to train the model. The results show that near indistinguishable full field predictions in real time are obtained from the surrogate when compared with HFQ® simulations. This technique can be adopted in industrial settings to aid in both concept and detailed component design for complex-shaped panel components formed under HFQ® conditions, without underlying knowledge of the forming process.

Conference paper

Ding Z, Wang H, Luo J, Li Net al., 2021, A review on forming technologies of fibre metal laminates, International Journal of Lightweight Materials and Manufacture, Vol: 4, Pages: 110-126, ISSN: 2588-8404

Fibre metal laminates (FMLs), as a class of hybrid material taking advantages of both metals and composites, have shown great promise as lightweight structural materials in the transportation industry. Accordingly, manufacturing technologies of FMLs are attracting increasing research interests. This review emphasises the developing technologies of forming FML components, with other aspects related to FML materials being briefly introduced. First, we provide an overall review of the historical background and recent developments of FMLs, their classifications, sheet fabrication processes, and their advantages and disadvantages. Then, various forming technologies are introduced in detail, with a particular focus on stamp forming, which is considered to be the most promising approach for the high-volume production of complex-shaped FML components. Furthermore, the deformation modes and defects in forming FMLs are analysed and the challenges encountered in the existing research are thoroughly discussed. Finally, studies on modelling and process simulation of forming FMLs are reviewed and discussed. Based on the comprehensive appraisal of various aspects, current research progress and challenges related to FMLs and their forming technologies are summarised and an outlook of further developments is discussed.

Journal article

Wang X, Zhu L, Sun L, Li Net al., 2021, A study of functionally graded lattice structural design and optimisation, 2020 6th International Conference on Mechanical Engineering and Automation Science (ICMEAS), Publisher: IEEE, Pages: 50-55

Artificially designed lattice based structures, enabled by additive manufacturing are promising in various engineering applications due to their high stiffness and strength with low density and attractive multifunctional properties. In this work, a robust framework has been developed for structural optimisation by generating graded lattice structures. The goal of optimisation was to achieve the minimum structural weight while satisfying the stiffness requirement. Periodic representative volume element (RVE) homogenisation method was employed to calculate the effective mechanical properties of a unit cell of the lattice structure. A metamaterial model was determined to represent the relationship between the effective elastic constants and the geometric parameter, i.e. the strut radius of quasi-isotropic BCC lattice unit cell. Mesh effect analysis was carried out to capture the optimal Finite Element (FE) mesh size for numerical simulation, taking into consideration of the trade-off between accuracy and efficiency. The optimisation process was conducted through commercial software optistruct by applying the feasible directions (MFD) algorithm for optimisation, to achieve the optimal distribution of lattice strut radii. In post-processing, local maximum radius values were applied to joints of lattice unit cells to avoid sharp changes of strut radii between adjacent unit cells. Finally, a case study of 3-point bending beam was conducted to examine this framework and it was found that the proposed optimisation framework is valid for design and optimising graded lattice structures.

Conference paper

Ganapathy M, Li N, Lin J, Bhattacharjee Det al., 2020, A feasibility study on warm forming of an as-quenched 22MnB5 boron steel, International Journal of Lightweight Materials and Manufacture, Vol: 3, Pages: 277-283, ISSN: 2588-8404

In this paper, the feasibility of a newly proposed forming method, being the warm forming of as-quenched 22MnB5 boron steels, was studied through a series of proof of concept experiments. To assess the material thermo-mechanical behaviours under the proposed forming conditions, first, the as-received 22MnB5 boron steel was austenized and quenched to below the martensite transformation finish temperature to obtain a martensitic microstructure; second, uniaxial tensile tests of the as-quenched steel were conducted under proposed warm forming conditions on a Gleeble 3800 materials simulator. To evaluate the material post-form properties, first, tempering treatments on the as-quenched steel samples were performed to simulate the heat-treating conditions in the proposed warm forming process; second, the mechanical properties (hardness, strength, and ductility) of as-tempered samples were measured and a microstructure analysis was conducted. From the experimental results, it was found that, under the proposed warm-forming process conditions (420 °C–620 °C), the material showed significant strain softening, which would increase the tendency of necking during stamping and adversely affect its drawability. In addition, it was found that the heating of martensite in a 22MnB5 boron steel to higher temperatures (>400 °C) adversely affected its post-form strength and ductility due to the tempering effect. Therefore, according to the results obtained in this study, the warm forming of as-quenched 22MnB5 boron steel may reduce the strength of formed parts by more than 50% in comparison to the possible strength the material could achieve under the investigated process.

Journal article

Tian F, Li N, 2020, Investigation of the feasibility of a novel heat stamping process for producing complex-shaped Ti-6Al-4V panel components, Procedia Manufacturing, Vol: 47, Pages: 1374-1380, ISSN: 2351-9789

A novel cost-efficient Heat Stamping (HS) process, combining Heat treatment and fast Stamping, was proposed to produce complex-shaped titanium alloy panel components with low energy consumption and short cycle time. To investigate the feasibility of the HS process for forming Ti-6Al-4V, the stress-strain behaviours of the material under step quenching treatments in HS processes were investigated and compared with those under direct heating treatments in Hot Forming (HF) processes, through uniaxial tensile tests at the temperature range of 800 - 950 °C and the strain rate range of 0.01 - 5 /s. To reduce the strain softening, step quenching treatment was designed by soaking the specimen at 950 °C and fast quenching it to 800 °C for forming. It was found that strain softening in the step quenching tests was reduced as compared to direct heating tests at the strain rate of 1 /s; strain hardening was observed in step quenching test at the strain rate of 0.1 /s, achieved by enhancing the dynamic phase transformation from β phase to secondary α (αs) during deformation. Strain rate hardening of the material under step quenching treatment was found to be higher than those under direct heating treatment at the same temperature of 800 °C. To evaluate the novel HS concept, Heat Stamping experiments under step quenching treatments were carried out by using a drawability tool set. A cup-shaped demonstrator with the drawing ratio of 1.3 was produced to prove the feasibility of HS process.

Journal article

Hooper P, Li N, JIANG JUN, LIN J, BAI Qet al., 2020, Method of creating a component using additive manufacturing, US20200055121A1

Images (4) Classifications B22F3/24 After-treatment of workpieces or articlesView 19 more classificationsUS20200055121A1

Patent

Ganapathy M, Li N, Lin J, Abspoel M, Bhattacharjee Det al., 2019, Experimental investigation of a new low-temperature hot stamping process for boron steels, International Journal of Advanced Manufacturing Technology, Vol: 105, Pages: 669-682, ISSN: 0178-0026

This paper demonstrates the promise of a new low-temperature hot stamping process with pre-cooling for 22MnB5 boron steels. It is the first time for the new process being successfully implemented for producing an automotive demonstrator component assisted with thorough experimental studies. The studies mainly include hot forming experiments carried out on an industrial prototyping line, post-form examinations, and in-die quenching tests. Automotive B-Pillar components with two designed drawing depths (50 and 64 mm) were hot stamped at a wide range of temperatures and forming speeds, through both the conventional hot stamping processes and the new processes with pre-cooling applied. For the as-formed B-Pillars, 3D shape scanning was conducted to investigate the thickness distribution of the components; uniaxial tensile testing, hardness testing, and scanning electron microscopes (SEM) observation were conducted to assess the final mechanical properties and microstructures. To understand the benefit of the low-temperature hot stamping in reducing cycle time, a separate set of in-die quenching experiments were designed and carried out, with combinations of three different process parameters: workpiece start quenching temperature, initial tool temperature, and die-workpiece contact pressure. The results of this work confirmed that low-temperature hot stamping could be performed successfully in producing complex-shaped components, such as automotive B-Pillars, with much reduced cycle time.

Journal article

Zheng K, Zhu L, Lin J, Dean TA, Li Net al., 2019, An experimental investigation of the drawability of AA6082 sheet under different elevated temperature forming processes, Journal of Materials Processing Technology, Vol: 273, ISSN: 0924-0136

The performed research has, for the first time, investigated and compared the drawability of AA6082 at a comparable temperature range between two elevated temperature forming processes: termed (i)Low Temperature Hot Form and Quench (LT-HFQ®)or pre-cooled HFQ®, patented by Adam et al. (2015)and (ii)Direct Heating Aluminium Forming (DHAF)which represents a refinement of conventional warm forming targeting a higher temperature range. A series of uniaxial tensile and cylindrical deep drawing experiments were conducted. According to uniaxial tensile test results, the most obvious work-hardening and reasonable ductility was observed under LT-HFQ® at a deformation temperature of 350 °C and strain rate of 1 s−1, which can enhance drawability. For deep drawing experiments, it was found that preheating conditions of each process prior to forming significantly affected forming characteristics and post-formed hardness of the alloy; both the achieved maximum Draw ratio (DR)and limit Blank Holding Force (BHF)at some specific process parameters were increased under LT-HFQ®. Forming speed and temperature had significant effects on alloy deformation and thus drawability for both processes. In addition, by evaluating the post-formed hardness, process drawability and microstructural evolutions under different processes were simultaneously analyzed.

Journal article

Li N, Mohamed M, Lin J, 2019, METHOD FOR PREPARING AND FORMING SHEET MATERIAL, WO 2019/052966 A2

There is provided a method of preparing a sheet metal material for forming, the method comprising preparing the sheet o metal material to have a desired resistance profile along an axis; heating the sheet metal material to a desired temperature by passing acurrent along the axis; and cutting the sheet metal material to define a blank for forming into a predefined arrangement. There is alsoprovided a method of forming a sheet material.

Patent

Li N, Lin J, 2019, A method for forming sheet material components, WO 2019/038534 Al

A method for forming a component from a Ti-alloy or Ni-alloy sheet material. The method comprising heat treating thesheet material, wherein a final temperature of the sheet material is above 100°C below a b-transus temperature of the sheet material.The sheet material is formed into a predefined configuration between two dies. Forming is completed before the temperature of the o sheet material reaches a start temperature for b to martensite transformation within the sheet material and wherein the temperature ofthe dies is less than a finish temperature for b to martensite transformation within the sheet material.

Patent

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