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Background:Musculoskeletal tissue degeneration impairs the life quality and function of many people. Meniscus degeneration is a major origin of knee osteoarthritis and a common threat to athletic ability, but its cellular mechanism remains elusive.Methods:We built a cell atlas of 12 healthy or degenerated human meniscus samples from the inner and outer meniscal zones of 8 patients using scRNA-seq to investigate meniscal microenvironment homeostasis and its changes in the degeneration process and verified findings with immunofluorescent imaging.Results:We identified and localized cell types in inner and outer meniscus and found new chondrocyte subtypes associated with degeneration. The observations suggested understandings on how cellular compositions, functions, and interactions participated in degeneration, and on the possible loop-like interactions among extracellular matrix disassembly, angiogenesis, and inflammation in driving the degeneration.Conclusions:The study provided a rich resource reflecting variations in the meniscal microenvironment during degeneration and suggested new cell subtypes as potential therapeutic targets. The hypothesized mechanism could also be a general model for other joint degenerations.Funding:The National Natural Science Foundation of China (81972123, 82172508, 62050178, 61721003), the National Key Research and Development Program of China (2021YFF1200901), Fundamental Research Funds for the Central Universities (2015SCU04A40); The Innovative Spark Project of Sichuan University (2018SCUH0034); Sichuan Science and Technology Program (2020YFH0075); Chengdu Science and Technology Bureau Project (2019-YF05-00090-SN); 1.3.5 Project for Disciplines of Excellence of West China Hospital Sichuan University (ZYJC21030, ZY2017301); 1.3.5 Project for Disciplines of Excellence – Clinical Research Incubation Project, West China Hospital, Sichuan University (2019HXFH039).

In November 2019, muscone content was measured from musk secreted by 114 male captive Alpine musk deer in Xinglongshan Alpine Musk Deer Farm located in Xinglongshan National Nature Reserve, western China. The relationships between muscone content and individual age, health, mating history and enclosure condition were analyzed. The results showed that the muscone content ranged from 0.35% to 5.55% (dry weight percentage of muscone in musk), which was correlated with age (r = −0.132). The muscone content of adults (1.5–8.5 yrs.;1.17 ± 0.09%) was significantly higher than in older individuals (9.5–11.5 yrs.; 0.85 ± 0.06%; p 0.05). The males in the enclosure with lower (1.04 ± 0.12%), medium (1.14 ± 0.16%) and higher density conditions (1.17 ± 0.13%) did not significantly differ in muscone levels. Males that lived with females had slightly higher muscone content (1.18 ± 0.12%) than those who did not (1.06 ± 0.10%; p > 0.05). In general, muscone content of musk decreased with age and poor health, but mating history or captive environment had insignificant effect. In musk deer farming, the farms could enhance the health of musk deer and concentrate on younger individuals in order to improve the musk quality and sustainable production of musk resources.

The prediction of failure and optimisation of the initial blank shape of a component formed under the complex thermal and mechanical conditions could not simply rely on its thinning or strain. In contrast, the prediction of forming limit of material as a comprehensive result of temperature, strain rate and loading path is an essential indicator in a non-isothermal hot stamping process. However, it is challenging to implement an advanced forming limit model into conventional finite element (FE) software to simulate the failure of the formed component and thus optimise its initial blank shape. In this work, a unified viscoplastic-Hosford-MK model was developed to post-process the simulated evolutionary thermomechanical characteristics and then numerically predict the forming limit criterion of AA7075 in a hot stamping process, of which results were demonstrated on a dedicated visualiser that independent of FE software. Subsequently, the initial blank shape was optimised by analysing the evolutionary temperature, strain rate and loading path of failure regions. Furthermore, the developed forming limit model and optimised initial blank shape were experimentally verified with an error of less than 10%.

BackgroundMusculoskeletal tissue degeneration impairs the life quality and function of many people. Meniscus degeneration is a major origin of knee osteoarthritis and a common threat to athletic ability, but its cellular mechanism remains elusive.MethodsWe built a cell atlas of healthy/degenerated human meniscus using scRNA-seq to investigate meniscal microenvironment homeostasis and its changes in the degeneration process and verified findings with immunofluorescent imaging.ResultsWe identified and localized cell types in inner and outer meniscus, found new chondrocyte subtypes contributing to degeneration, and revealed how cellular compositions, functions, and interactions participated in degeneration.We found that ECM disassembly, angiogenesis, and inflammation form a positive feedback loop driving the degeneration. Comparison of cellular interactions between different degenerative states identified each cell type’s functions in the loop.ConclusionsThe study revealed changes in the meniscal microenvironment during degeneration and discovered new cell subtypes as potential therapeutic targets. The observed mechanism could also be a general model for other joint degenerations.FundingThe National Natural Science Foundation of China (81972123, 82172508, 62050178, 61721003) and the National Key Research and Development Program of China (2021YFF1200901).

This perspective discusses the need and directions for the development of a unified information framework to enable the assembly of cell atlases and a revolution in medical research on the virtual body of assembled cell systems.

SUMMARYSingle-cell omics data can characterize multifaceted features of massive cells and bring significant insights to biomedical researches. The accumulation of single-cell data provides growing resources for constructing atlases for all cells of a human organ or the whole body. The true assembly of a cell atlas should be cell-centric rather than file-centric. We proposed a unified information framework enabling seamless cell-centric data assembly and developed a human Ensemble Cell Atlas (hECA) as an instance. hECA version 1.0 assembled scRNA-seq data across multiple studies into one orchestrated data repository. It contains 1,093,299 labeled cells and metadata from 116 published human single-cell studies, covering 38 human organs and 11 systems. We invented three methods of applications based on the cell-centric assembly: “In data” cell sorting enables targeted data retrieval in the full atlas with customizable logic expressions; The “quantitative portraiture” system provides a multi-view presentation of biological entities (organs, cell types, and genes) of multiple granularities; The customizable reference creation allows users to use the cell-centric assembly to generate references for their own cell type annotations. Case studies on agile construction of user-defined sub-atlases and “in data” investigation of CAR-T off-targets in multiple organs showed the great potential of cell-centric atlas assembly.

The goal of big projects like Human Cell Atlas (HCA) and Human BioMedical Atlas Program (HuBMAP) is to build maps that comprehensively define and describe all cell types and their molecular features in a healthy human being. Just like geographical maps must have coordinates, a key task in building cell maps is to provide coordinate systems for cells. A well-designed coordinate system helps better understand the highly orchestrated function and organization of different cells. Cells could be depicted by external information like their spatial locations in the body and organ, the sex and race of the donor, and multiple endogenous attributes of cells such as their types, states, functions, developing trajectory, etc. These heterogeneities are encoded in or can be predicted with transcriptomics and other omics data. Cell heterogeneities are multifaceted, including three major types: continuous values or scores, categorical groups and structured annotations. Here we propose to a unified multidimensional coordinate system UniCoord to represent the multifaceted heterogeneities of cells. It is based on a general deep learning framework, with a supervised VAE structure to learn the mapping relationship between gene expressions and the generated coordinates in a low-dimensional space that encode multiple cell attributes of the three types. Experiment results on several datasets showed that UniCoord was able to represent a variety of cell heterogeneous properties that are discrete, continuous or of hierarchical structures. The trained UniCoord model can be used to automatically label attributes of cells and generate the corresponding expression data. Experiments showed that UniCoord is a feasible coordinates framework for representing multifaceted cell heterogeneity in comprehensive cell atlases.

Contrast-based photoacoustic imaging is an emerging imaging technology that gold nanoparticles (a kind of photoacoustic contrast agent with a size of 100 nm) are injected into blood vessels for photoacoustic imaging. Image reconstruction is a critical step in photoacoustic imaging, due to the fact that limitations and incompleteness of photoacoustic imaging data of image reconstruction are equivalent to the linear inverse problems. The photoacoustic data is transferred to IOT based on the platform of MATLAB and is quickly processed through the regularization method of Krylov space (the widely used method is LSQR). In order to solve the problem of Krylov space uncertainty, Krylov space dimension selection, and the minimum mismatch of the LSQR objective function (the limitation of LSQR algorithm) of LSQR algorithm, a KRLODPLSMR-GCV3DC algorithm is proposed to solve the above problems in this paper.In this study, the raw photoacoustic data was obtained by injecting gold nanoparticles into capillaries and subcutaneous veins of the palm, and the KRLODPLSMR-GCV3DC algorithm was verified to solve the limitations of LSQR algorithm through four groups of experiments.

Extracellular electron transfer (EET) is a governing factor for the electrochemical performance of a bioelectrochemical system (BES) such as the microbial fuel cell (MFC). Herein, an in situ method to fabricate a bio-reduced graphene oxide (GO) (br-GO) modified carbon felt electrode to increase EET was developed. GO (0.5mgmL-1) was spiked into the anode chamber in a three-electrode BES and was transformed to br-GO with a self-assembled three-dimensional (3D) structure. The response of the br-GO modified electrode potential to the attached population of Shewanella putrefaciens increased from 0.071V to 0.517V (vs Ag/AgCl). Meanwhile, br-GO modification resulted a significant enhancement in the total amount of extracellular electrons transferred between the modified electrode and microbe. The process of br-GO modification lowered the charge transfer resistance of the electrode and enhanced the EET. The modified electrode was further employed as an anode in the MFC, and consequently, the power density of the MFC was significantly enhanced. The current study not only gives a simple and effective way for improving the EET with br-GO fabrication, but also provides a strategy to enhance the power density of the MFC.

In photoacoustic imaging, regularization is often used to improve the fidelity of reconstructed images. This method is based on augmented Lagrangian alternate TV method, which is a known compression sensing method. The results show that this method can quickly calculate the regularization parameters and efficiently reconstruct the initial sound pressure. In this paper, 2 - valued image and CT scan data are used to evaluate the algorithm

Summary Expectations of machine learning (ML) are high for discovering new patterns in high-throughput biological data, but most such practices are accustomed to relying on existing knowledge conditions to design experiments. Investigations of the power and limitation of ML in revealing complex patterns from data without the guide of existing knowledge have been lacking. In this study, we conducted systematic experiments on such ab initio knowledge discovery with ML methods on single-cell RNA-sequencing data of early embryonic development. Results showed that a strategy combining unsupervised and supervised ML can reveal major cell lineages with minimum involvement of prior knowledge or manual intervention, and the ab initio mining enabled a new discovery of human early embryonic cell differentiation. The study illustrated the feasibility, significance, and limitation of ab initio ML knowledge discovery on complex biological problems., Graphical Abstract, Highlights • We explore the feasibility of ab initio knowledge discovery from scRNA-seq data • A combined ML strategy to infer cell lineages with minimum prior knowledge • It recovers basic developmental knowledge and suggests a new discovery • We discuss the power and limitation of ab initio knowledge discovery, The Bigger Picture Machine learning (ML) has been shown to be powerful in many artificial intelligence tasks, so people expect it to be able to reveal patterns that even human experts may have difficulty discovering. Scientists are enthusiastic in using ML to analyze the complex biology underlying various single-cell genomics data, but most existing studies of this type are accustomed to relying on existing knowledge to design experiments. Such practices may miss important discoveries and leave the question open as to how far ML and data may go beyond the sphere of existing knowledge. This study uses the example of cell lineages in early embryonic development to investigate the feasibility of machine-learning discovery of biological knowledge from data with minimum use of prior knowledge. We call the tasks ab initio knowledge discovery. The strategy and observations can act as a baseline for future efforts of discovering new knowledge from single-cell genomics data., Machine learning (ML) is highly expected to reveal biological patterns from single-cell omics data, but most existing ML practices involve prior knowledge. We explore the feasibility of ab initio knowledge discovery from scRNA-sequencing data with minimum use of prior knowledge. A strategy combining unsupervised and supervised ML is shown to be powerful in recovering correct embryonic cell lineages and also suggests a new discovery. The observed successes and limitations suggest future directions for ab initio knowledge discovery.

Contrast-based photoacoustic imaging is an emerging imaging technology that gold nanoparticles (a kind of photoacoustic contrast agent with a size of 100 nm) are injected into blood vessels for photoacoustic imaging. Image reconstruction is a critical step in photoacoustic imaging, due to the fact that limitations and incompleteness of photoacoustic imaging data of image reconstruction are equivalent to the linear inverse problems. The photoacoustic data is transferred to IOT based on the platform of MATLAB and is quickly processed through the regularization method of Krylov space (the widely used method is LSQR). In order to solve the problem of Krylov space uncertainty, Krylov space dimension selection, and the minimum mismatch of the LSQR objective function (the limitation of LSQR algorithm) of LSQR algorithm, a KRLODPLSMR-GCV3DC algorithm is proposed to solve the above problems in this paper.In this study, the raw photoacoustic data was obtained by injecting gold nanoparticles into capillaries and subcutaneous veins of the palm, and the KRLODPLSMR-GCV3DC algorithm was verified to solve the limitations of LSQR algorithm through four groups of experiments.

A novel sheet metal forming process, by manufacturing parts in a single sheet with varying thickness, has been employed in this work. It combines hot forming and cold-die quenching, also known as HFQ®, and the use of aluminium tailor welded blanks (TWBs) into a hybrid process. A series of hot stamping tests on the AA6082 TWBs were performed to investigate the deformation behaviour and failure features. Two failure modes, i.e. circumferential necking and parallel weld necking have been observed in the formed parts depending on the forming speed and thickness ratio of the TWBs. An advanced forming limit prediction model has been developed and further integrated into finite element simulation via a cloud-based multi-objective platform 1 to investigate the failure/necking features of AA6082 TWBs. The model incorporates the theories of Hosford yield function, the anisotropic nature of plastic deformation in sheet metals and the Marciniak-Kaczynski (M-K) theory. According to the theories, the incremental work per unit volume ratio ( d e ¯ B ∙ σ ¯ B / d e ¯ A ∙ σ ¯ A ) between Zone B (thickness imperfect zone) and Zone A (the remainder of the material) is a key parameter determining the formability, by which the complex failure features have been fundamentally studied. The transition of failure mode in a TWB was attributed to the joint effects of temperature, strain rate and loading path changes. Strain rate could accelerate the development of localised necking in the TWBs when the failure mode was in transition from the circumferential mode to parallel mode.

A novel hot stamping technique known as ‘Solution Heat treatment, Forming and in-die Quenching (HFQ ® )’ was employed to manufacture lightweight structural components from AA6082 tailor-welded blanks (TWBs) of different thickness combinations: 1.5–1.5 and 2.0–1.0 mm. A finite element (FE) model was built to study the deformation characteristics during the hot stamping process. The FE model was successfully validated by comparing simulation results with experimental ones. Subsequently, the verified simulation results were analysed through a novel multi-objective FE platform known as ‘Knowledge-Based Cloud – Finite Element (KBC-FE)’. KBC-FE operates in a cloud environment and offers various advanced unique functions via functional modules. The ‘formability’ module was implemented in the current study to predict the limiting dome height and failure mode during the hot stamping process. Good agreements were achieved between the predicted and experimental results, from which studies were extended to predict the forming features of 2.0–1.5 mm TWBs. The ‘formability’ module has successfully captured the complex nature of a hot stamping process, featuring a non-isothermal and non-linear loading path. The formability of TWBs was found to be dependent on forming speed and blank thickness, out of which the latter has a dominant effect.

An intrinsic feature of the hot stamping process, in which a hot blank is quenched and formed between water cooled dies, is the severe thermo-mechanical deformation that the blank experiences under the combined influences of non-isothermal and non-proportional loadings. This results in challenges for conventional forming limit prediction models to accurately predict material behaviour. In this paper, a novel viscoplastic-Hosford-MK model was developed to predict the forming limits of an Al–Li alloy under hot stamping conditions. The effectiveness of the developed model was verified by the demonstration of accurate responses to cold die quenching, strain rate and loading path changes, enabling the developed model to reveal a realistic critical material response under complex deformation conditions. Finally, by applying the developed model to the hot stamping of an AA2060 component, its accuracy was successfully validated. It was indicated that the onset of necking during hot stamping of the component did not necessarily occur at the maximum thinning region, and this was due to the comprehensive effects of varying loading path, strain rate and temperature. A detailed mathematical analysis of the developed M-K model was also conducted, and it was found that the incremental work per unit volume ratio ( W ˙ b / W ˙ a = σ ¯ b d ɛ ¯ b / σ ¯ a d ɛ ¯ a ) between Zone b (where a thickness inhomogeneity exists) and Zone a (the remainder of the material) was a significant parameter that determined the formability of AA2060 under hot stamping conditions.

Multiple steps of bioinformatics processing are needed to convert the raw scRNA-seq data to information that can be used in downstream analyses and in building cell atlases. Dozens of software packages have been developed and different labs tend to have different preferences on choices of the workflow. Such diversity can cause difficulties in future efforts of aggregating data from multiple labs, and also difficulties for new labs to start in this field. A few pipelines have been developed to help integrating multiple steps into a whole, but the fixed software architecture makes it hard for developers to add new features or exchange parts in the pipeline.We presented SIP, a Single-cell Interchangeable Pipeline. It is a one-stop platform for the processing of scRNA-seq data from multiple platforms, and will also support for other types of data like scATAC-seq data. SIP utilizes container technology to solve the deployment dilemma when handling multiple packages and provides an easy-to-use interface for users to conduct the complicated multi-step process from raw data to final results with a single command. It also allows advanced users to assemble different versions of the pipeline by interchanging parts or adding new modules. SIP is available at https://github.com/XuegongLab/SIP under the GPL-3.0 license.

This paper discussed the friction coefficient evolution of a TiN coated contact during sliding wear. A hard TiN coating was prepared on a bearing steel (GCr15) substrate by applying the composite method of cathode arc and magnetron sputtering. The microstructure, microhardness, microscratch and tribological behaviour of this coating were studied to obtain the relationship between friction coefficient and other coating properties, meanwhile the full friction coefficient evolution curve with different stages during the wear process of TiN coating was shown. It was found that, coating friction and wear, as two interactive responses from a tribo-system, mutually affected each other and should be studied as a single physical phenomenon. Therefore, a novel friction-wear interactive friction model was developed to represent the evolution of friction coefficient and to predict coating breakdown. The results show that the evolution of the friction coefficient curve can reflect different stages of the wear process and the wear life can be estimated using the new friction model.

Deep neural networks, including recurrent networks, have been successfully applied to human activity recognition. Unfortunately, the final representation learned by recurrent networks might encode some noise (irrelevant signal components, unimportant sensor modalities, etc.). Besides, it is difficult to interpret the recurrent networks to gain insight into the models' behavior. To address these issues, we propose two attention models for human activity recognition: temporal attention and sensor attention. These two mechanisms adaptively focus on important signals and sensor modalities. To further improve the understandability and mean F1 score, we add continuity constraints, considering that continuous sensor signals are more robust than discrete ones. We evaluate the approaches on three datasets and obtain state-of-the-art results. Furthermore, qualitative analysis shows that the attention learned by the models agree well with human intuition., Comment: 8 pages. published in The International Symposium on Wearable Computers (ISWC) 2018

© 2015 The Authors.Uniaxial tensile tests of aluminium-lithium alloy AA1420wereconducted at superplastic forming conditions. The mechanical properties of this Al-Li alloy were then modelled by a set of physicallybased constitutive equations. The constitutive equations describe the isotropic work hardening,recovery and damage by dislocation density changes and grain size evolution. Based on a recent upgraded optimisation technique, the material constants for these constitutive equations were determined.

© 2015 The Authors.The selection of the heat transfer coefficient is one of the most important factors that determine the reliability of FE simulation results of a hot stamping process, in which the formed component is held within cold dies until fully quenched. The quenching process could take up to 10. seconds. In order to maximise the production rate, the optimised quenching parameters should be identified to achieve the highest possible quenching rate and to reduce the quenching time. For this purpose, a novel-testing rig for the Gleeble 3800 thermo- mechanical simulator was designed and manufactured, with an advanced control system for temperature and contact pressure. The effect of contact pressure on the heat transfer coefficient was studied. The findings of this research will provide useful guidelines for the selection of the heat transfer coefficient in simulations of hot stamping processes and useful information for the design of hot stamping processes.

The use of Finite Element (FE) simulation software to adequately predict the outcome of sheet metal forming processes is crucial to enhancing the efficiency and lowering the development time of such processes, whilst reducing costs involved in trial-and-error prototyping. Recent focus on the substitution of steel components with aluminum alloy alternatives in the automotive and aerospace sectors has increased the need to simulate the forming behavior of such alloys for ever more complex component geometries. However these alloys, and in particular their high strength variants, exhibit limited formability at room temperature, and high temperature manufacturing technologies have been developed to form them. Consequently, advanced constitutive models are required to reflect the associated temperature and strain rate effects. Simulating such behavior is computationally very expensive using conventional FE simulation techniques. This paper presents a novel Knowledge Based Cloud FE (KBC-FE) simulation technique that combines advanced material and friction models with conventional FE simulations in an efficient manner thus enhancing the capability of commercial simulation software packages. The application of these methods is demonstrated through two example case studies, namely: the prediction of a material's forming limit under hot stamping conditions, and the tool life prediction under multi-cycle loading conditions.

An advanced forming technology, solution Heat treatment, Forming and in-die Quenching (HFQ ® ), has been employed to form AA6082 tailor welded blanks (TWBs). In comparison with conventional stamping of TWBs, the mechanical properties and formability of AA6082 laser TWBs could be improved under the HFQ ® forming condition. The TWB was divided into three physical zones, i.e. base metal, heat affected zone (HAZ) and weld zone, based on the hardness distribution. It was found that the degraded hardness of the weldment can be restored after HFQ ® forming. TWBs of AA6082 with different thickness ratios of 2 (2–1 mm), 1.3 (2–1.5 mm) and 1 (1.5–1.5 mm) were used to study the TWB thickness ratio effects on the forming behaviour. Hemispherical punch dome tests on the TWBs with varying thickness ratios demonstrated different formabilities, and indicated increased displacement of the weld line with increasing thickness ratio. Finite element (FE) modelling was adopted to analyse the weld line movement and strain distributions during HFQ ® forming.

The feasibility of forming a third generation aluminium-lithium alloy (AA2060) into a complex shaped panel component, was studied by using an advanced forming technology called solution heat treatment, cold die forming and in-die quenching (HFQ) process. The main challenges using HFQ technology to form complex shaped AA2060 component was to find out optimum forming parameters, such as forming temperature, forming speed, lubrication condition and blank holding force. In this paper, the optimum forming temperature was mainly concerned. The flow stresses of AA2060 were obtained at different temperatures ranging from 350 to 520 °C at the strain rate of 2 s −1 . The suitable temperature to achieve the adequate ductility was found at 470 °C. By forming the AA2060 blanks at the optimum forming temperature, experimental results exhibited the feasibility for forming complex-shaped AA2060 components. The formed components were analysed through strain measurements. The post-form mechanical properties of AA2060 were assessed using hardness and tensile tests.

Novel chitosan composite coatings containing titania nanoparticles (n-TiO2) for biomedical applications were developed by electrophoretic deposition (EPD) from ethanol–water suspensions. The optimal ethanol–water ratio was studied in order to avoid bubble formation during the EPD process and to ensure homogeneous coatings. Different n-TiO2 contents (0.5–10 g L−1) were studied for a fixed chitosan concentration (0.5 g L−1) and the properties of the electrophoretic coatings obtained were characterized. Coating composition was analyzed by thermogravimetric analysis (TG), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analysis. Scanning electron microscopy (SEM) was employed to study both the surface and the cross section morphology of the coatings, and the thicknesses (2–6 μm) of the obtained coatings were correlated with the initial ceramic content. Contact angle measurements, as a preliminary study to predict hypothetic protein attachment on the coatings, were performed for different samples and the influence of a second chitosan layer on top of the coatings was also tested. Finally, the electrochemical behavior of the coatings, evaluated by polarization curves in DMEM at 37 °C, was studied in order to assess the corrosion resistance provided by the n-TiO2/chitosan coatings.

1,3-Di-{N-[bis(dimethylamino)methane]}benzyl-diamide 1 was synthesized by the reaction of isophthaloyl dichloride with 1,1,3,3-tetramethylguanidine (TMG). Its structure was confirmed by IR, 1H NMR, EI-MS and elemental analysis. 1H NMR results of the interaction of 1 with phosphate-containing biomolecules showed it has the ability of selective molecular recognition for nucleotides.

An advanced forming technology, solution Heat treatment, Forming and in-die Quenching (HFQ ® ), has been employed to form AA6082 tailor welded blanks (TWBs). In comparison with conventional stamping of TWBs, the mechanical properties and formability of AA6082 laser TWBs could be improved under the HFQ forming condition. The TWB was divided into three physical zones, i.e. base metal, heat affected zone (HAZ) and weld zone, based on the hardness distribution. It was found that the degraded hardness of the weldment can be restored after HFQ forming. TWBs of AA6082 with different thickness ratios of 2 (2-1mm), 1.3 (2-1.5mm) and 1 (1.5-1.5mm) were used to study the TWB thickness ratio effects on the forming behaviour. Hemispherical punch dome tests on the TWBs with varying thickness ratios demonstrated different formabilities, and indicated increased displacement of the weld line with increasing thickness ratio. Finite element (FE) modelling was adopted to analyse the weld line movement and strain distributions during HFQ forming. HFQ ® is a registered trademark of Impression Technologies Ltd.