Meso-scale modelling of 3D woven composite T-joints with weave variations

A meso-scale modelling framework is proposed to simulate the 3D woven fibre architectures and the mechanical performance of the composite T-joints, subjected to quasi-static tensile pull-off loading. The proposed method starts with building the realistic reinforcement geometries of the 3D woven T-joints at the mesoscale, of which the modelling strategy is applicable for other types of geometries with weave variations at the T-joint junction. Damage modelling incorporates both interface and constituent material damage, in conjunction with a continuum damage mechanics approach to account for the progressive failure behaviour. With a voxel based cohesive zone model, the proposed method is able to model mode I delamination based on the voxel mesh technique, which has advantages in meshing. Predicted results are in good agreement with experimental data beyond initial failure, in terms of load-displacement responses, failure events, damage initiation and propagation. The significant effect of fibre architecture variations on mechanical behaviour is successfully predicted through this modelling method without any further correlation of input parameters in damage model. This predictive method will facilitate the design and optimisation of 3D woven T-joint preforms

Experimental assessment of the mechanical behaviour of 3D woven composite T-joints

To understand the influence of the fibre architecture of 3D woven composite T-joints on mechanical performance, as well as the benefits that 3D woven T-joints can offer over the equivalent 2D laminates, experimental testing is performed on two types of 3D woven T-joint with only weave variation at the junction, and one type of 2D woven laminate T-joint. A quasi-static tensile pull-off loading is selected in this work as this out-of-plane load case is one of the typical loading conditions for such T-joint structures. The significant advantages of 3D woven composite T-joints in terms of ultimate strength and damage tolerance over the 2D alternative were identified in the testing. More importantly, this work showed that variation in the fibre architecture can considerably enhance properties such as delamination resistance and total energy absorption to failure, as well as increasing slightly the stiffness and initial failure load. This experimental assessment has demonstrated that using 3D woven reinforcements is an effective way to improve the load-bearing capability of composite T-joints over laminates, and also that this improvement could be optimised with regard to fibre architecture

Effect of fibre architecture on tensile pull-off behaviour of 3D woven composite T-joints

3D woven composites are frequently employed due to their improved through-thickness properties and high damage tolerance compared with laminated composites. Due to the large design space for 3D weave patterns, an in-depth understanding of the relationship between the weave parameters and mechanical properties is essential for the design of these materials. This numerical study investigates the effect of fibre architecture on the mechanical performance of 3D woven composite T-joints under tensile pull-off loading. Six weave pattern variations, subjected to the same preform manufacturing constraint, are designed and numerically analysed, along with another two that have been manufactured and tested for validation previously. Results show a significant architecture dependence in the mechanical responses. Following the design of experiments on weave patterns, the complex architecture-dependant effect is decoupled by two independent variables, yarn path entanglement and yarn path crossover. The study also provides design recommendations for 3D woven T-joint reinforcements under tensile pull-off loading

Design optimisation of 3D woven reinforcements with geometric features

3D weaving technology enables the manufacturing of advanced near net-shape profiles, through incorporating geometric features such as bifurcations, and varying cross-sections into the preform, for use as reinforcements in composite materials. Their design space is very large due to a wide range of weave patterns in their 3D fibre architectures, and currently the lack of analysis techniques that relate weave architectures with the resulting processing and structural performance restricts the application of such advanced 3D woven composites. In this thesis, the effect of fibre architecture on mechanical properties was shown to be significant for 3D woven composite T-joints, which further illustrates the challenges for designing an optimum 3D woven reinforcement. This work is an attempt to develop modelling techniques that are able to accurately predict the resulting structural performance for composites based on 3D woven architectures. Geometric models were developed to account for the deformation in the fibre architecture caused by the bifurcation process in manufacturing of 3D near net-shape T-joint preforms, in order to improve the accuracy of the reinforcement geometry in finite element models. A methodology based on voxels was proposed to overcome the current meshing problems whilst allowing delamination modelling for composites with complex fibre architecture. Consequently, a meso-scale modelling framework for predicting weave architectures of 3D woven composite T-joints and the resulting mechanical behaviour under quasi-static pull-off loading was proposed, in which damage modelling incorporates both interface and constituent material damage, in conjunction with a continuum damage mechanics approach to account for the progressive failure behaviour. Results were shown to agree well with experimental data beyond initial failure. Based on the proposed predictive modelling method, the effect of fibre architecture, in which the design space was simplified by three design variables, i.e. weft yarn path straightness, weft yarn path entanglement and weft yarn path cross-over, on mechanical performance was investigated. Varying yarn path cross-over was found to improve both stiffness and failure load. Increasing the proportion of yarn path entanglement also improved the damage resistance capability, but a high level of yarn path entanglement caused a reduction in yarn path straightness and therefore the structural stiffness would be compromised. Based on the above findings, a design optimisation philosophy for 3D woven T-joint reinforcements under tensile pull-off loading was proposed

Mesoscale geometric modelling of bifurcation in 3D woven T-beam preforms

Manipulation of the through-thickness yarn path enables 3D woven reinforcement to separate locally in the form of a bifurcation, creating net-shaped preforms for T- and I-beams. Preforming introduces fibre architecture deformation at the 3D woven bifurcation area. We report a geometric modelling approach to represent the realistic fibre architecture, as a preprocessing tool for finite element analyses. The study started with x-ray micro-computed tomography (µCT) of two 3D woven T-beams varying only by their yarn path at the T-junction area. Supported by the µCT image analysis, a set of mathematical formula were proposed to describe the identified features in the 3D woven T-beams. We then moved on to implement the automated modelling procedure in the open-source software TexGen. Using the weave pattern as input data, TexGen first simulates as-woven flat T-piece. Next, TexGen applies geometric transformation and refinements to simulate the preforming process of T-beams. The paper highlights an efficient approach to model the complex woven bifurcation structure at mesoscale

Robust Fully-Asynchronous Methods for Distributed Training over General Architecture

Perfect synchronization in distributed machine learning problems is inefficient and even impossible due to the existence of latency, package losses and stragglers. We propose a Robust Fully-Asynchronous Stochastic Gradient Tracking method (R-FAST), where each device performs local computation and communication at its own pace without any form of synchronization. Different from existing asynchronous distributed algorithms, R-FAST can eliminate the impact of data heterogeneity across devices and allow for packet losses by employing a robust gradient tracking strategy that relies on properly designed auxiliary variables for tracking and buffering the overall gradient vector. More importantly, the proposed method utilizes two spanning-tree graphs for communication so long as both share at least one common root, enabling flexible designs in communication architectures. We show that R-FAST converges in expectation to a neighborhood of the optimum with a geometric rate for smooth and strongly convex objectives; and to a stationary point with a sublinear rate for general non-convex settings. Extensive experiments demonstrate that R-FAST runs 1.5-2 times faster than synchronous benchmark algorithms, such as Ring-AllReduce and D-PSGD, while still achieving comparable accuracy, and outperforms existing asynchronous SOTA algorithms, such as AD-PSGD and OSGP, especially in the presence of stragglers

Mesoscale geometric modelling of bifurcation in 3D woven T-beam preforms

Manipulation of the through-thickness yarn path enables 3D woven reinforcement to separate locally in the form of a bifurcation, creating net-shaped preforms for T- and I-beams. Preforming introduces fibre architecture deformation at the 3D woven bifurcation area. We report a geometric modelling approach to represent the realistic fibre architecture, as a preprocessing tool for finite element analyses. The study started with x-ray micro-computed tomography (µCT) of two 3D woven T-beams varying only by their yarn path at the T-junction area. Supported by the µCT image analysis, a set of mathematical formula were proposed to describe the identified features in the 3D woven T-beams. We then moved on to implement the automated modelling procedure in the open-source software TexGen. Using the weave pattern as input data, TexGen first simulates as-woven flat T-piece. Next, TexGen applies geometric transformation and refinements to simulate the preforming process of T-beams. The paper highlights an efficient approach to model the complex woven bifurcation structure at mesoscale

Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training

Transformer-based large language models (LLMs) have demonstrated impressive capabilities in a variety of natural language processing (NLP) tasks. Nonetheless, it is challenging to deploy and fine-tune LLMs on mobile edge devices with limited computing, memory, and energy budgets. In this paper, we propose Confidant, a multi-backend collaborative training framework for customizing state-of-the-art LLMs on commodity mobile devices like smartphones. Confidant partitions an LLM into several sub-models so that each fits into a mobile device's memory. A pipeline parallel training mechanism is further developed to ensure fast and efficient distributed training. In addition, we propose a novel backend scheduler to allocate different attention heads to heterogeneous compute hardware, including mobile CPU and GPUs, to maximize the compute resource utilization on each edge device. Our preliminary experimental results show that Confidant achieves at most 45.3% memory reduction and 8.03x inference speedup in practical settings.Comment: 6 pages, 7 figures; Submitted to HotMobile 202

Experimental and numerical investigation of interface damage in composite L-angle sections under four-point bending

© The Author(s) 2020. Curved laminates in aero-structures, such as the L-angle sections where webs and flanges meet, are prone to delamination due to high interlaminar stresses in these regions. Some efforts to investigate delamination in these structures can be found in the literature but commonly structures are limited to unidirectional layups or modelling approaches are constrained to the cohesive element based methods. In this work, multi-directional L-angle laminates were manufactured using unidirectional prepregs and tested under four-point bending load conditions to examine the interface damage. Acoustic emission technique was used to assist the capture of damage initiation and propagation. Three interface modelling strategies for predicting delamination, namely cohesive element, cohesive surface and perfectly bonded interface were used in the numerical study. The interface damage behaviour was successfully predicted by the simulation methods and differences among the strategies were compared

AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training

It is usually infeasible to fit and train an entire large deep neural network (DNN) model using a single edge device due to the limited resources. To facilitate intelligent applications across edge devices, researchers have proposed partitioning a large model into several sub-models, and deploying each of them to a different edge device to collaboratively train a DNN model. However, the communication overhead caused by the large amount of data transmitted from one device to another during training, as well as the sub-optimal partition point due to the inaccurate latency prediction of computation at each edge device can significantly slow down training. In this paper, we propose AccEPT, an acceleration scheme for accelerating the edge collaborative pipeline-parallel training. In particular, we propose a light-weight adaptive latency predictor to accurately estimate the computation latency of each layer at different devices, which also adapts to unseen devices through continuous learning. Therefore, the proposed latency predictor leads to better model partitioning which balances the computation loads across participating devices. Moreover, we propose a bit-level computation-efficient data compression scheme to compress the data to be transmitted between devices during training. Our numerical results demonstrate that our proposed acceleration approach is able to significantly speed up edge pipeline parallel training up to 3 times faster in the considered experimental settings