Analysis of actuation and instabilities in dielectric elastomer devices

Dielectric elastomer (DE) devices have gained significant interest in fields such as soft robotics, mechanical engineering, biomedical technology, and energy engineering due to their lightweight and fast actuation capabilities. However, these devices have several shortcomings that this thesis aims to address through the analysis of instabilities and actuation in various configurations. The electroelasticity theory is presented, defining the general kinematics and constitutive equations for these hyperelastic materials. Using this theory as a foundation, various configurations are introduced and analysed, with a focus on the novel ‘floating’ device as both a slab and tubular elastomer. These configurations are examined under different boundary conditions, and the deformation paths are analysed as geometrical parameters are varied. The onset of electro-mechanical instability is shown, as well as the introduction of the expansion limit. The theory of incremental deformations is specialised to investigate surface instabilities in three previously introduced elastomer slab configurations. It is shown that the instability is more sensitive to pre-stress in the ‘floating’ configuration, while the configuration deformed by sprayed charges is more stable against surface instabilities compared to the same configuration actuated by voltage. The effects of stiff electrodes on surface instabilities are also studied using surface-coating models, and it is demonstrated that the stability domain is significantly reduced when the device contracts. New bifurcation modes come into play and each one has been studied and characterised. Laminated composite elastomers are then considered, which are of particular interest due to their ability to enhance actuation characteristics. Using a small strain model and various boundary conditions, it is shown how, with specific parameters, an inverse mode of actuation can be achieved in both rank-1 and rank-2 laminated composites. The rank-2 laminate is demonstrated to enhance the rank-1 inverse actuation mode, and a guideline for optimizing composite parameters is provided. Existing materials are also analysed to show how current technology requires a rank-2 laminate to obtain the inverse mode of actuation

New actuation modes of composite dielectric elastomers

The typical actuation mode of a dielectric elastomer membrane subjected to an electric field across its thickness is in-plane expansion. We show that, by selecting properly the contrast between phases (i.e. shear moduli and permittivity ratios), a hierarchical laminate may display longitudinal contraction when actuated in the same way. In particular, simple and second rank laminates are investigated. The latter performs in general better; however, we provide a guideline on how to optimize the microstructure to limit the values of the contrast parameters at which the new ‘non-conventional’ mode becomes available. As the requirements in terms of permittivity ratio of the two phases are somewhat extreme, we review the availability of materials that have been processed so far to assess the viability of such composite devices

Surface instabilities of soft dielectric elastomers with implementation of electrode stiffness

This paper contains a thorough investigation into plane-strain electroelastic surface instabilities of dielectric elastomers. We employ a systematic approach to our investigation, introducing three ways to actuate an elastomer device, namely, actuation by means of (1) attached compliant electrodes, (2) sprayed charges onto the opposite surfaces, and (3) fixed electrodes between which the device “floats” in vacuum and expands transversally. We examine electromechanical instability with particular attention to the third listed mode of actuation and the features of the specimen. We then tackle surface instability for the three modes, showing the relationship between applied pre-stress and the stability domain, as well as the characteristics of the obtained bifurcation fields. The effects of the stiffness of the electrode (relevant in the first listed mode of actuation) on surface instabilities are then investigated by adopting an elastic surface–substrate interaction model in which the properties of the coating enter the boundary conditions for the substrate. Various electrode materials are assumed, demonstrating that their implementation in the model increases the number of solutions at bifurcation and changes the overall stability domain. We present this new enriched bifurcation map, showing the dependence on the wavenumber, and characterise the solutions by examining the bifurcated fields

Fault Injection Analytics: A Novel Approach to Discover Failure Modes in Cloud-Computing Systems

Cloud computing systems fail in complex and unexpected ways due to unexpected combinations of events and interactions between hardware and software components. Fault injection is an effective means to bring out these failures in a controlled environment. However, fault injection experiments produce massive amounts of data, and manually analyzing these data is inefficient and error-prone, as the analyst can miss severe failure modes that are yet unknown. This paper introduces a new paradigm (fault injection analytics) that applies unsupervised machine learning on execution traces of the injected system, to ease the discovery and interpretation of failure modes. We evaluated the proposed approach in the context of fault injection experiments on the OpenStack cloud computing platform, where we show that the approach can accurately identify failure modes with a low computational cost.Comment: IEEE Transactions on Dependable and Secure Computing; 16 pages. arXiv admin note: text overlap with arXiv:1908.1164

Automating the Correctness Assessment of AI-generated Code for Security Contexts

In this paper, we propose a fully automated method, named ACCA, to evaluate the correctness of AI-generated code for security purposes. The method uses symbolic execution to assess whether the AI-generated code behaves as a reference implementation. We use ACCA to assess four state-of-the-art models trained to generate security-oriented assembly code and compare the results of the evaluation with different baseline solutions, including output similarity metrics, widely used in the field, and the well-known ChatGPT, the AI-powered language model developed by OpenAI. Our experiments show that our method outperforms the baseline solutions and assesses the correctness of the AI-generated code similar to the human-based evaluation, which is considered the ground truth for the assessment in the field. Moreover, ACCA has a very strong correlation with human evaluation (Pearson's correlation coefficient r=0.84 on average). Finally, since it is a fully automated solution that does not require any human intervention, the proposed method performs the assessment of every code snippet in ~0.17s on average, which is definitely lower than the average time required by human analysts to manually inspect the code, based on our experience

Who Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generators

AI-based code generators are an emerging solution for automatically writing programs starting from descriptions in natural language, by using deep neural networks (Neural Machine Translation, NMT). In particular, code generators have been used for ethical hacking and offensive security testing by generating proof-of-concept attacks. Unfortunately, the evaluation of code generators still faces several issues. The current practice uses automatic metrics, which compute the textual similarity of generated code with ground-truth references. However, it is not clear what metric to use, and which metric is most suitable for specific contexts. This practical experience report analyzes a large set of output similarity metrics on offensive code generators. We apply the metrics on two state-of-the-art NMT models using two datasets containing offensive assembly and Python code with their descriptions in the English language. We compare the estimates from the automatic metrics with human evaluation and provide practical insights into their strengths and limitations

Enhancing Robustness of AI Offensive Code Generators via Data Augmentation

In this work, we present a method to add perturbations to the code descriptions, i.e., new inputs in natural language (NL) from well-intentioned developers, in the context of security-oriented code, and analyze how and to what extent perturbations affect the performance of AI offensive code generators. Our experiments show that the performance of the code generators is highly affected by perturbations in the NL descriptions. To enhance the robustness of the code generators, we use the method to perform data augmentation, i.e., to increase the variability and diversity of the training data, proving its effectiveness against both perturbed and non-perturbed code descriptions

Enhancing Failure Propagation Analysis in Cloud Computing Systems

In order to plan for failure recovery, the designers of cloud systems need to understand how their system can potentially fail. Unfortunately, analyzing the failure behavior of such systems can be very difficult and time-consuming, due to the large volume of events, non-determinism, and reuse of third-party components. To address these issues, we propose a novel approach that joins fault injection with anomaly detection to identify the symptoms of failures. We evaluated the proposed approach in the context of the OpenStack cloud computing platform. We show that our model can significantly improve the accuracy of failure analysis in terms of false positives and negatives, with a low computational cost.Comment: 12 pages, The 30th International Symposium on Software Reliability Engineering (ISSRE 2019

Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generation

Neural Machine Translation (NMT) has reached a level of maturity to be recognized as the premier method for the translation between different languages and aroused interest in different research areas, including software engineering. A key step to validate the robustness of the NMT models consists in evaluating the performance of the models on adversarial inputs, i.e., inputs obtained from the original ones by adding small amounts of perturbation. However, when dealing with the specific task of the code generation (i.e., the generation of code starting from a description in natural language), it has not yet been defined an approach to validate the robustness of the NMT models. In this work, we address the problem by identifying a set of perturbations and metrics tailored for the robustness assessment of such models. We present a preliminary experimental evaluation, showing what type of perturbations affect the model the most and deriving useful insights for future directions.Comment: Paper accepted for publication in the proceedings of The 1st Intl. Workshop on Natural Language-based Software Engineering (NLBSE) to be held with ICSE 202