Optimisation of deep drawn corners subject to hot stamping constraints using a novel deep-learning-based platform

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

Deformation and thinning field prediction for HFQ® formed panel components using convolutional neural networks

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

Optimisation of panel component regions subject to hot stamping constraints using a novel deep-learning-based platform

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

Control design for interconnected power systems with OLTCs via robust decentralized control

This paper addresses the problem of designing a decentralized control of interconnected power systems, with OLTC and SVCs, under large changes in real and reactive loads that cause large structural changes in the system model. In addition to this, small changes in load are regulated by small disturbance controllers whose gains are adjusted for variations in power system model due to large changes in loads. The only feedback needed by subsystem controllers is the state of the subsystem itself. The design is carried out within a large-scale Markov jump parameter systems framework. In this paper, unlike other control schemes, OLTC transformers are used to damp power-angle oscillations. Simulation results are presented to demonstrate the performance of the designed controller. © 2006 IEEE

Design of switching damping controllers for power systems based on a Markov jump parameter system approach

The application of a new technique, based on the theory of Markov Jump Parameter Systems (MJPS), to the problem of designing controllers to damp power system oscillations is presented in this paper. This problem is very difficult to address, mainly because these controllers are required to have an output feedback decentralized structure. The technique relies on the statistical knowledge about the system operating conditions to provide less conservative controllers than other modern robust control approaches. The influence of the system interconnections over its modes of oscillation is reduced by means of a proper control design formulation involving Integral Quadratic Constraints. The discrete nature of some typical events in power systems (such as line tripping or load switching) is adequately modeled by the MJPS approach, therefore allowing the controller to withstand such abrupt changes in the operating conditions of the system, as shown in the results. © 2006 IEEE

Rapid feasibility assessment of components to be formed through hot stamping: A deep learning approach

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

Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation

Image-to-image translation has been made much progress with embracing Generative Adversarial Networks (GANs). However, it's still very challenging for translation tasks that require high quality, especially at high-resolution and photorealism. In this paper, we present Discriminative Region Proposal Adversarial Networks (DRPAN) for high-quality image-to-image translation. We decompose the procedure of image-to-image translation task into three iterated steps, first is to generate an image with global structure but some local artifacts (via GAN), second is using our DRPnet to propose the most fake region from the generated image, and third is to implement "image inpainting" on the most fake region for more realistic result through a reviser, so that the system (DRPAN) can be gradually optimized to synthesize images with more attention on the most artifact local part. Experiments on a variety of image-to-image translation tasks and datasets validate that our method outperforms state-of-the-arts for producing high-quality translation results in terms of both human perceptual studies and automatic quantitative measures.Comment: ECCV 201

A reduced-reference perceptual image and video quality metric based on edge preservation

In image and video compression and transmission, it is important to rely on an objective image/video quality metric which accurately represents the subjective quality of processed images and video sequences. In some scenarios, it is also important to evaluate the quality of the received video sequence with minimal reference to the transmitted one. For instance, for quality improvement of video transmission through closed-loop optimisation, the video quality measure can be evaluated at the receiver and provided as feedback information to the system controller. The original image/video sequence-prior to compression and transmission-is not usually available at the receiver side, and it is important to rely at the receiver side on an objective video quality metric that does not need reference or needs minimal reference to the original video sequence. The observation that the human eye is very sensitive to edge and contour information of an image underpins the proposal of our reduced reference (RR) quality metric, which compares edge information between the distorted and the original image. Results highlight that the metric correlates well with subjective observations, also in comparison with commonly used full-reference metrics and with a state-of-the-art RR metric. © 2012 Martini et al