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

New chromosome number and unreduced pollen formation in Achillea species (Asteraceae)

Cytological studies were performed in 14 populations of 8 Achillea species growing in Iran. A. eriophora, A. tenuifolia, A. oxyodonta, A. talagonica and A. biebersteinii showed 2n = 2x = 18 chromosome number, A. wilhelmsii and A. vermicularis showed 2n = 4x = 36 and A. millefolium showed 2n = 6x = 54 chromosome number. The chromosome numbers of A. eriophora and A. talagonica are new to science and new polyploidy levels are reported for A. tenuifoli and A. wilhelmsii. Tetraploid and hexaploid species, they formed only bivalents in metaphase of meiosis-I showing diplontic behavior possibly due to allopolyploid nature of the species studied and the presence of control over pairing among homologous chromosomes. Multipolar cells were observed almost in all populations and species studied leading to the formation of abnormal tetrads and pollen grains as well as unreduced (2n) pollen formation

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

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

Global dynamic topography observations reveal limited influence of large-scale mantle flow

Convective circulation of the Earth’s mantle maintains some fraction of surface topography that varies with space and time. Most predictive models show that this dynamic topography has peak amplitudes of about ±2km, dominated by wavelengths of 10⁴km. Here, we test these models against our comprehensive observational database of 2,120 spot measurements of dynamic topography that were determined by analysing oceanic seismic surveys. These accurate measurements have typical peak amplitudes of ±1km and wavelengths of approximately 10³km, and are combined with limited continental constraints to generate a global spherical harmonic model whose robustness has been carefully tested and benchmarked. Our power spectral analysis reveals significant discrepancies between observed and predicted dynamic topography. At longer wavelengths (such as 10⁴km), observed dynamic topography has peak amplitudes of about ±500m. At shorter wavelengths (such as 10³km), significant dynamic topography is still observed. We show that these discrepancies can be explained if short-wavelength dynamic topography is generated by temperature-driven density anomalies within a sub-plate asthenospheric channel. Stratigraphic observations from adjacent continental margins show that these dynamic topographic signals evolve quickly with time. More rapid temporal and spatial changes in vertical displacement of the Earth’s surface have direct consequences for fields as diverse as mantle flow, oceanic circulation and long-term climate change

Infrared spectra, thermogravimetric analysis and antifungal studies of noval Cr(III), Fe(III) and Cu(II) 2-methyl-quinazolinone complexes

Some new solid complexes [CrCl3(L)3]×6H2O, [FeCl3(L)3]×6H2O and [Cu(CH3COO)2(L)3]×2H2O have been synthesized quantitatively by the interactions of 2-methyl-quinazolinone (L) with CrCl3.6H2O, FeCl3.6H2O and Cu(CH3COO)2.2H2O in a mixture of an ethanol-bidistilled water (1:1), at 60 °C. They were characterized by melting point, molar conductivity, magnetic moment, elemental analysis, infrared spectra and thermal analyses. The results supported the formation of the complexes and indicated that the ligand reacted as a monodentate ligand bound to the metal ion through the oxygen atom. The antifungal activity of the free ligand and their metal complexes were evaluated against several species, such as Fusarium solani, Rizoctonia solani, Sclortium rolfsii and Botryodiplodia and they showed a good antifungal activity to some selected fungal strain as compared with free ligand. KEY WORDS: Quinazolinone; Cr(III); Fe(III); Cu(II); Antifungal activity, Thermal analyses Bull. Chem. Soc. Ethiop. 2014, 28(1), 53-66.DOI: http://dx.doi.org/10.4314/bcse.v28i1.

Implicit neural representations of sheet stamping geometries with small-scale features

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