Showing papers in "Engineering With Computers in 2022"

Analysis of three-dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis

Abstract: Abstract In this work, we present a deep collocation method (DCM) for three-dimensional potential problems in non-homogeneous media. This approach utilizes a physics-informed neural network with material transfer learning reducing the solution of the non-homogeneous partial differential equations to an optimization problem. We tested different configurations of the physics-informed neural network including smooth activation functions, sampling methods for collocation points generation and combined optimizers. A material transfer learning technique is utilized for non-homogeneous media with different material gradations and parameters, which enhance the generality and robustness of the proposed method. In order to identify the most influential parameters of the network configuration, we carried out a global sensitivity analysis. Finally, we provide a convergence proof of our DCM. The approach is validated through several benchmark problems, also testing different material variations.

Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps

Abstract: We propose FiberNet, a method to estimate \emph{in-vivo} the cardiac fiber architecture of the human atria from multiple catheter recordings of the electrical activation. Cardiac fibers play a central role in the electro-mechanical function of the heart, yet they are difficult to determine in-vivo, and hence rarely truly patient-specific in existing cardiac models. FiberNet learns the fiber arrangement by solving an inverse problem with physics-informed neural networks. The inverse problem amounts to identifying the conduction velocity tensor of a cardiac propagation model from a set of sparse activation maps. The use of multiple maps enables the simultaneous identification of all the components of the conduction velocity tensor, including the local fiber angle. We extensively test FiberNet on synthetic 2-D and 3-D examples, diffusion tensor fibers, and a patient-specific case. We show that 3 maps are sufficient to accurately capture the fibers, also in the presence of noise. With fewer maps, the role of regularization becomes prominent. Moreover, we show that the fitted model can robustly reproduce unseen activation maps. We envision that FiberNet will help the creation of patient-specific models for personalized medicine. The full code is available at http://github.com/fsahli/FiberNet.

Data-driven modeling of the mechanical behavior of anisotropic soft biological tissue

Abstract: Closed-form constitutive models are currently the standard approach for describing soft tissues’ mechanical behavior. However, there are inherent pitfalls to this approach. For example, explicit functional forms can lead to poor fits, non-uniqueness of those fits, and exaggerated sensitivity to parameters. Here we overcome some of these problems by designing deep neural networks (DNN) to replace such explicit expert models. One challenge of using DNNs in this context is the enforcement of stress-objectivity. We meet this challenge by training our DNN to predict the strain energy and its derivatives from (pseudo)-invariants. Thereby, we can also enforce polyconvexity through physics-informed constraints on the strain-energy and its derivatives in the loss function. Direct prediction of both energy and derivative functions also enables the computation of the elasticity tensor needed for a finite element implementation. Then, we showcase the DNN’s ability by learning the anisotropic mechanical behavior of porcine and murine skin from biaxial test data. Through this example, we find that a multi-fidelity scheme that combines high fidelity experimental data with a low fidelity analytical approximation yields the best performance. Finally, we conduct finite element simulations of tissue expansion using our DNN model to illustrate the potential of data-driven approaches such as ours in medical device design. Also, we expect that the open data and software stemming from this work will broaden the use of data-driven constitutive models in soft tissue mechanics.

A boosted chimp optimizer for numerical and engineering design optimization challenges

Abstract: Chimp optimization algorithm (ChoA) has a wholesome attitude roused by chimp's amazing thinking and hunting ability with a sensual movement for finding the optimal solution in the global search space. Classical Chimps optimizer algorithm has poor convergence and has problem to stuck into local minima for high-dimensional problems. This research focuses on the improved variants of the chimp optimizer algorithm and named as Boosted chimp optimizer algorithms. In one of the proposed variants, the existing chimp optimizer algorithm has been combined with SHO algorithm to improve the exploration phase of the existing chimp optimizer and named as IChoA-SHO and other variant is proposed to improve the exploitation search capability of the existing ChoA. The testing and validation of the proposed optimizer has been done for various standard benchmarks and Non-convex, Non-linear, and typical engineering design problems. The proposed variants have been evaluated for seven standard uni-modal benchmark functions, six standard multi-modal benchmark functions, ten standard fixed-dimension benchmark functions, and 11 types of multidisciplinary engineering design problems. The outcomes of this method have been compared with other existing optimization methods considering convergence speed as well as for searching local and global optimal solutions. The testing results show the better performance of the proposed methods excel than the other existing optimization methods.

A versatile SPH modeling framework for coupled microfluid-powder dynamics in additive manufacturing: binder jetting, material jetting, directed energy deposition and powder bed fusion

Abstract: Abstract Many additive manufacturing (AM) technologies rely on powder feedstock, which is fused to form the final part either by melting or by chemical binding with subsequent sintering. In both cases, process stability and resulting part quality depend on dynamic interactions between powder particles and a fluid phase, i.e., molten metal or liquid binder. The present work proposes a versatile computational modeling framework for simulating such coupled microfluid-powder dynamics problems involving thermo-capillary flow and reversible phase transitions. In particular, a liquid and a gas phase are interacting with a solid phase that consists of a substrate and mobile powder particles while simultaneously considering temperature-dependent surface tension and wetting effects. In case of laser–metal interactions, the effect of rapid evaporation is incorporated through additional mechanical and thermal interface fluxes. All phase domains are spatially discretized using smoothed particle hydrodynamics. The method’s Lagrangian nature is beneficial in the context of dynamically changing interface topologies due to phase transitions and coupled microfluid-powder dynamics. Special care is taken in the formulation of phase transitions, which is crucial for the robustness of the computational scheme. While the underlying model equations are of a very general nature, the proposed framework is especially suitable for the mesoscale modeling of various AM processes. To this end, the generality and robustness of the computational modeling framework is demonstrated by several application-motivated examples representing the specific AM processes binder jetting, material jetting, directed energy deposition, and powder bed fusion. Among others, it is shown how the dynamic impact of droplets in binder jetting or the evaporation-induced recoil pressure in powder bed fusion leads to powder motion, distortion of the powder packing structure, and powder particle ejection.

Mitigation of residual stresses and microstructure homogenization in directed energy deposition processes

Abstract: In additive manufacturing (AM), residual stresses and microstructural inhomogeneity are detrimental to the mechanical properties of as-built AM components. In previous studies, the reduction of the residual stresses and the optimization of the microstructure have been treated separately. Nevertheless, the ability to control both them at the same time is mandatory for improving the final quality of AM parts. This is the main goal of this paper. Thus, a thermo-mechanical finite element model is firstly calibrated by simulating a multi-track 40-layer Ti–6Al–4V block fabricated by directed energy deposition (DED). Next, the numerical tool is used to study the effect of the baseplate dimensions and the energy density on both residual stresses and microstructure evolution. On the one hand, the results indicate that the large baseplate causes higher residual stresses but produces more uniform microstructures, and contrariwise for the smaller baseplate. On the other hand, increasing the energy density favors stress relief, but its effect fails to prevent the stress concentration at the built basement. Based on these results, two approaches are proposed to control both the stress accumulation and the metallurgical evolution during the DED processes: (i) the use of a forced cooling suitable for small baseplates and, (ii) the adoption of grooves when large baseplates are used. The numerical predictions demonstrated the effectiveness of the proposed manufacturing strategies.