Abstract
This article presents the design process for generating a shell-like structure from an activated bent auxetic surface through an inductive process based on applying deep learning algorithms to predict a numeric value of geometrical features. The process developed under the Material Intelligence Workflow applied to the development of (1) a computational simulation of the mechanical and physical behaviour of an activated auxetic surface, (2) the generation of a geometrical dataset composed of six geometric features with 3,000 values each, (3) the construction and training of a regression Deep Neuronal Network (DNN) model, (4) the prediction of the geometric feature of the auxetic surface's pattern distance, and (5) the reconstruction of a new shell based on the predicted value. This process consistently reduces the computational power and simulation time to produce digital prototypes by integrating AI-based algorithms into material computation design processes.
You have full access to this open access chapter, Download conference paper PDF
Similar content being viewed by others
Keywords
1 Introduction
The emergent applications of Artificial Intelligence algorithms in architectural and design practices opened a wide range of novel methods and processes to envision, create and optimize the design process, varying from the scale of urban design to the exploration of synthetic spaces. Naturally, data and the way how are organized plays a crucial role in the entire process, which can vary from datasets of images to numeric values. Nevertheless, applying AI-based algorithms to material computation requires a linear workflow to generate a specific dataset of values that can precisely represent a geometry. Requires that the design process be based on geometrical features that represent its physical characteristics.
This research project utilizes a regression Deep Neuronal Network to predict the distances of each cell's patterns from an auxetic surface that was previously actively bent, based on its Gaussian curvature, osculating point, pattern distance and the applied force parameters.
Auxetics are metamaterial structures with a negative Poisson’s ratio, in which the mechanical performance relies on the geometry rather than on the material itself. When stretched, they become thicker in the perpendicular direction to the applied force. This phenomenon occurs due to their internal structure and how this deforms when the sample is uniaxially loaded.
This makes them a material system with a wide range of applications on the architectural scale, for example, by reducing the amount of energy needed to create a three-dimensional shape [1], by distributing the internal mechanical forces of the system, achieving a relaxed and stable form.
Computing bent activated auxetic surfaces requires simulating each cell's behaviour that composes the surface and its global deformation, demanding a significant computational power. By creating and training a tailored regression DNN model, the simulation time and power can be reduced by just a fraction, offering the users an interactive tool to input the desired performance and receive a precise predicted pattern.
1.1 Auxetic Structures
Auxetic structures present the unique capacity of becoming wider when stretched and narrower when compressed [2]. The word auxetic comes from the Greek word αὐξητικός (auxetikos) which means “tends to increase”. Some of its edges and vertices work under compression to give a material the capacity to extend, reducing one axis its length, thus giving space for the other edges and vertices to elongate and, consequently, make the system extend. This relationship between compression and traction forces is defined as the Poisson ratio (v), which is the ratio of the transverse contraction of a material to the strain in the direction of the stretching force [3]. A negative Poisson ratio occurs when compression forces are applied, and, in contrast, the Poisson ratio is positive when there is tensile deformation (Fig. 1). The Poisson ratio values could have a wider range of values for anisotropic materials than isotropic materials.
Bi-dimensional auxetic structures are results from the tessellation of a given plane with periodic regular polygons [4] working as an individual but concatenated cell, where its deformation magnitude and direction are directly related to the Poisson ratio. Because of this, auxetic structures materials should have a low density or be flexible. From this basic definition of the deformation ratio, auxetic surfaces have specific characteristics that contribute to a more refined understanding of their variabilities and geometric properties [5] as a material system:
-
Shear properties: shear modulus can be similar to the bulk module, meaning that the structure becomes hard to break but easy to deform.
-
Indentation resistance: Hardness can increase in an auxetic material due to its negative Poisson’s ratio.
-
Fracture toughness: because of the displacement for geometry, it possesses more crack resistance to fracture.
-
Synclastic curvature: will form a synclastic curvature geometry by a natural distribution of its internal forces.
-
Energy absorption: auxetic structures have a high capacity to absorb constant deformation loads in low frequencies.
-
Variable permeability: The pore-opening properties offer a high filtering capacity on the micro and macro scale.
1.2 Generative Particle-Spring System for Material Behaviour Simulation
A particle-spring system consists of particles given mass and position, which are connected by springs with stiffness and rest length [6]. By applying an external force over the network of particles and springs, each particle moves, affecting the others producing a concatenated deformation because of the springs, distributing the embedded energy to find its equilibrium state.
Physics simulators engines for generative design software such as Kangaroo Physics are powerful tools to simulate and fast preview the physical deformation of geometries submitted to external forces over a predefined geometrical system. Offering the capacity to compute the synclastic geometries generated by the application of tension in opposite axis directions on auxetic structures. Particle-Spring system simulations have been successfully applied to test different auxetic properties and the influence on the Poisson ratio when the internal angle of the hexagons of an auxetic cell is changed [7].
Despite the good performance of particle-spring physics simulators to quickly simulate geometric deformations, this process is based on iterative methods. It requires computing several variations of the same model demanding high computational power to simulate complex models.
1.3 Artificial Intelligence for Material Prediction
The application of machine learning algorithms to find a specific solution to a given problem is not new. In the early 90’s, shallow learning algorithms were used in the process of using inductive systems in knowledge acquisition [8] for the application of different civil engineering purposes, improving the understanding of a given domain through the systematic development of a system of decision rules governing that domain [9]. Problems that share the same domain are among the most common potential applications of trained Artificial Neuronal Network (ANN) models [10]. Offering one substantial difference from conventional iterative processes for searching for potential solutions. Because solutions emerge from local rules, exploring new outcomes relies on the generation of new global results [11].
These processes follow a common strategy: (1) the generation of a geometry-based dataset after optimization or physical simulation process; (2) architecture definition and training of an Artificial Neural Network (ANN); (3) prediction of the output value; (4) reconstruction of the global geometry.
Because Linear Regression (LR) models can only predict a specific value with a linear relationship between the features and the target, the ANN models require the target values to be continuous from an interval. Because Deep Learning algorithms work like the human brain neurons, it consists of an Input layer, Hidden layers and an Output layer, which can learn the complex relationship between the features and target due to the presence of activation function in each layer [12]. For this, a Forward propagation process for multiplying weights of each feature by adding them and a Backward propagation for updating the weights–requiring optimization and loss functions in the model- are enabled.
2 Research Objectives, Methods, and Processes
This research aims to build a clear workflow to predict an auxetic structure three-dimensional deformation pattern from a series of given properties as inputs to reconstruct the structure computationally. For this, a dataset generation is required from a base geometry with a defined set of rules after being subjected to a simulation of physical deformation activated with a vertical force in an active bending process. A Particle-Spring (PS) physics simulator is used to study the morphological modification of the deformed structure by measuring its geometrical features and then exported as a value dataset. Due to the high computational power required to simulate complex or large geometries, a trained ANN model with the dataset is used to predict, bypassing the physics simulator, and reducing computational power and time considerably. To achieve the research follows a workflow composed of five steps:
-
Computational simulation of the mechanical and physical behavior of an activated auxetic surface.
-
Parametric workflow for the generation of datasets of material features.
-
The construction and training of a regression Deep Neuronal Network (DNN).
-
Prediction of a specific feature of the geometry (output_Y) from given features values (input_X).
-
Reconstruction of the material system geometry of the predicted structure based on feature inputs.
2.1 Generative Physics Simulation
A ten-by-ten cell of 1 by 1 unit auxetic structure geometry was used as basic geometry. The edges of each cell work like pivots that moves towards its centre point at a relative distance between its centre point and the global force point. This movement gives the auxetic properties to the structure. If a cell centre point is closer to the vector force, the displacement is bigger, increasing its flexibility; on the contrary, the greater the distance, the displacement value is lower increasing its stiffness (Fig. 2).
The auxetic structure was input as a rigid body in a PS physics simulator. Each of the four vertices of the structure served as anchor points. The vector force was the force that randomly changed its position and amplitude, generating different outcomes from the simulator.
After applying a horizontal force to the global surface, the pivot point of each cell was deformed and displaced, generating a vector pointing to the interior of each cell. Finally, vector normals were reoriented to each cell, and its length was used to rebuild the surface in two dimensions. That length becomes extremely important in this process; the surface can be rebuilt in two dimensions to generate a specific three-dimensional deformation and simplify the potential manufacturing process.
2.2 Dataset Generation
The simulated geometry in the PS simulator was analyzed to extract the values of each geometric feature (Fig. 4), the six global features [13]: the osculating point and the Gaussian curvature of the global deformation, the start and endpoints from the U and V coordinates, and the displacement distance of the pivot point (Fig. 3). Also, the local cell data was extracted: the centroid position in x, y and z, U and V nodes coordinates, and each pivot point displacement distance.
With this data a dataset of 3.000 values per feature was generated and to define the input_X–features to feed the network–, and output _Y–feature to predict-, which is associated with the pattern_distance.
2.3 Deep Neuronal Network Architecture Model
A Principal Components Analysis (PCA) was initiated to understand the correlation between the global features of the system and the local features of each cell, in order to detect and select the input features for training the model and to generate the output feature to predict (Table 1), the value from which the structure will be reconstructed.
After several iterations, an ANN Model was composed of six Dense Layers with a rectified linear activation function ReLU, and one Dense layer with a sigmoid activation to predict the value. The model was trained with the dataset produced under different geometrical deformations (Fig. 5), with 100 epochs and a validation split of 20%.
2.4 Geometry Reconstruction
The predicted values of the displacement were associated to each of the four pivots of each cell in x, y and z dimensions (Fig. 6). This allowed the rebuilding of the shell structure by reversing the parametric design process and bypassing the PS simulator (Fig. 7), at the same time, the final pattern was redrawn in two dimensions for a potential 3d printing manufacturing process.
3 Conclusions
This workflow offers a novel way to optimize and reduce the computational power needed to compute the three-dimensional physical deformation of structures and to invert the design process allowing the designer to input the desired parameter retrieving the optimal solution within the AI-based design model.
The material strength can be considerably optimized through geometry by applying an integrated material intelligence workflow to develop digital prototypes, decreasing the amount of embedded required energy during the design and manufacturing processes.
The next steps could be based on the comparison and analysis of the predicted values between this research workflow with Regression models plugins and Particle-Systems simulators in a generative software. Along with testing the process with other shell structures by increasing the heights and the tridimensionality of the structures.
References
Vivanco T, Valencia A, Yuan PF (2020) 4D printing: computational mechanical design of bi-dimensional 3D printed patterns over tensioned textiles for low-energy three-dimensional volumes. In: Proceedings of the 25th CAADRIA conference
Evans KE, Nkansah MA, Hutchinson IJ, Rogers SC (1991) Molecular network design. Nature 353:124
Evans KE (1991) Design of doubly-curved sandwich panels with honeycomb cores. Compos Struct 17:95–111
Daekwon PJ, Romo A (2015) Poisson’s ratio material distributions. Emerging experience in past, present and future of digital architecture. In: Proceedings of the 20th International conference of the association for computer-aided architectural design research in Asia (CAADRIA 2015)/Daegu 20–22 May 2015. pp 735–744
Liu Y, Hu H (2010) A review on auxetic structures and polymeric materials. Sci Res Essays 5(10):1052–1063
Bertin T (2001) Evaluating the use of particle-spring systems in the conceptual design of grid shell structures. Thesis Master of Engineering in Civil and Environental Engineering, Massachusetts Institute of Technology
Naboni R, Mirante L (2015) Metamaterial computation and fabrication of auxetic patterns for architecture. 129–136. https://doi.org/10.5151/despro-sigradi2015-30268
Hanna S (2007) Inductive machine learning of optimal modular structures: estimating solutions using support vector machines. Artif Intell Eng Des Anal Manuf 21(4):351–366. https://doi.org/10.1017/S0890060407000327
Arciszewski T, Ziarko W (1990) Inductive learning in civil engineering: rough sets approach. Comp. Aided Civil Infrastr Eng 5:19–28. https://doi.org/10.1111/j.1467-8667.1990.tb00038.x
Reich Y, Barai SV (1999) Evaluating machine learning models for engineering problems. Artif Intell Eng 13(1999):257–272
Aksöz Z, Preisinger C (2020) An interactive structural optimization of space frame structures using machine learning. In: Gengnagel C, Baverel O, Burry J, Ramsgaard Thomsen M, Weinzierl S (eds) Impact: design with all senses. DMSB 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-29829-6_2
Razi M, Athappilly K (2005) A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models. Expert Syst Appl 29(1):65–74
La Magna R, Knippers J (2018) Tailoring the bending behaviour of material patterns for the induction of double curvature. https://doi.org/10.1007/978-981-10-6611-5_38
Acknowledgements
Antonia Valencia, Tomas Sanchez for the initial studies, Faculty of Architecture, Design and Urban Studies and the Digital Fabrication Laboratory of the Pontifical Catholic University of Chile. Fab Lab Barcelona and the Institute for Advanced Architecture of Catalonia.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Copyright information
© 2023 The Author(s)
About this paper
Cite this paper
Vivanco, T., Ojeda, J., Yuan, P. (2023). Regression-Based Inductive Reconstruction of Shell Auxetic Structures. In: Yuan, P.F., Chai, H., Yan, C., Li, K., Sun, T. (eds) Hybrid Intelligence. CDRF 2022. Computational Design and Robotic Fabrication. Springer, Singapore. https://doi.org/10.1007/978-981-19-8637-6_42
Download citation
DOI: https://doi.org/10.1007/978-981-19-8637-6_42
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-8636-9
Online ISBN: 978-981-19-8637-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)