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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Fri, 23 Feb 2024 01:17:17 GMT2024-02-23T01:17:17ZA general surface reconstruction method for post-processing of topology optimisation results
http://hdl.handle.net/10985/16996
A general surface reconstruction method for post-processing of topology optimisation results
BERTOLINO, Giulia; COSTA, Giulio; POURROY, Franck; PERRY, Nicolas; MONTEMURRO, Marco
In this work, a new semi-automatic surface reconstruction procedure is proposed. The main goal of the method is to reconstruct the boundary surface of a triangulation obtained as a result of a topology optimisation calculation. The reconstruction problem is articulated in two main phases: tessellation mapping and surface fitting. The first phase consists of retrieving a suitable map of the triangulation representing the boundary of the optimised topology. To this purpose, a segmentation of the original triangulation is performed and sub-domains (i.e. patches) are identified by means of a semi-automatic strategy. Then, a shape preserving parametrisation algorithm [1] is used on each patch in order to carry out the mapping and to preserve the real 3D shape of the boundary. The second phase deals with an original approach to the surface fitting problem: the problem is stated as a Constrained Non-Linear Programming Problem (CNLPP) by introducing a constraint on the maximum value of the Gaussian curvature of the boundary surface. In this study, the surface fitting problem is solved in the framework of Non-Uniform Rational Basis Splines (NURBS) surfaces. The main idea is to keep all the parameters defining the NURBS surface as design variables in order to state the surface fitting problem in the most general sense. Nevertheless, this fact implies two consequences of paramount importance, constituting just as many difficulties in solving the related CNLPP. Firstly, when the surface fitting problem is formulated by including the number of control points and the degrees of the basis functions among the unknowns, the overall number of design variables for the problem at hand is not fixed a-priori: hence, the resulting CNLPP is defined over a search space of variable dimension. Secondly, the numerical strategy chosen to face such a problem must be able to handle design variables of different nature and to optimise, at the same time, the dimension of the design domain as well as the value of each constitutive parameter of the NURBS surface. In order to overcome the two aforementioned issues, the surface fitting phase is composed of two optimisation steps. Firstly, the ERASMUS (EvolutionaRy Algorithm for optimiSation of ModUlar Systems) tool [2] optimises both the value and the number of design variables by means of a two-level Darwinian strategy, allowing the simultaneous evolution of individuals and species. Secondly, the optimum solution provided by ERASMUS constitutes the initial guess for the local gradient-based optimization, which aims at improving the accuracy of the fitting surface. The proposed method coupled with the NURBS based SIMP algorithm [3], represents a valid solution for the semi-automatic post-processing of complex 3D shapes resulting from topology optimisation.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/169962019-01-01T00:00:00ZBERTOLINO, GiuliaCOSTA, GiulioPOURROY, FranckPERRY, NicolasMONTEMURRO, MarcoIn this work, a new semi-automatic surface reconstruction procedure is proposed. The main goal of the method is to reconstruct the boundary surface of a triangulation obtained as a result of a topology optimisation calculation. The reconstruction problem is articulated in two main phases: tessellation mapping and surface fitting. The first phase consists of retrieving a suitable map of the triangulation representing the boundary of the optimised topology. To this purpose, a segmentation of the original triangulation is performed and sub-domains (i.e. patches) are identified by means of a semi-automatic strategy. Then, a shape preserving parametrisation algorithm [1] is used on each patch in order to carry out the mapping and to preserve the real 3D shape of the boundary. The second phase deals with an original approach to the surface fitting problem: the problem is stated as a Constrained Non-Linear Programming Problem (CNLPP) by introducing a constraint on the maximum value of the Gaussian curvature of the boundary surface. In this study, the surface fitting problem is solved in the framework of Non-Uniform Rational Basis Splines (NURBS) surfaces. The main idea is to keep all the parameters defining the NURBS surface as design variables in order to state the surface fitting problem in the most general sense. Nevertheless, this fact implies two consequences of paramount importance, constituting just as many difficulties in solving the related CNLPP. Firstly, when the surface fitting problem is formulated by including the number of control points and the degrees of the basis functions among the unknowns, the overall number of design variables for the problem at hand is not fixed a-priori: hence, the resulting CNLPP is defined over a search space of variable dimension. Secondly, the numerical strategy chosen to face such a problem must be able to handle design variables of different nature and to optimise, at the same time, the dimension of the design domain as well as the value of each constitutive parameter of the NURBS surface. In order to overcome the two aforementioned issues, the surface fitting phase is composed of two optimisation steps. Firstly, the ERASMUS (EvolutionaRy Algorithm for optimiSation of ModUlar Systems) tool [2] optimises both the value and the number of design variables by means of a two-level Darwinian strategy, allowing the simultaneous evolution of individuals and species. Secondly, the optimum solution provided by ERASMUS constitutes the initial guess for the local gradient-based optimization, which aims at improving the accuracy of the fitting surface. The proposed method coupled with the NURBS based SIMP algorithm [3], represents a valid solution for the semi-automatic post-processing of complex 3D shapes resulting from topology optimisation.An Efficient Hybrid Optimization Strategy for Surface Reconstruction
http://hdl.handle.net/10985/20261
An Efficient Hybrid Optimization Strategy for Surface Reconstruction
BERTOLINO, Giulia; POURROY, Franck; PERRY, Nicolas; MONTEMURRO, Marco
An efficient and general surface reconstruction strategy is presented in this study. The proposed approach can deal with both open and closed surfaces of genus greater than or equal to zero and it is able to approximate non-convex sets of target points (TPs). The surface reconstruction strategy is split into two main phases: (a) the mapping phase, which makes use of the shape preserving method (SPM) to get a proper parametrisation of each sub-domain composing the TPs set; (b) the fitting phase, where each patch is fitted by means of a suitable Non-Uniform Rational Basis Spline (NURBS) surface without introducing simplifying hypotheses and/or rules on the parameters tuning the shape of the parametric entity. Indeed, the proposed approach aims stating the surface fitting problem in the most general sense, by integrating the full set of design variables (both integer and continuous) defining the shape of the NURBS surface. To this purpose, a new formulation of the surface fitting problem is proposed: it is stated in the form of a special Constrained Non-Linear Programming Problem (CNLPP) defined over a domain having variable dimension, wherein both the number and the value of the design variables are simultaneously optimised. To deal with this class of CNLPPs, a hybrid optimisation tool has been employed. The optimisation procedure is split in two steps: firstly, an improved genetic algorithm (GA) optimises both the value and the number of design variables by means of a two-level Darwinian strategy allowing the simultaneous evolution of individuals and species; secondly, the solution provided by the GA constitutes the initial guess for the subsequent deterministic optimisation, which aims at improving the accuracy of the fitting surfaces. The effectiveness of the proposed methodology is proven through some meaningful benchmarks taken from the literature.
Fri, 01 Jan 2021 00:00:00 GMThttp://hdl.handle.net/10985/202612021-01-01T00:00:00ZBERTOLINO, GiuliaPOURROY, FranckPERRY, NicolasMONTEMURRO, MarcoAn efficient and general surface reconstruction strategy is presented in this study. The proposed approach can deal with both open and closed surfaces of genus greater than or equal to zero and it is able to approximate non-convex sets of target points (TPs). The surface reconstruction strategy is split into two main phases: (a) the mapping phase, which makes use of the shape preserving method (SPM) to get a proper parametrisation of each sub-domain composing the TPs set; (b) the fitting phase, where each patch is fitted by means of a suitable Non-Uniform Rational Basis Spline (NURBS) surface without introducing simplifying hypotheses and/or rules on the parameters tuning the shape of the parametric entity. Indeed, the proposed approach aims stating the surface fitting problem in the most general sense, by integrating the full set of design variables (both integer and continuous) defining the shape of the NURBS surface. To this purpose, a new formulation of the surface fitting problem is proposed: it is stated in the form of a special Constrained Non-Linear Programming Problem (CNLPP) defined over a domain having variable dimension, wherein both the number and the value of the design variables are simultaneously optimised. To deal with this class of CNLPPs, a hybrid optimisation tool has been employed. The optimisation procedure is split in two steps: firstly, an improved genetic algorithm (GA) optimises both the value and the number of design variables by means of a two-level Darwinian strategy allowing the simultaneous evolution of individuals and species; secondly, the solution provided by the GA constitutes the initial guess for the subsequent deterministic optimisation, which aims at improving the accuracy of the fitting surfaces. The effectiveness of the proposed methodology is proven through some meaningful benchmarks taken from the literature.Multi-scale shape optimisation of lattice structures: an evolutionary-based approach
http://hdl.handle.net/10985/16978
Multi-scale shape optimisation of lattice structures: an evolutionary-based approach
BERTOLINO, Giulia; DE PASQUALE, Giorgio; MONTEMURRO, Marco
This study deals with the problem of the least-weight design of a lattice structure subject to constraints of different nature. To face this problem, a general multi-scale optimisation procedure is proposed. This approach aims at optimising simultaneously both global and local geometric parameters defining the shape of the representative volume element (RVE) of the lattice at the mesoscopic scale. The optimisation procedure involves design requirements defined at different scales: geometric and manufacturing constraints are involved at the mesoscopic scale, whilst thermodynamic constraints on the positive definiteness of the stiffness tensor of the lattice (modelled as an equivalent homogeneous anisotropic medium) intervene at the macroscopic scale. Finally, since lattice structures usually undergo compressive loads, a requirement on the first local buckling load is considered too. The proposed approach is based on (a) the Non-Uniform Rational Basis Splines (NURBS) curves theory to describe the shape of the struts composing the lattice, (b) the strain energy homogenisation technique of periodic media to perform the scale transition and (c) a special genetic algorithm to perform optimisation calculations. The optimised solutions provided by the presented method are characterised by a weight saving of about 39% with slightly enhanced mechanical properties when compared to conventional octahedral lattice structures.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/169782019-01-01T00:00:00ZBERTOLINO, GiuliaDE PASQUALE, GiorgioMONTEMURRO, MarcoThis study deals with the problem of the least-weight design of a lattice structure subject to constraints of different nature. To face this problem, a general multi-scale optimisation procedure is proposed. This approach aims at optimising simultaneously both global and local geometric parameters defining the shape of the representative volume element (RVE) of the lattice at the mesoscopic scale. The optimisation procedure involves design requirements defined at different scales: geometric and manufacturing constraints are involved at the mesoscopic scale, whilst thermodynamic constraints on the positive definiteness of the stiffness tensor of the lattice (modelled as an equivalent homogeneous anisotropic medium) intervene at the macroscopic scale. Finally, since lattice structures usually undergo compressive loads, a requirement on the first local buckling load is considered too. The proposed approach is based on (a) the Non-Uniform Rational Basis Splines (NURBS) curves theory to describe the shape of the struts composing the lattice, (b) the strain energy homogenisation technique of periodic media to perform the scale transition and (c) a special genetic algorithm to perform optimisation calculations. The optimised solutions provided by the presented method are characterised by a weight saving of about 39% with slightly enhanced mechanical properties when compared to conventional octahedral lattice structures.Cellular structures from additive processes: design, homogenization and experimental validation
http://hdl.handle.net/10985/17331
Cellular structures from additive processes: design, homogenization and experimental validation
DE PASQUALE, Giorgio; CATAPANO, Anita; BERTOLINO, Giulia; REVELLI, Luca; MONTEMURRO, Marco
The importance of lightweight structures in many fields of engineering is well known since long time. The innovations in technological processes based on material addiction allow pushing the design towards challenging geometries and associated structural properties. Engineered materials like lattice structures can be theoretically used to modify the local material properties and strength with minimization of the mass of components; in practice, several issues are still to be solved in stabilization of additive processes and achieving repeatable structures able to pass qualification procedures. At this purpose, dedicated experimental and design methods like those reported in this paper are needed.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/173312018-01-01T00:00:00ZDE PASQUALE, GiorgioCATAPANO, AnitaBERTOLINO, GiuliaREVELLI, LucaMONTEMURRO, MarcoThe importance of lightweight structures in many fields of engineering is well known since long time. The innovations in technological processes based on material addiction allow pushing the design towards challenging geometries and associated structural properties. Engineered materials like lattice structures can be theoretically used to modify the local material properties and strength with minimization of the mass of components; in practice, several issues are still to be solved in stabilization of additive processes and achieving repeatable structures able to pass qualification procedures. At this purpose, dedicated experimental and design methods like those reported in this paper are needed.