SAM
https://sam.ensam.eu:443
The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Mon, 04 Mar 2024 07:45:09 GMT2024-03-04T07:45:09ZInvestigation on reducing geometry files size through floating points indexing
http://hdl.handle.net/10985/16980
Investigation on reducing geometry files size through floating points indexing
VAISSIER, Benjamin; CHOUGRANI, Laurent; VERON, Philippe; PERNOT, Jean-Philippe
In a context of full cooperative data exchanges, frequent transfers between specialized software and remote design and manufacturing, fluidity is the key. It is thus important to reduce the size of data encoding files in order to ease their manipulation. In particular, in the case of 3D-geometry-based processes, triangulated meshes are often used. In such files, the 3D points are localized in space thanks to three coordinates. In order to reduce the size of geometry files, this paper investigates the use of an indexing mechanism to encode these floating-point coordinates.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/169802019-01-01T00:00:00ZVAISSIER, BenjaminCHOUGRANI, LaurentVERON, PhilippePERNOT, Jean-PhilippeIn a context of full cooperative data exchanges, frequent transfers between specialized software and remote design and manufacturing, fluidity is the key. It is thus important to reduce the size of data encoding files in order to ease their manipulation. In particular, in the case of 3D-geometry-based processes, triangulated meshes are often used. In such files, the 3D points are localized in space thanks to three coordinates. In order to reduce the size of geometry files, this paper investigates the use of an indexing mechanism to encode these floating-point coordinates.Lattice support structure discrete optimization for additive manufacturing
http://hdl.handle.net/10985/19984
Lattice support structure discrete optimization for additive manufacturing
VAISSIER, Benjamin; VERON, Philippe; PERNOT, Jean-Philippe
The emergence and improvement of Additive Manufacturing technologies allow the fabrication of complex shapes so far inconceivable. However, to produce those intricate geometries, support structures are required. Besides wasting unnecessary material, these structures are consuming valuable production and post-processing times. This paper proposes a new framework to optimize the geometry and topology of inner and outer support structures. Starting from a uniform lattice structure filling both the inner and outer areas to be supported, the approach removes a maximum number of beams so as to minimize the volume of the support. The geometry of the initial lattice structure is optimized at the beginning considering the possibilities of the manufacturing technologies. Then, the pruning of the structure is performed through a genetic algorithm, the parameters of which have been optimized through a design of experiments. The proposed approach is validated on several test cases of various geometries, containing both inner and outer parts to be supported. The generated support structures are compared to the ones obtained by commercial software.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/199842018-01-01T00:00:00ZVAISSIER, BenjaminVERON, PhilippePERNOT, Jean-PhilippeThe emergence and improvement of Additive Manufacturing technologies allow the fabrication of complex shapes so far inconceivable. However, to produce those intricate geometries, support structures are required. Besides wasting unnecessary material, these structures are consuming valuable production and post-processing times. This paper proposes a new framework to optimize the geometry and topology of inner and outer support structures. Starting from a uniform lattice structure filling both the inner and outer areas to be supported, the approach removes a maximum number of beams so as to minimize the volume of the support. The geometry of the initial lattice structure is optimized at the beginning considering the possibilities of the manufacturing technologies. Then, the pruning of the structure is performed through a genetic algorithm, the parameters of which have been optimized through a design of experiments. The proposed approach is validated on several test cases of various geometries, containing both inner and outer parts to be supported. The generated support structures are compared to the ones obtained by commercial software.Parametric design of graded truss lattice structures for enhanced thermal dissipation
http://hdl.handle.net/10985/16741
Parametric design of graded truss lattice structures for enhanced thermal dissipation
VAISSIER, Benjamin; CHOUGRANI, Laurent; VERON, Philippe; PERNOT, Jean-Philippe
Truss lattice structures are intricate geometries, whose fabrication has recently been simplified by the development of Additive Manufacturing (AM) technologies. These lightweight geometries present great volume densities and surface-to-occupancy ratios, which makes them ideal for thermal dissipation applications. This paper introduces a new framework for the parametric design of graded truss lattice structures that maximize passive cooling. It exploits the results of a semi-analytic formulation and analysis of the volume density and surface-to-occupancy ratio of state-of-the-art unit cells. In particular, it comes out that any truss lattice structure presents an optimal beam diameter over unit cell size ratio that maximizes its surface-to-occupancy value. This value and the ratio for which it is reached are identified and compared for the most common unit cells. The unit cell with the maximal surface-to-occupancy ratio is then identified, along with its set of optimal parameters, taking into account additive manufacturing constraints. The validation of this optimal geometry is performed by populating pre-defined design spaces of both academic and industrial case studies. An orientation strategy and a parametric gradation approach are also proposed to further optimize the generated heat sinks and maximize passive cooling. These results are very helpful to support decision making during the parametric design of a heat sink and to identify, a priori, the optimal unit cell, its control parameters, its orientation and its gradation strategy. The generated geometries are compared with traditional heat sink structures through static heat dissipation simulations, in order to demonstrate their interest.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/167412019-01-01T00:00:00ZVAISSIER, BenjaminCHOUGRANI, LaurentVERON, PhilippePERNOT, Jean-PhilippeTruss lattice structures are intricate geometries, whose fabrication has recently been simplified by the development of Additive Manufacturing (AM) technologies. These lightweight geometries present great volume densities and surface-to-occupancy ratios, which makes them ideal for thermal dissipation applications. This paper introduces a new framework for the parametric design of graded truss lattice structures that maximize passive cooling. It exploits the results of a semi-analytic formulation and analysis of the volume density and surface-to-occupancy ratio of state-of-the-art unit cells. In particular, it comes out that any truss lattice structure presents an optimal beam diameter over unit cell size ratio that maximizes its surface-to-occupancy value. This value and the ratio for which it is reached are identified and compared for the most common unit cells. The unit cell with the maximal surface-to-occupancy ratio is then identified, along with its set of optimal parameters, taking into account additive manufacturing constraints. The validation of this optimal geometry is performed by populating pre-defined design spaces of both academic and industrial case studies. An orientation strategy and a parametric gradation approach are also proposed to further optimize the generated heat sinks and maximize passive cooling. These results are very helpful to support decision making during the parametric design of a heat sink and to identify, a priori, the optimal unit cell, its control parameters, its orientation and its gradation strategy. The generated geometries are compared with traditional heat sink structures through static heat dissipation simulations, in order to demonstrate their interest.Genetic-algorithm based framework for lattice support structure optimization in additive manufacturing
http://hdl.handle.net/10985/16940
Genetic-algorithm based framework for lattice support structure optimization in additive manufacturing
VAISSIER, Benjamin; CHOUGRANI, Laurent; VERON, Philippe; PERNOT, Jean-Philippe
The emergence and improvement of Additive Manufacturing technologies allow the fabrication of complex shapes so far inconceivable. However, to produce those intricate geometries, support structures are required. Besides wasting unnecessary material, these structures are consuming valuable production and post-processing times. This paper proposes a new framework to optimize the geometry and topology of inner and outer support structures. Starting from a uniform lattice structure filling both the inner and outer areas to be supported, the approach removes a maximum number of beams so as to minimize the volume of the support. The most suited geometry for the initial lattice structure is defined at the beginning considering the possibilities of the manufacturing technologies. Then, the pruning of the structure is performed through a genetic algorithm, for which the control parameters values have been tuned through a design of experiments. The proposed approach is validated on several test cases of various geometries, containing both inner and outer areas to be supported. The generated support structures are compared to the ones obtained by several state-of-the-art support structure strategies and are proved to have lower material consumption.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/169402019-01-01T00:00:00ZVAISSIER, BenjaminCHOUGRANI, LaurentVERON, PhilippePERNOT, Jean-PhilippeThe emergence and improvement of Additive Manufacturing technologies allow the fabrication of complex shapes so far inconceivable. However, to produce those intricate geometries, support structures are required. Besides wasting unnecessary material, these structures are consuming valuable production and post-processing times. This paper proposes a new framework to optimize the geometry and topology of inner and outer support structures. Starting from a uniform lattice structure filling both the inner and outer areas to be supported, the approach removes a maximum number of beams so as to minimize the volume of the support. The most suited geometry for the initial lattice structure is defined at the beginning considering the possibilities of the manufacturing technologies. Then, the pruning of the structure is performed through a genetic algorithm, for which the control parameters values have been tuned through a design of experiments. The proposed approach is validated on several test cases of various geometries, containing both inner and outer areas to be supported. The generated support structures are compared to the ones obtained by several state-of-the-art support structure strategies and are proved to have lower material consumption.Lightweight Mesh File Format Using Repetition Pattern Encoding for Additive Manufacturing
http://hdl.handle.net/10985/19955
Lightweight Mesh File Format Using Repetition Pattern Encoding for Additive Manufacturing
VAISSIER, Benjamin; CHOUGRANI, Laurent; VÉRON, Philippe; PERNOT, Jean-Philippe
To facilitate the transfer, storage and manipulation of intricate parts’ geometry whose fabrication has been made possible thanks to the rise of Additive Manufacturing (AM) technologies, an encoding framework reducing the resulting file size has been developed. This approach leverages the fact that many AM parts are presenting repetition patterns, by encoding the repetition of similar geometry chunks. The decomposition of the part into chunks is a complex optimization problem, whose identification as a Weighted Exact Cover (WEC) problem allowed to develop a new heuristic algorithm dedicated to its fast resolution in linear time . The encoding strategy is implemented through a variation of the AMF file standard (for quick adoption of the format by existing software), and also through a new ad-hoc hybrid file format. To demonstrate the efficiency of the approach, the encryption of lattice and support structures through these two encoding strategies are compared to the results of several state-of-the-art encoding approaches. The way this data weight lightening strategy preserves the overall accuracy is discussed while considering different floating points encoding precisions with respect to the AM process requirements. This comparison exhibits file size reductions up to -84% in comparison with file sizes generated by state-of-the-art approaches. Not only the proposed repetition pattern encoding framework allows file size reductions, but it could also be exploited to optimize and speed-up some steps of the Product Development Process (PDP), including process planning phases.
Wed, 01 Jan 2020 00:00:00 GMThttp://hdl.handle.net/10985/199552020-01-01T00:00:00ZVAISSIER, BenjaminCHOUGRANI, LaurentVÉRON, PhilippePERNOT, Jean-PhilippeTo facilitate the transfer, storage and manipulation of intricate parts’ geometry whose fabrication has been made possible thanks to the rise of Additive Manufacturing (AM) technologies, an encoding framework reducing the resulting file size has been developed. This approach leverages the fact that many AM parts are presenting repetition patterns, by encoding the repetition of similar geometry chunks. The decomposition of the part into chunks is a complex optimization problem, whose identification as a Weighted Exact Cover (WEC) problem allowed to develop a new heuristic algorithm dedicated to its fast resolution in linear time . The encoding strategy is implemented through a variation of the AMF file standard (for quick adoption of the format by existing software), and also through a new ad-hoc hybrid file format. To demonstrate the efficiency of the approach, the encryption of lattice and support structures through these two encoding strategies are compared to the results of several state-of-the-art encoding approaches. The way this data weight lightening strategy preserves the overall accuracy is discussed while considering different floating points encoding precisions with respect to the AM process requirements. This comparison exhibits file size reductions up to -84% in comparison with file sizes generated by state-of-the-art approaches. Not only the proposed repetition pattern encoding framework allows file size reductions, but it could also be exploited to optimize and speed-up some steps of the Product Development Process (PDP), including process planning phases.