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A platform for research: civil engineering, architecture and urbanism
PATO: Producibility-Aware Topology Optimization Using Deep Learning for Metal Additive Manufacturing
https://orcid.org/http://orcid.org/0000-0002-8999-9759
This paper introduces PATO - a producibility-aware topology optimization (TO) framework to help efficiently explore the design space of components fabricated using metal additive manufacturing(AM), while ensuring manufacturability. Specifically, parts fabricated through Laser Powder Bed Fusion (LPBF) are prone to defects such as warpage or cracking due to high residual stress values generated from the steep thermal gradients produced during the build process. PATO is based on the a priori discovery of crack-free designs, so that the optimized part can be built defect-free at the outset. To ensure that the design is crack free, producibility is explicitly encoded within the standard formulation of TO, using maximum shear strain index (MSSI) as a crack index. Simulating the build process, in order to estimate MSSI, is a coupled, multi-physics, time-complex computation and incorporating it in the TO loop can be computationally prohibitive. Current advances in deep convolutional neural networks (DCNN) are leveraged to develop a high-fidelity surrogate model based on an Attention-based U-Net architecture to predict the MSSI values as a spatially varying field over the part’s domain. Further, automatic differentiation is employed to directly compute the gradient of maximum MSSI with respect to the input design variables and augment it with the performance-based sensitivity field to optimize the design while considering the trade-off between weight, manufacturability, and functionality. The effectiveness of the proposed method is demonstrated through benchmark studies in 3D and experimental validation.
PATO: Producibility-Aware Topology Optimization Using Deep Learning for Metal Additive Manufacturing
https://orcid.org/http://orcid.org/0000-0002-8999-9759
This paper introduces PATO - a producibility-aware topology optimization (TO) framework to help efficiently explore the design space of components fabricated using metal additive manufacturing(AM), while ensuring manufacturability. Specifically, parts fabricated through Laser Powder Bed Fusion (LPBF) are prone to defects such as warpage or cracking due to high residual stress values generated from the steep thermal gradients produced during the build process. PATO is based on the a priori discovery of crack-free designs, so that the optimized part can be built defect-free at the outset. To ensure that the design is crack free, producibility is explicitly encoded within the standard formulation of TO, using maximum shear strain index (MSSI) as a crack index. Simulating the build process, in order to estimate MSSI, is a coupled, multi-physics, time-complex computation and incorporating it in the TO loop can be computationally prohibitive. Current advances in deep convolutional neural networks (DCNN) are leveraged to develop a high-fidelity surrogate model based on an Attention-based U-Net architecture to predict the MSSI values as a spatially varying field over the part’s domain. Further, automatic differentiation is employed to directly compute the gradient of maximum MSSI with respect to the input design variables and augment it with the performance-based sensitivity field to optimize the design while considering the trade-off between weight, manufacturability, and functionality. The effectiveness of the proposed method is demonstrated through benchmark studies in 3D and experimental validation.
PATO: Producibility-Aware Topology Optimization Using Deep Learning for Metal Additive Manufacturing
https://orcid.org/http://orcid.org/0000-0002-8999-9759
This paper introduces PATO - a producibility-aware topology optimization (TO) framework to help efficiently explore the design space of components fabricated using metal additive manufacturing(AM), while ensuring manufacturability. Specifically, parts fabricated through Laser Powder Bed Fusion (LPBF) are prone to defects such as warpage or cracking due to high residual stress values generated from the steep thermal gradients produced during the build process. PATO is based on the a priori discovery of crack-free designs, so that the optimized part can be built defect-free at the outset. To ensure that the design is crack free, producibility is explicitly encoded within the standard formulation of TO, using maximum shear strain index (MSSI) as a crack index. Simulating the build process, in order to estimate MSSI, is a coupled, multi-physics, time-complex computation and incorporating it in the TO loop can be computationally prohibitive. Current advances in deep convolutional neural networks (DCNN) are leveraged to develop a high-fidelity surrogate model based on an Attention-based U-Net architecture to predict the MSSI values as a spatially varying field over the part’s domain. Further, automatic differentiation is employed to directly compute the gradient of maximum MSSI with respect to the input design variables and augment it with the performance-based sensitivity field to optimize the design while considering the trade-off between weight, manufacturability, and functionality. The effectiveness of the proposed method is demonstrated through benchmark studies in 3D and experimental validation.
PATO: Producibility-Aware Topology Optimization Using Deep Learning for Metal Additive Manufacturing
Int J Interact Des Manuf
Iyer, Naresh (author) / Mirzendehdel, Amir M. (author) / Raghavan, Sathya (author) / Jiao, Yang (author) / Ulu, Erva (author) / Behandish, Morad (author) / Nelaturi, Saigopal (author) / Robinson, Dean (author)
2024-12-01
18 pages
Article (Journal)
Electronic Resource
English
Design for manufacturing , Metal additive manufacturing , Residual stress , Cracking index , Automatic differentiation , Attention-based Neural-Net Engineering , Engineering, general , Engineering Design , Mechanical Engineering , Computer-Aided Engineering (CAD, CAE) and Design , Electronics and Microelectronics, Instrumentation , Industrial Design
PATO: Producibility-Aware Topology Optimization Using Deep Learning for Metal Additive Manufacturing
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