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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.15525 |
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| _version_ | 1866910801908989952 |
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| author | Wang, Jingyi Papadopoulos, Panayiotis |
| author_facet | Wang, Jingyi Papadopoulos, Panayiotis |
| contents | A design optimization framework for process parameters of additive manufacturing based on finite element simulation is proposed. The finite element method uses a coupled thermomechanical model developed for fused deposition modeling from the authors' previous work. Both gradient-based and gradient-free optimization methods are proposed. The gradient-based approach, which solves a PDE-constrained optimization problem, requires sensitivities computed from the fully discretized finite element model. We show the derivation of the sensitivities and apply them in a projected gradient descent algorithm. For the gradient-free approach, we propose two distinct algorithms: a local search algorithm called the method of local variations and a Bayesian optimization algorithm using Gaussian processes. To illustrate the effectiveness and differences of the methods, we provide two-dimensional design optimization examples using all three proposed algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_15525 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Optimization of process parameters in additive manufacturing based on the finite element method Wang, Jingyi Papadopoulos, Panayiotis Numerical Analysis A design optimization framework for process parameters of additive manufacturing based on finite element simulation is proposed. The finite element method uses a coupled thermomechanical model developed for fused deposition modeling from the authors' previous work. Both gradient-based and gradient-free optimization methods are proposed. The gradient-based approach, which solves a PDE-constrained optimization problem, requires sensitivities computed from the fully discretized finite element model. We show the derivation of the sensitivities and apply them in a projected gradient descent algorithm. For the gradient-free approach, we propose two distinct algorithms: a local search algorithm called the method of local variations and a Bayesian optimization algorithm using Gaussian processes. To illustrate the effectiveness and differences of the methods, we provide two-dimensional design optimization examples using all three proposed algorithms. |
| title | Optimization of process parameters in additive manufacturing based on the finite element method |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2310.15525 |