Simultaneous Topology Optimization Using Adaptive Weighting Factor for Additive Manufacturing of Statically Loaded Structures

2021 ◽  
Vol 45 (9) ◽  
pp. 715-726
Author(s):  
Jae Hun Jo ◽  
Joong Seok Lee
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Weighting Factor ◽  
Adaptive Weighting

scholarly journals A Shape Optimization Method for Part Design Derived from the Buildability Restrictions of the Directed Energy Deposition Additive Manufacturing Process

Designs ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 19
Author(s):  
Andreas K. Lianos ◽  
Harry Bikas ◽  
Panagiotis Stavropoulos
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Energy Deposition ◽  
Design Method ◽  
Current Trend ◽  
Internal Stresses ◽  
Material Distribution ◽  
Load Case ◽  
Design Elements ◽  
Industrial Sectors

The design methodologies and part shape algorithms for additive manufacturing (AM) are rapidly growing fields, proven to be of critical importance for the uptake of additive manufacturing of parts with enhanced performance in all major industrial sectors. The current trend for part design is a computationally driven approach where the parts are algorithmically morphed to meet the functional requirements with optimized performance in terms of material distribution. However, the manufacturability restrictions of AM processes are not considered at the primary design phases but at a later post-morphed stage of the part’s design. This paper proposes an AM design method to ensure: (1) optimized material distribution based on the load case and (2) the part’s manufacturability. The buildability restrictions from the direct energy deposition (DED) AM technology were used as input to the AM shaping algorithm to grant high AM manufacturability. The first step of this work was to define the term of AM manufacturability, its effect on AM production, and to propose a framework to estimate the quantified value of AM manufacturability for the given part design. Moreover, an AM design method is proposed, based on the developed internal stresses of the build volume for the load case. Stress tensors are used for the determination of the build orientation and as input for the part morphing. A top-down mesoscale geometric optimization is used to realize the AM part design. The DED Design for Additive Manufacturing (DfAM) rules are used to delimitate the morphing of the part, representing at the same time the freeform mindset of the AM technology. The morphed shape of the part is optimized in terms of topology and AM manufacturability. The topology optimization and AM manufacturability indicator (TMI) is introduced to screen the percentage of design elements that serve topology optimization and the ones that serve AM manufacturability. In the end, a case study for proof of concept is realized.

scholarly journals Topology Optimization for additive manufacturing with distortion constraints

2021 ◽  
Vol 386 ◽  
pp. 114095
Author(s):  
Grzegorz Misiun ◽  
Emiel van de Ven ◽  
Matthijs Langelaar ◽  
Hubert Geijselaers ◽  
Fred van Keulen ◽  
...  
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing

Innovative Design, Structural Optimization and Additive Manufacturing of New-Generation Turbine Blades

2021 ◽  
pp. 1-31
Author(s):  
Lorenzo Pinelli ◽  
Andrea Amedei ◽  
Enrico Meli ◽  
Federico Vanti ◽  
Benedetta Romani ◽  
...  
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Structural Optimization ◽  
Turbine Blade ◽  
Turbine Blades ◽  
Topological Optimization ◽  
Mode Shapes ◽  
Structural Topology Optimization ◽  
Structural Topology ◽  
New Generation

Abstract The need for high performances is pushing the complexity of mechanical design at very high levels, especially for turbomachinery components. Structural topology optimization methods together with additive manufacturing techniques for high resistant alloys are considered very promising tools, but their potentialities have not been deeply investigated yet for critical rotating components like new-generation turbine blades. This research work proposes a methodology for the design, the optimization and the additive manufacturing of extremely stressed turbomachinery components like turbine blade-rows. The presented procedure pays particular attention to important aspects of the problems as fluid-structure interactions and fatigue of materials, going beyond the standard structural optimization approaches found in the literature. The numerical procedure shows robustness and efficiency, making the proposed methodology a good tool for rapid design and prototyping, and for reducing the design costs and the time-to-market typical of these mechanical elements. The procedure has been applied to a low-pressure turbine rotor to improve the aeromechanical behavior while keeping the aerodynamic performance. From the original geometry, mode-shapes, forcing functions and aerodynamic damping have been numerically evaluated and are used as input data for the following topological optimization. Finally, the optimized geometry has been verified in order to confirm the improved aeromechanical design. After the structural topology optimization, the final geometries provided by the procedure have been then properly rendered to make them suitable for additive manufacturing. Some prototypes of the new optimized turbine blade have been manufactured to be tested in terms of fatigue.

scholarly journals Data-Driven Additive Manufacturing Constraints for Topology Optimization

2018 ◽  
Author(s):  
Benjamin M. Weiss ◽  
Joshua M. Hamel ◽  
Mark A. Ganter ◽  
Duane W. Storti
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Optimal Designs ◽  
Data Driven ◽  
Constraint Function ◽  
Manufacturing Constraint ◽  
Gradient Based ◽  
Fixed Radius ◽  
Different Shapes ◽  
Moving Morphable Components

The topology optimization (TO) of structures to be produced using additive manufacturing (AM) is explored using a data-driven constraint function that predicts the minimum producible size of small features in different shapes and orientations. This shape- and orientation-dependent manufacturing constraint, derived from experimental data, is implemented within a TO framework using a modified version of the Moving Morphable Components (MMC) approach. Because the analytic constraint function is fully differentiable, gradient-based optimization can be used. The MMC approach is extended in this work to include a “bootstrapping” step, which provides initial component layouts to the MMC algorithm based on intermediate Solid Isotropic Material with Penalization (SIMP) topology optimization results. This “bootstrapping” approach improves convergence compared to reference MMC implementations. Results from two compliance design optimization example problems demonstrate the successful integration of the manufacturability constraint in the MMC approach, and the optimal designs produced show minor changes in topology and shape compared to designs produced using fixed-radius filters in the traditional SIMP approach. The use of this data-driven manufacturability constraint makes it possible to take better advantage of the achievable complexity in additive manufacturing processes, while resulting in typical penalties to the design objective function of around only 2% when compared to the unconstrained case.

Sign in / Sign up

Export Citation Format

Share Document