Multi-Component Topology Optimization for Powder Bed Additive Manufacturing (MTO-A)

2018 ◽  
Author(s):  
Yuqing Zhou ◽  
Tsuyoshi Nomura ◽  
Kazuhiro Saitou
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Simultaneous Optimization ◽  
Previous Attempt ◽  
Numerical Instability ◽  
Feature Size ◽  
Powder Bed ◽  
Nonlinear Projection ◽  
Gradient Based ◽  
Length Width

This paper presents a gradient-based multi-component topology optimization (MTO) method for structures assembled from components made by powder bed additive manufacturing. It is built upon our previous work on the continuously-relaxed MTO framework utilizing the concept of fractional component membership. The previous attempt on the integration of the relaxed MTO framework with additive manufacturing constraints, however, suffered from numerical instability for larger size problems, limiting its application to 2D low-resolution examples. To overcome this difficulty, this paper proposes an improved MTO formulation based on a design field regularization and a nonlinear projection of component membership variables, with a focus on powder bed additive manufacturing. For each component, constraints on the maximum allowable build volume (i.e., length, width, and height), the elimination of enclosed voids, and the minimum printable feature size are imposed during the simultaneous optimization of the overall base topology and component partitioning. The scalability of the new MTO formulation is demonstrated by a few 2D examples with much higher resolution than previously reported, and the first reported 3D example of MTO.

Multicomponent Topology Optimization for Additive Manufacturing With Build Volume and Cavity Free Constraints

2019 ◽  
Vol 19 (2) ◽  
Author(s):  
Yuqing Zhou ◽  
Tsuyoshi Nomura ◽  
Kazuhiro Saitou
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Optimization Method ◽  
Simultaneous Optimization ◽  
Powder Bed ◽  
Optimization Framework ◽  
Minimum Compliance ◽  
Single Piece ◽  
Gradient Based ◽  
Length Width

Topology optimization for additive manufacturing has been limited to the design of single-piece components that fit within the printer's build volume. This paper presents a gradient-based multicomponent topology optimization method for structures assembled from components built by powder bed additive manufacturing (MTO-A), which enables the design of multipiece assemblies larger than the printer's build volume. Constraints on component geometry for powder bed additive manufacturing are incorporated in a density-based topology optimization framework, with an additional design field governing the component partitioning. For each component, constraints on the maximum allowable build volume (i.e., length, width, and height) and the elimination of enclosed cavities are imposed during the simultaneous optimization of the overall topology and component partitioning. Numerical results of the minimum compliance designs revealed that manufacturing constraints, previously applied to single-piece topology optimization, can unlock richer design exploration space when applied to multicomponent designs.

Gradient-Based Multi-Component Topology Optimization for Additive Manufacturing (MTO-A)

2017 ◽  
Author(s):  
Yuqing Zhou ◽  
Kazuhiro Saitou
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Simultaneous Optimization ◽  
Minimum Length ◽  
Manufacturing Constraints ◽  
Component Level ◽  
Component Size ◽  
Design Variables ◽  
Gradient Based ◽  
Minimum Length Scale

Topology optimization for additive manufacturing has been limited to the component-level designs with the component size smaller than the printer’s build volume. To enable the design of structures larger than the printer’s build volume, this paper presents a gradient-based multi-component topology optimization framework for structures assembled from components built by additive manufacturing. Constraints on component geometry for additive manufacturing are incorporated in the density-based topology optimization, with additional design variables specifying fractional component membership. For each component, constraints on build size, enclosed voids, overhangs, and the minimum length scale are imposed during the simultaneous optimization of overall base topology and component partitioning. The preliminary result on a minimum compliance structure shows promising advantages over the conventional monolithic topology optimization. Manufacturing constraints previously applied to monolithic topology optimization gain new interpretations when applied to multi-component assemblies, which can unlock richer design space for topology exploration.

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.

Design and Modeling of a Microscale Selective Laser Sintering System

2016 ◽  
Author(s):  
Nilabh Roy ◽  
Anil Yuksel ◽  
Michael Cullinan
Keyword(s):  
Heat Transfer ◽  
Additive Manufacturing ◽  
Selective Laser Sintering ◽  
Laser Sintering ◽  
Feature Size ◽  
Powder Bed ◽  
Metal Additive Manufacturing ◽  
Wave Nature ◽  
Field Radiation ◽  
Metal Additive

The development of micro and nanoscale additive manufacturing methods in metals and ceramics is important for many applications in the aerospace, medical device, and electronics industries. Unfortunately, most commercially available metal additive manufacturing tools have feature-size resolutions of greater than 100 μm, which is too large to precisely control the microstructure of the parts they produce. A few research-grade metal additive manufacturing tools do exist, but their build rate is generally too slow for commercial applications. Therefore, this paper presents a new microscale selective laser sintering (μ-SLS) that can be used to improve the minimum feature-size resolution of metal additively manufactured parts by up to two orders of magnitude, while still maintaining the throughput of traditional additive manufacturing processes. In order to achieve this goal, several innovative design features like the use of (1) ultra-fast lasers, (2) a micro-mirror based optical system, (3) nanoscale powders, and (4) a precision spreader mechanism, have been implemented. The micro-SLS system is capable of achieving build rates of approximately 1 cm3/hr while achieving a feature-size resolution of approximately 1 μm. This paper will also present new molecular scale models that have been developed for the micro-SLS to quantify and certify the micro-SLS build process. Modeling of the micro-SLS process is challenging, because most macroscale models of the SLS process contain assumptions that are no longer valid when the size of the particles that are being sintered is smaller than the wavelength of the laser being used to sinter them. Therefore, in modeling the micro-SLS process we must account for the wave nature of light and can no longer rely on the ray tracing models commonly used to model the SLS process. Also, heat transfer in the micro-SLS process is dominated by near-field radiation due to the diffraction of the light off the nanoparticles in the powder bed and the ultrafast lasers that are used in the micro-SLS system. This means that the assumptions of heat transfer by conduction and far-field radiation in the macroscale SLS systems are no longer valid for the micro-SLS system. Finally, the agglomeration of nanoparticles in the powder bed must be accurately modeled in order to precisely predict the formation of defects in the final parts produced. Overall, the goal of this modeling effort is to be able to predict the quality of a part produced using any given processing conditions, in order to produce parts that are “born certified” and do not need to be tested post fabrication.

scholarly journals Data-Driven Additive Manufacturing Constraints for Topology Optimization

2020 ◽  
Vol 143 (2) ◽  
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

Abstract 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 with 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 with the unconstrained case.

Efficient Multi-Material Topology Optimization

2021 ◽  
pp. 275-292
Author(s):  
Amir M. Mirzendehdel ◽  
Krishnan Suresh
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Consumer Products ◽  
Simultaneous Optimization ◽  
Multiple Materials ◽  
Am Processes

This chapter focuses on generating optimized topologies using multiple materials. The interest in multi-material topology optimization (MMTO) stems from the well-recognized synergy between topology optimization (TO) and additive manufacturing (AM), where organic structures created through TO can be directly fabricated by a variety of AM processes. Given the rapidly increasing capabilities of AM, there is an opportunity to improve the performance of consumer products, biomedical, and aerospace components, through simultaneous optimization of topology and distribution of multiple materials.

Topology Optimization for Additively Manufactured Self-Supporting Axisymmetric Structures

2021 ◽  
Author(s):  
Hak Yong Lee ◽  
Julia D. W. Carroll ◽  
James K. Guest
Keyword(s):  
Topology Optimization ◽  
Pressure Vessels ◽  
Structural Performance ◽  
Support Structures ◽  
Feature Size ◽  
Internal Support ◽  
Gradient Based ◽  
Axisymmetric Structures ◽  
Constraint Approach ◽  
Moving Asymptotes

Abstract This paper discusses the design of axisymmetric structures with self-supporting features that can be additively manufactured without requiring internal support structures. This is motivated by wire-fed additive manufacturing processes, many of which can fabricate designs with enclosed pores that inherently exist in many axisymmetric structures, such as double walled pressure vessels. Although enclosed pores are possible, features that rise at shallow angles from the build plate typically cannot be fabricated without the use of support structures, which require removal and thus are unfavorable in such applications. In this paper, an overhang constraint is applied to ensure that all designed features rise at a designer-prescribed self-supporting angle to eliminate the need for such support structures. This is achieved by coupling the projection-based overhang constraint approach with topology optimization and axisymmetric finite elements whose stiffness is interpolated using Solid Isotropic Material with Penalization (SIMP). Gradients are computed with the adjoint method and the Method of Moving Asymptotes (MMA) is employed as the gradient-based optimizer. Two numerical examples related to a canonical pressure vessel and an optical mirror support structure are used to demonstrate the approach. Solutions are shown to satisfy minimum feature size requirements and designer-prescribed (process dependent) overhang constraint angles, while providing clear and crisp representations of topology. As observed in past works on overhang constraints, a clear trade-off is illustrated between the magnitude of the overhang constraint angle and the structural performance (mass or stiffness), with more strict requirements producing designs with lower performance.

Designing for Additive Manufacturing: Lightweighting Through Topology Optimization Enables Lunar Spacecraft

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Melissa E. Orme ◽  
Michael Gschweitl ◽  
Michael Ferrari ◽  
Ivan Madera ◽  
Franck Mouriaux
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Preliminary Design ◽  
Powder Bed ◽  
Lunar Landing ◽  
Rigorous Testing ◽  
Numerical Effort ◽  
Engine Mount ◽  
Development Approach

An end-to-end development approach for space flight qualified additive manufacturing (AM) components is presented and demonstrated with a case study consisting of a system of five large, light-weight, topologically optimized components that serve as an engine mount in SpaceIL's GLPX lunar landing craft that will participate in the Google Lunar XPrize challenge. The development approach includes a preliminary design exploration intended to save numerical effort in order to allow efficient adoption of topology optimization and additive manufacturing in industry. The approach also addresses additive manufacturing constraints, which are not included in the topology optimization algorithm, such as build orientation, overhangs, and the minimization of support structures in the design phase. Additive manufacturing is carried out on the topologically optimized designs with powder bed laser technology and rigorous testing, verification, and validation exercises complete the development process.

scholarly journals Topology Optimization for Additive Manufacturing as an Enabler for Light Weight Flight Hardware

Designs ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 51 ◽  
Author(s):  
Melissa Orme ◽  
Ivan Madera ◽  
Michael Gschweitl ◽  
Michael Ferrari
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Case Studies ◽  
Space Mission ◽  
Element Analysis ◽  
Powder Bed ◽  
Satellite Technology ◽  
Process Testing ◽  
Qualification Testing ◽  
Flight Hardware

Three case studies utilizing topology optimization and Additive Manufacturing for the development of space flight hardware are described. The Additive Manufacturing (AM) modality that was used in this work is powder bed laser based fusion. The case studies correspond to the redesign and manufacture of two heritage parts for a Surrey Satellite Technology LTD (SSTL) Technology Demonstrator Space Mission that are currently functioning in orbit (case studies 1 and 2), and a system of five components for the SpaceIL’s lunar launch vehicle planned for launch in the near future (case study 3). In each case, the nominal or heritage part has undergone topology optimization, incorporating the AM manufacturing constraints that include: minimization of support structures, ability to remove unsintered powder, and minimization of heat transfer jumps that will cause artifact warpage. To this end the topology optimization exercise must be coupled to the Additive Manufacturing build direction, and steps are incorporated to integrate the AM constraints. After design verification by successfully passing a Finite Element Analysis routine, the components have been fabricated and the AM artifacts and in-process testing coupons have undergone verification and qualification testing in order to deliver structural components that are suitable for their respective missions.

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