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.

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.

Approximate Functionally Graded Materials for Multi-Material Additive Manufacturing

2018 ◽  
Author(s):  
Yuen-Shan Leung ◽  
Huachao Mao ◽  
Yong Chen
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Functionally Graded Materials ◽  
Functionally Graded ◽  
Gradual Transition ◽  
Material Distribution ◽  
Graded Materials ◽  
Base Materials ◽  
Multiple Materials ◽  
Am Processes

Functionally graded materials (FGM) possess superior properties of multiple materials due to the continuous transitions of these materials. Recent progresses in multi-material additive manufacturing (AM) processes enable the creation of arbitrary material composition, which significantly enlarges the manufacturing capability of FGMs. At the same time, the fabrication capability also introduces new challenges for the design of FGMs. A critical issue is to create the continuous material distribution under the fabrication constraints of multi-material AM processes. Using voxels to approximate gradient material distribution could be one plausible way for additive manufacturing. However, current FGM design methods are non-additive-manufacturing-oriented and unpredictable. For instance, some designs require a vast number of materials to achieve continuous transitions; however, the material choices that are available in a multi-material AM machine are rather limited. Other designs control the volume fraction of two materials to achieve gradual transition; however, such transition cannot be functionally guaranteed. To address these issues, we present a design and fabrication framework for FGMs that can efficiently and effectively generate printable and predictable FGM structures. We adopt a data-driven approach to approximate the behavior of FGM using two base materials. A digital material library is constructed with different combinations of the base materials, and their mechanical properties are extracted by Finite Element Analysis (FEA). The mechanical properties are then used for the conversion process between the FGM and the dual material structure such that similar behavior is guaranteed. An error diffusion algorithm is further developed to minimize the approximation error. Simulation results on four test cases show that our approach is robust and accurate, and the framework can successfully design and fabricate such FGM structures.

Functional Performance Tailoring of Additively Manufactured Components via Topology Optimization

2017 ◽  
Author(s):  
John C. Steuben ◽  
John G. Michopoulos ◽  
Athanasios P. Iliopoulos ◽  
Andrew J. Birnbaum
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
High Performance ◽  
Functional Performance ◽  
Underwater Vehicle ◽  
Length Scale ◽  
Hand Tool ◽  
Component Shape ◽  
Future Work ◽  
Am Processes

The freedom of design that is afforded by Additive Manufacturing (AM) processes opens exciting possibilities for the production of lightweight, high performance components and structures. Consequently, in recent years the development of software tools to enable engineering design methods that exploit the unique features of AM has become a subject of increased research interest. In this paper we explore the use of Topology Optimization (TO) algorithms to tailor component shape in order to achieve the intended functionality of additively manufactured components at the macro length scale. We present two case studies: the first concerns the hierarchical nesting of functions in a hand tool, while the second covers the development of a metamaterial component substructure for an Uninhabited Underwater Vehicle (UUV) hull. We offer conclusions regarding the usefulness of TO techniques for the design of AM components, and a summary of future work, which we feel is necessary to improve such methodologies.

Multi-Material Topology Optimization for Additive Manufacturing

2015 ◽  
Author(s):  
Amir M. Mirzendehdel ◽  
Krishnan Suresh
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Optimization Methods ◽  
Optimal Designs ◽  
Element Analysis ◽  
First Order ◽  
Multiple Materials ◽  
Material Fabrication ◽  
Single Material ◽  
Order Element

Additive manufacturing (AM) and topology optimization strongly complement each other in that the complex and optimal designs created through the latter can directly be fabricated through AM, leading to reduced design and fabrication time. As AM expands into multi-material fabrication, there is a natural need for efficient multi-material topology optimization methods, where one must simultaneously optimize the topology, and the distribution of various materials within the topology. In this paper we generalize the single-material Pareto tracing method of topology optimization to multiple materials, and discuss its implementation using assembly-free finite element analysis, and first-order element-sensitivity. The effectiveness of the algorithm is demonstrated through illustrative examples.

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 Additive Manufacturing and Topology Optimization of Magnetic Materials for Electrical Machines—A Review

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 283
Author(s):  
Thang Pham ◽  
Patrick Kwon ◽  
Shanelle Foster
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Optimization Techniques ◽  
Electrical Machines ◽  
Electrical Machine ◽  
Magnetic Components ◽  
And Topology ◽  
Multiple Materials ◽  
Manufacturing Technologies ◽  
Conventional Optimization

Additive manufacturing has many advantages over traditional manufacturing methods and has been increasingly used in medical, aerospace, and automotive applications. The flexibility of additive manufacturing technologies to fabricate complex geometries from copper, polymer, and ferrous materials presents unique opportunities for new design concepts and improved machine power density without significantly increasing production and prototyping cost. Topology optimization investigates the optimal distribution of single or multiple materials within a defined design space, and can lead to unique geometries not realizable with conventional optimization techniques. As an enabling technology, additive manufacturing provides an opportunity for machine designers to overcome the current manufacturing limitation that inhibit adoption of topology optimization. Successful integration of additive manufacturing and topology optimization for fabricating magnetic components for electrical machines can enable new tools for electrical machine designers. This article presents a comprehensive review of the latest achievements in the application of additive manufacturing, topology optimization, and their integration for electrical machines and their magnetic components.

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.

Integration of Design for Manufacturing Methods With Topology Optimization in Additive Manufacturing

2017 ◽  
Vol 139 (6) ◽  
Author(s):  
Rajit Ranjan ◽  
Rutuja Samant ◽  
Sam Anand
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Design Methodology ◽  
Relevant Literature ◽  
Design Guidelines ◽  
Layer By Layer ◽  
Material Deposition ◽  
First Case ◽  
Improvement Method ◽  
Am Processes

Additive manufacturing (AM) processes are used to fabricate complex geometries using a layer-by-layer material deposition technique. These processes are recognized for creating complex shapes which are difficult to manufacture otherwise and enable designers to be more creative with their designs. However, as AM is still in its developing stages, relevant literature with respect to design guidelines for AM is not readily available. This paper proposes a novel design methodology which can assist designers in creating parts that are friendly to additive manufacturing. The research includes formulation of design guidelines by studying the relationship between input part geometry and AM process parameters. Two cases are considered for application of the developed design guidelines. The first case presents a feature graph-based design improvement method in which a producibility index (PI) concept is introduced to compare AM friendly designs. This method is useful for performing manufacturing validation of pre-existing designs and modifying it for better manufacturability through AM processes. The second approach presents a topology optimization-based design methodology which can help designers in creating entirely new lightweight designs which can be manufactured using AM processes with ease. Application of both these methods is presented in the form of case studies depicting design evolution for increasing manufacturability and associated producibility index of the part.

Optimization and Simulation of Additive Manufacturing Processes

2020 ◽  
pp. 187-209
Author(s):  
Deepak Kumar Sahini ◽  
Joyjeet Ghose ◽  
Sanjay Kumar Jha ◽  
Ajit Behera ◽  
Animesh Mandal
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Sustainable Manufacturing ◽  
Simulation Models ◽  
Point Of View ◽  
Manufacturing Optimization ◽  
Current Trends ◽  
Optimization And Simulation ◽  
Metallic Parts ◽  
Am Processes

Additive manufacturing (AM) has developed and gained popularity across the globe into a multi-billion-dollar industry that involves many materials and techniques. AM has created itself as a technology for the manufacturing of metallic parts with enhanced mechanical characteristics that are scientifically sound and commercially feasible. However, there are various challenges, from business point of view, like high machine and material costs. Considering the complexities involved, sustainable manufacturing, optimization tools, and simulation models are necessary in order to save time and costly trial and errors. Topology optimization and simulation of AM processes are commercially available and are receiving attention from scientists and industry. Thus, this chapter is designed to provide readers with a brief introduction to AM technologies with typical applications. The main objective of this chapter is to provide the current trends and innovations in the field of design for additive manufacturing (DFAM), topology optimization, and simulation technologies.

Additive Manufacturing Constraints in Topology Optimization for Improved Manufacturability

2018 ◽  
Vol 140 (5) ◽  
Author(s):  
Kunal Mhapsekar ◽  
Matthew McConaha ◽  
Sam Anand
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Experimental Validation ◽  
Manufacturing Processes ◽  
Manufacturing Constraints ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
And Topology ◽  
Am Processes

Additive manufacturing (AM) provides tremendous advantage over conventional manufacturing processes in terms of creative freedom, and topology optimization (TO) can be deemed as a potential design approach to exploit this creative freedom. To integrate these technologies and to create topology optimized designs that can be easily manufactured using AM, manufacturing constraints need to be introduced within the TO process. In this research, two different approaches are proposed to integrate the constraints within the algorithm of density-based TO. Two AM constraints are developed to demonstrate these two approaches. These constraints address the minimization of number of thin features as well as minimization of volume of support structures in the optimized parts, which have been previously identified as potential concerns associated with AM processes such as powder bed fusion AM. Both the manufacturing constraints are validated with two case studies each, along with experimental validation. Another case study is presented, which shows the combined effect of the two constraints on the topology optimized part. Two metrics of manufacturability are also presented, which have been used to compare the design outputs of conventional and constrained TO.

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