Minimum length-scale constraints for parameterized implicit function based topology optimization

Open access via Springer Compact Agreement The author would like to thank the Numerical Analysis Group at the Rutherford Appleton Laboratory for their FORTRAN HSL packages (HSL, a collection of Fortran codes for large-scale scientific computation. See http://www.hsl.rl.ac.uk/). The author also would like to acknowledge the support of the Maxwell compute cluster funded by the University of Aberdeen. Finally, the author thanks the anonymous reviewers for their helpful comments and suggestions that improved this paper.Peer reviewedPublisher PD

Multiscale structural optimisation with concurrent coupling between scales

A robust three-dimensional multiscale topology optimisation framework with concurrent coupling between scales is presented. Concurrent coupling ensures that only the microscale data required to evaluate the macroscale model during each iteration of optimisation is collected and results in considerable computational savings. This represents the principal novelty of the framework and permits a previously intractable number of design variables to be used in the parametrisation of the microscale geometry, which in turn enables accessibility to a greater range of mechanical point properties during optimisation. Additionally, the microscale data collected during optimisation is stored in a re-usable database, further reducing the computational expense of subsequent iterations or entirely new optimisation problems. Application of this methodology enables structures with precise functionally-graded mechanical properties over two-scales to be derived, which satisfy one or multiple functional objectives. For all applications of the framework presented within this thesis, only a small fraction of the microstructure database is required to derive the optimised multiscale solutions, which demonstrates a significant reduction in the computational expense of optimisation in comparison to contemporary sequential frameworks. The derivation and integration of novel additive manufacturing constraints for open-walled microstructures within the concurrently coupled multiscale topology optimisation framework is also presented. Problematic fabrication features are discouraged through the application of an augmented projection filter and two relaxed binary integral constraints, which prohibit the formation of unsupported members, isolated assemblies of overhanging members and slender members during optimisation. Through the application of these constraints, it is possible to derive self-supporting, hierarchical structures with varying topology, suitable for fabrication through additive manufacturing processes.Open Acces

Multiscale structural, thermal and thermo-structural optimization towards three-dimensional printable structures

This thesis develops a robust framework for the multiscale design of three-dimensional lattices with macroscopically tailored structural and thermal characteristics. The work exploits the high process flexibility and precision of additive manufacturing to the physical realization of complex microstructure of metamaterials by developing and implementing a multiscale approach. Structures derived from such metamaterials exhibit properties which differ from that of the constituent base material. Inspired by the concept of Free Material Optimization (FMO), a periodic microscale model is developed whose geometric parameterization enables smoothly changing properties and for which the connectivity of neighbouring microstructures in the large-scale domain is guaranteed by slowly changing large-scale descriptions of the lattice parameters. The microscale model is evaluated at full factorial design points to discretely populate material property spaces. A property point is fully defined for a micro-architecture when its elasticity matrix, thermal conductivity matrix and volume fraction is determined. The process of property-space population is facilitated by leveraging the existence of micro-architecture symmetries so that there exists a 95% reduction in the simulations required despite a full-factorial design of experiments. The discrete property evaluations are converted to continuous functions by response surface modelling so that the properties exist as continuous functions of the micro-architecture geometry parameters. A lattice-based functional grading of material is derived using the finite element method. The optimization is driven by a chain-rule combination of sensitivities derived by the adjoint method and sensitivities derived from explicit material property expressions. The novelty of the work lies in the use of multiple geometry-based small-scale design parameters for optimization problems in three-dimensional real space. The approach is demonstrated by solving structural, thermal and thermo-structural optimization problems. The results show designs with improved optimality compared to commonly implemented optimization methodologies. The optimal designs obtained are physically realizable by additive manufacturing techniques.Open Acces

Imposing minimum and maximum member size, minimum cavity size, and minimum separation distance between solid members in topology optimization

This paper focuses on density-based topology optimization and proposes a combined method to simultaneously impose Minimum length scale in the Solid phase (MinSolid), Minimum length scale in the Void phase (MinVoid) and Maximum length scale in the Solid phase (MaxSolid). MinSolid and MinVoid mean that the size of solid parts and cavities must be greater than the size of a prescribed circle or sphere. This is ensured through the robust design approach based on eroded, intermediate and dilated designs. MaxSolid seeks to restrict the formation of solid parts larger than a prescribed size, which is imposed through local volume restrictions. In the first part of this article, we show that by proportionally restricting the maximum size of the eroded, intermediate and dilated designs, it is possible to obtain optimized designs satisfying, simultaneously, MinSolid, MinVoid and MaxSolid. However, in spite of obtaining designs with crisp boundaries, some results can be difficult to manufacture due to the presence of multiple rounded cavities, which are introduced by the maximum size restriction with the sole purpose of avoiding thick solid members in the structure. To address this issue, in the second part of this article we propose a new geometric constraint that seeks to control the minimum separation distance between two solid members, also called the Minimum Gap (MinGap). Differently from MinVoid, MinGap introduces large void areas that do not necessarily have to be round. 2D and 3D test cases show that simultaneous control of MinSolid, MinVoid, MaxSolid and MinGap can be useful to improve the manufacturability of maximum size constrained designs

Process planning for robotic wire ARC additive manufacturing

Robotic Wire Arc Additive Manufacturing (WAAM) refers to a class of additive manufacturing processes that builds parts from 3D CAD models by joining materials layerupon- layer, as opposed to conventional subtractive manufacturing technologies. Over the past half century, a significant amount of work has been done to develop the capability to produce parts from weld deposits through the additive approach. However, a fully automated CAD-topart additive manufacturing (AM) system that incorporates an arc welding process has yet to be developed. The missing link is an automated process planning methodology that can generate robotic welding paths directly from CAD models based on various process models. The development of such a highly integrated process planning method for WAAM is the focus of this thesis

Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions

In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request

Coupled Experimentally-Driven Constraint Functions and Topology Optimization utilized in Design for Additive Manufacturing

Topology optimization (TO) is a structural optimization technique that searches for the proper material distribution inside a design space such that an objective function is maximized/ minimized. Rapid prototyping technologies such as additive manufacturing (AM) have allowed results from TO to be manufacturable. However, despite advancements in their ability to manufacture complex geometries, AM technologies still face certain constraints such as printing features at overhangs (unsupported features oriented at a certain angle from the axis normal to the build plate) and small feature sizes, amongst others. In the field of design for additive manufacturing (DfAM), it is common to only restrict one constraint to control the quality of the final parts. However, several studies have found that the final quality of a feature is heavily affected by at least two coupled constraints: the overhanging angle and the feature’s thickness. Modifying a structure’s layout while restricting only one constraint can uselessly increase the weight of a structure. To tackle this problem, the work done in this thesis considers the interplay between two geometrical constraints. The proposed research reviews some of the essential manufacturing constraints in topology optimization and emphasizes the need for coupling existing constraints. It first develops experiments to obtain a qualitative and a quantitative relationship between the design features’ surface qualities, orientation, and thickness. The relation between those parameters is used to update the layout of topologically optimized structures. The layout is changed by obtaining the medial axis of topologically optimized structures and then using implicit functions to conditionally thickening it. Throughout the analysis, it was observed that both the inclination and the thickness affect the surface quality. Furthermore, the effect of the parameters is more pronounced for low thicknesses and higher overhanging angles. The overhanging angle impacts the surface quality more than the thickness, which can be seen through ANOVA