In-loop additive manufacturing constraints for open-walled microstructures

2021 ◽  
pp. 102385
Author(s):  
Ryan Murphy ◽  
Robert Hewson ◽  
Matthew Santer
2017 ◽  
Vol 23 (2) ◽  
pp. 305-319 ◽  
Author(s):  
Recep M. Gorguluarslan ◽  
Umesh N. Gandhi ◽  
Yuyang Song ◽  
Seung-Kyum Choi

Purpose Methods to optimize lattice structure design, such as ground structure optimization, have been shown to be useful when generating efficient design concepts with complex truss-like cellular structures. Unfortunately, designs suggested by lattice structure optimization methods are often infeasible because the obtained cross-sectional parameter values cannot be fabricated by additive manufacturing (AM) processes, and it is often very difficult to transform a design proposal into one that can be additively designed. This paper aims to propose an improved, two-phase lattice structure optimization framework that considers manufacturing constraints for the AM process. Design/methodology/approach The proposed framework uses a conventional ground structure optimization method in the first phase. In the second phase, the results from the ground structure optimization are modified according to the pre-determined manufacturing constraints using a second optimization procedure. To decrease the computational cost of the optimization process, an efficient gradient-based optimization algorithm, namely, the method of feasible directions (MFDs), is integrated into this framework. The developed framework is applied to three different design examples. The efficacy of the framework is compared to that of existing lattice structure optimization methods. Findings The proposed optimization framework provided designs more efficiently and with better performance than the existing optimization methods. Practical implications The proposed framework can be used effectively for optimizing complex lattice-based structures. Originality/value An improved optimization framework that efficiently considers the AM constraints was reported for the design of lattice-based structures.


2020 ◽  
Vol 10 (3) ◽  
pp. 1100 ◽  
Author(s):  
Samyeon Kim ◽  
Seung Ki Moon

Parts with complex geometry have been divided into multiple parts due to manufacturing constraints of conventional manufacturing. However, since additive manufacturing (AM) is able to fabricate 3D objects in a layer-by-layer manner, design for AM has been researched to explore AM design benefits and alleviate manufacturing constraints of AM. To explore more AM design benefits, part consolidation has been researched for consolidating multiple parts into fewer number of parts at the manufacturing stage of product lifecycle. However, these studies have been less considered product recovery and maintenance at end-of-life stage. Consolidated parts for the manufacturing stage would not be beneficial at end-of-life stage and lead to unnecessary waste of materials during maintenance. Therefore, in this research, a design method is proposed to consolidate parts for considering maintenance and product recovery at the end-of-life stage by extending a modular identification method. Single part complexity index (SCCI) is introduced to measure part and interface complexities simultaneously. Parts with high SCCI values are grouped into modules that are candidates for part consolidation. Then the product disassembly complexity (PDC) can be used to measure disassembly complexity of a product before and after part consolidation. A case study is performed to demonstrate the usefulness of the proposed design method. The proposed method contributes to guiding how to consolidate parts for enhancing product recovery.


Author(s):  
Alok Sutradhar ◽  
Jaejong Park ◽  
Payam Haghighi ◽  
Jacob Kresslein ◽  
Duane Detwiler ◽  
...  

Topology optimization provides optimized solutions with complex geometries which are often not suitable for direct manufacturing without further steps or post-processing by the designer. There has been a recent progression towards linking topology optimization with additive manufacturing, which is less restrictive than traditional manufacturing methods, but the technology is still in its infancy being costly, time-consuming, and energy inefficient. For applications in automotive or aerospace industries, the traditional manufacturing processes are still preferred and utilized to a far greater extent. Adding manufacturing constraints within the topology optimization framework eliminates the additional design steps of interpreting the topology optimization result and converting it to viable manufacturable parts. Furthermore, unintended but inevitable deviations that occur during manual conversion from the topology optimized result can be avoided. In this paper, we review recent advances to integrate (traditional) manufacturing constraints in the topology optimization process. The focus is on the methods that can create manufacturable and well-defined geometries. The survey will discuss the advantages, limitations, and related challenges of manufacturability in topology optimization.


Author(s):  
Dylan Bender ◽  
Ahmad Barari

Abstract The traditional input to almost all commercially available Additive Manufacturing (AM) systems is in STL (Standard Tessellation Language) format, which represents a solid model by its tessellated surfaces. This does not allow transferring the entire information of a solid model to the additive manufacturing preprocessing system. However, in some recent applications such as additive manufacturing preprocessing simulation, closed-loop of topology optimization and additive manufacturing process planning, and AM-based design optimization the full access to the solid model information is necessary. Slicing of the finite element model directly is introduced in this paper. The presented approach enables access to the entire solid model information during the AM preprocessing tasks with a focus on coupling the topology optimization in the design process with the actual manufacturing constraints.


2018 ◽  
Vol 51 (11) ◽  
pp. 1359-1364 ◽  
Author(s):  
Davin Jankovics ◽  
Hossein Gohari ◽  
Mohsen Tayefeh ◽  
Ahmad Barari

Author(s):  
Yuqing Zhou ◽  
Kazuhiro Saitou

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.


2015 ◽  
Vol 137 (11) ◽  
Author(s):  
Nicholas Meisel ◽  
Christopher Williams

The PolyJet material jetting process is uniquely qualified to create complex, multimaterial structures. However, key manufacturing constraints need to be explored and understood in order to guide designers in their use of the PolyJet process including (1) minimum manufacturable feature size, (2) removal of support material, (3) survivability of small features, and (4) the self-supporting angle in the absence of support material. The authors use a design of experiments (DOE) approach to identify the statistical significance of geometric and process parameters and to quantify the relationship between these significant parameters and part manufacturability. The results from this study include the identification of key variables, relationships, and quantitative design thresholds necessary to establish a preliminary set of design for additive manufacturing (DfAM) guidelines for material jetting. Experimental design studies such as the one in this paper are crucial to provide designers with the knowledge to ensure that their proposed designs are manufacturable with the PolyJet process, whether designed manually or by an automated method, such as topology optimization (TO).


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