Additive Manufacturing Constraints Research Articles

Optimizing additive manufacturing: Minimizing support structures through constraint-based design

Support structures are critical in Additive Manufacturing (AM) but pose challenges due to increased time and material costs for post-processing. This study introduces a novel constraint-based strategy to refine structural parameters while reducing reliance on support structures. By integrating this technique with topology optimization, component angulations are calibrated to align with AM constraints, enabling optimal designs without the need for support. Implementation of this constraint was done using MATLAB, transferring models to Cura software for support identification during printing. To validate the results, stress distribution was scrutinized in the ABAQUS environment, revealing a significant reduction in stress levels due to the proposed methodology, consistent with existing studies on stress reduction phenomena. Moreover, the study demonstrates a substantial decrease in material volume usage, leading to cost savings and promoting sustainable manufacturing practices. This reduction in material usage also enhances structural integrity, minimizing the risk of failure and improving overall performance.

Stress-driven generative design and numerical assessment of customized additive manufactured lattice structures

The rise of additive manufacturing (AM) has positioned lattice infilling as a pivotal strategy for creating lightweight, customized engineering components. This study presents a generative method that enables the conformal design and stiffness prediction of complex gradient strut-node lattice structures. A stress-driven Multi-Agent System (MAS) is introduced for the parametric optimization of lattice material distribution, incorporating geometric limitations, stress factors, and AM constraints. A beam element model simplifies the numerical analysis of the structure linear stiffness. By applying the Response Surface Method (RSM), a numerical model is established, not only conducting a quantitative analysis on the sensitivity of MAS design variables but predicting mechanical performance. This method is validated by designing a supporting component, demonstrating that the optimized lattice design can achieve a linear stiffness 1.4 times greater than that of conventional uniform lattice infills for the same mass. This research provides a comprehensive framework for the efficient design and analysis of irregular lattice structures at a macroscopic scale.

Multidisciplinary Design Methodology for Micro-Gas-Turbines—Part I: Reduced Order Component Design and Modeling

Abstract Ultramicrogas turbines (UMGTs) for electric power generation up to 1 kW are a viable replacement technology for lithium batteries in drones due to their high energy density. Previous research has shown that small-scale effects disqualify conceptual design practices applied to larger gas turbines owing to highly coupled, nonlinear component interactions. To fill this gap, we propose an interdisciplinary conceptual design and analysis framework based on reduced order models. To this end, the current work is divided into two parts covering component design and system integration, analysis, and optimization. In Part I, automated conceptual design of all engine subcomponents is elaborated facilitating interdependent reduced order models for compressor, turbine, combustor and high-speed generator while also considering additive manufacturing constraints. In a second step, the reduced order performance models are compared to computational fluid dynamics (CFD) Reynolds-averaged-Navier–Stokes (RANS) simulations of various turbomachinery geometries as well as experimental data of combustor and high-speed generator prototypes, showing good agreement and thus validating the component modules. In conclusion, the first part of this work elaborates an automated and efficient method to conceptual design of all components required for a functional UMGT. Since the strategy is applicable independent of component arrangement and engine layout, the proposed methods offer a universal framework for small gas turbine generators.

Ceramic and Metal Additive Manufacturing of Monolithic Rotors From SiAlON and Inconel and Comparison of Aerodynamic Performance for 300W Scale Microturbines

Abstract The gas turbine industry is continuously developing and testing new materials and manufacturing methods to improve the performance and durability of hot section components, which are subjected to extreme conditions. SiAlON and Inconel 718 are especially desirable for turbomachinery applications due to their high strength and high-temperature capabilities. To demonstrate the viability of additive manufacturing for small-scale turbomachinery for 300W scale microturbines, a monolithic rotor with a design speed of 450,000 RPM containing radial turbine and compressor was developed considering additive manufacturing constraints. The geometry was manufactured from SiAlON and Inconel 718 using lithographic ceramic manufacturing and selective laser melting, respectively. The additive manufacturing and thermal process parameters as well as material characterization are described in detail. Surface and computerized tomography scans were conducted for both rotors. While the metallic rotor showed undesirable printing artifacts and a large number of defects, the ceramic part achieved a level of relative precision and surface quality similar to large-scale production via casting. To compare turbomachinery performance, an aerodynamic test facility was developed allowing to measure pressure ratios and efficiency of small compressors. The rotors were tested in engine-realistic speeds, achieving a compressor rotor pressure ratio of 2.2. The ceramic part showed superior efficiency and pressure ratio compared to the Inconel rotor. This can be explained by lower profile and incidence losses due to a higher fidelity physical representation of the model geometry and better surface finish.

The Impact of Additive Manufacturing Constraints and Design Objectives on Structural Topology Optimization

To analyze the impact of different objective functions and additive manufacturing (AM) constraints on structural topology optimization, it is necessary to perform an in-depth comparative study. This analysis should consider specific structural design factors, such as compliance, volume, or stress minimization, and assess their effects on the topology optimization for AM. In addition, the inclusion of AM constraints can have a significant influence on various aspects, including optimal part geometry, part volume, support structure volume, and structural performance. Thus, it is essential to investigate and compare these factors to determine the optimal part design for AM. This study focuses on comparing topology optimization results obtained using compliance, stress, or multi-objective minimization, with and without AM constraints. A comparative analysis was conducted in the study, utilizing three structural design examples: cantilever beam, bridge-shaped structure, and L-shaped beam. The comparison results provide insights into the effects of build orientation, AM constraints such as overhang, and different design objectives on the structural topology optimization for AM.

An Evolutive-Deformation approach to enhance self-supporting areas in Additive Manufacturing designs

Additive Manufacturing (AM) technology has matured rapidly in recent years, opening an engineering design freedom front when combined with Topology Optimization (TO). Although there is no doubt about their potential to provide optimal parts, there still exists a wide gap between them which reduces the efficiency of the design process. Overhang constraints in TO are still limited, therefore the designs generated by these algorithms are not yet ready for being directly manufactured. They usually need support structures to avoid droops or warps, which is time-consuming, costly and can lead to the loss of the optimal path since designers are required to manually redesign the geometry. The proposed approach combines both design and AM constraints into an automatic workflow in which the geometry provided by the TO software is evolved to minimize support structures for any printing direction while preserving its structural performance. The NSGA-II algorithm is coupled with a Free-Form Deformation (FFD) method to develop different geometries, while fitness is evaluated with FEM analysis. The framework provides Pareto Fronts with the most promising printing directions and geometries in terms of support structures, stiffness and weight. This multi-objective optimization approach that aims to improve TO parts manufacturability is applied to two case studies and one real-world part, highlighting on average a reduction of support structures of around 30%, whereas mass is practically preserved and stiffness decreased by 5%.

Spatially optimised fibre-reinforced composites with isosurface-controlled additive manufacturing constraints

A design approach accounting for manufacturing constraints is described for spatially optimised fibre-reinforced composites. The approach is based on the optimisation of local fibre orientation, the fibre volume fraction and density-based topology optimisation to determine the optimal design. A continuity equation is adopted to constrain the fibre orientation and ensure continuous fibres within the bounds of realistic fibre volume fractions. This results in a fibre orientation with a corresponding and controllable variation of the fibre volume fraction. In order to ensure the continuous fibre can be deposited, the manufacturability of the optimised results is ensured by introducing constraints controlled with two scalar fields to reconstruct fibre paths which are able to provide sufficient information to generate printer toolpaths. A cantilever beam problem is solved to show the advantage of the fibre reinforcement, the inclusion of manufacturing constraints and the penalty in compliance due to the application of the manufacturing constraints. The results show that the presented approach successfully guarantees the manufacturability with minimal loss of performance.

Structural topology optimization with four additive manufacturing constraints by two-phase self-supporting design

Structural topology optimization with four additive manufacturing constraints by two-phase self-supporting design

G-Lattices: A Novel Lattice Structure and Its Generative Synthesis Under Additive Manufacturing Constraints

Abstract This paper introduces a novel strut-based lattice structure that is called G-Lattices and a method for their generative synthesis. Given additive manufacturing (AM), user and geometrical constraints, G-Lattices can automatically be generated via a particle tracing algorithm, which places/moves particles in a lattice unit cell. As a proof of concept, several G-Lattices are manufactured through an AM machine. Additionally, the proposed G-Lattice synthesis method is customized for the models under vertical loading, which are validated through finite element method experiments and have greater strength over volume ratios compared to conventional lattice structures.

Incorporating additive manufacturing constraints into magneto-structural topology optimization

Abstract The sequential using of topology optimization and AM (additive manufacturing) can generate and fabricate superior-performance yet lightweight magneto-structural components. In this paper, a magneto-structural topology optimization method is proposed considering AM constraints to improve the design manufacturability of optimized designs. The design problem is formulated with the weighted combination of magnetic compliance and mechanical compliance as a single objective subjected to two types of manufacturing constraints and the volume fraction constraint. Two important AM constraints are in a sequential manner incorporated into the topology optimization model, in which the overhang angle constraint is followed by the maximum length scale constraint. Corresponding sensitivities of the objective and constraints are derived, and the optimization problem is solved by the method of moving asymptotes. Two numerical examples and a practical conceptual design of linear motor rotor structure are systematically investigated to demonstrate the effectiveness of the proposed method.

Comparison of the design maps of TPMS based bone scaffolds using a computational modeling framework simultaneously considering various conditions.

In recent years, the triply periodic minimal surface (TPMS)-based scaffolds have been served as one of the crucial types of structures for biological replacements, the energy absorber, etc. Meanwhile, the development of additive manufacturing (AM) has facilitated the production of TPMS scaffolds with complex microstructures. However, the design maps of TPMS scaffolds, especially considering the AM constraints, remain unclear, which has hindered the design and application of TPMS scaffolds. The aims of the present study were to develop an efficient computational modeling framework for investigating the design maps of TPMS scaffolds simultaneously considering the AM constraints, the biological requirements, and the structural anisotropy. To demonstrate the computational framework, five widely-used topologies of the TPMS-based scaffolds (i.e. the Diamond, the Gyroid, the Fischer-Koch S, the F-RD, and the Schwarz P) were used, whose design maps for the surface-to-volume ratio and the effective elastic modulus were also investigated. The results showed that as the porosities increase, the design ranges of the surface-to-volume ratios decreases for all the structures. Compared with the effect of the constraint for the pore size, the minimal structural thickness for AM constraint has a greater effect on the surface-to-volume ratio. Regarding the elastic modulus, in the region of low porosity (approximately 0.5-0.7), the range for the effective elastic modulus of Schwarz P is the widest (approximately 2.24-32.6 GPa), but the Gyroid can achieve both high porosity and low effective elastic modulus (e.g. 0.61 GPa at the porosity of 0.90). These results and the method developed in the present study provided important basis and guidance for the design and application of the TPMS-based porous structures.

Topology Optimization Design With Addictive Manufacturing Constraints for Centrifugal Impeller

Abstract Weight reduction of centrifugal impeller is of great significance for improving thrust-weight ratio of aero-engine. In this paper, the weight reduction design for a centrifugal impeller considering additive manufacturing constraints was carry out. First of all, reducing the structure weight by using the topology optimization method based on variable density method. Secondly, reconstructing the results of topology optimization based on the overhang angle, minimum length, connectivity and other additive manufacturing constraints, whose structure contains the self-supporting cavity, oval powder discharge holes and annular convex plate. Finally, adjusting the reconstructed model by using size optimization, which obtained an innovative hollow centrifugal impeller with both lightweight and high mechanical performance. The results indicate that the mass of the optimized centrifugal impeller reduces by 18.5% compared with the original model at the same stress level. The method and scheme can provide reference for the structural optimization design of aero-engine centrifugal impeller.

Deep Learning for Additive Manufacturing-driven Topology Optimization

This paper investigates the potential of Deep Learning (DL) for data-driven topology optimization (TO). Unlike the rest of the literature that mainly applies DL to TO from a mechanical perspective, we developed an original approach to integrate mechanical and geometrical constraints simultaneously. Our approach takes as input the mechanical constraints (Boundary conditions, loads configuration, volume fraction) alongside the geometrical ones (total number of elements, minimum overhang, maximum length, minimum thickness) and generates a 2D design complying with these constraints. Thus, it combines the best of both mechanical (CAE) and geometrical design worlds. Conversely, geometrical design constraints are complex, not yet formalized, and contradictory between Additive Manufacturing (AM) processes, applications, and materials. Some are even descriptive, lacking a well-defined mathematical description, or are well-defined but proprietary and inaccessible. Hence, despite the synergy between AM and TO, integrating AM constraints into the TO formulation is still a hurdle. Furthermore, even when their integration is possible, TO’s convergence to a solution is compromised. On the other hand, DL has proven robust in capturing geometrical and spatial correlations. Consequently, our approach solves the previously listed setbacks by aligning DL to serve Design for AM (DfAM); there is no need to identify an analytical formula for a geometrical constraint but simply a sufficient number of examples describing it, and convergence is no longer a blockade when the DL model is trained on converged designs. Our approach tailors the design’s geometrical aspects with great flexibility and creativity. It reconciles design and manufacturing and accelerates the design life cycle of a part. Moreover, it can be easily updated to include additional constraints and can be implemented in the future into CAD software as a lighter and faster generative design module

In-loop additive manufacturing constraints for open-walled microstructures

The derivation and integration of novel in-loop additive manufacturing constraints for open-walled microstructures within a multiscale optimization framework is presented. Problematic fabrication features are discouraged in-loop 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 optimization. Through the application of these constraints, it is possible to derive self-supporting, hierarchical structures with varying topology, suitable for fabrication through AM processes. Two classical compliance minimization problems are solved using both the base framework with a prohibitive lower bound constraint applied on member radius to guarantee manufacturability and the base framework with integrated in-loop AM constraints. In both cases, the present work leads to a significant increase in the mechanical performance.

Generic roughness meta-model in 3D printing by Fused Deposition Modeling

Design for Additive Manufacturing (DfAM) aims at optimizing the product design based on design rules defined thanks to Additive Manufacturing (AM) constraints. To elaborate a functional design, several parameters like part orientation and layer thickness influence the results of mechanical strength, roughness, and manufacturing time. Surface roughness is an important input of Fused Deposition Modeling which impacts the product functionalities. To participate in product design optimization and DfAM development, this research suggests studying roughness models to clarify the best functional design methodology. This analysis permits to propose a meta-model that provides a better estimation for the surface roughness of the FDM products. A new roughness model is developed by a combination of genetic programming and symbolic regression due to the experimental data which helps the manufacturer to predict the surface quality of the products before fabrication. It enables us to estimate the surface roughness of all the AM products fabricated in a different value of layer thickness for all possible orientations in the space regarding part building direction.

Topology optimization subject to additive manufacturing constraints

In topology optimization the goal is to find the ideal material distribution in a domain subject to external forces. The structure is optimal if it has the highest possible stiffness. A volume constraint ensures filigree structures, which are regulated via a Ginzburg–Landau term. During 3D printing overhangs lead to instabilities. As a remedy an additive manufacturing constraint is added to the cost functional. First order optimality conditions are derived using a formal Lagrangian approach. With an Allen-Cahn interface propagation the optimization problem is solved iteratively. At a low computational cost the additive manufacturing constraint brings about support structures, which can be fine tuned according to demands and increase stability during the printing process.

Support point determination for support structure design in additive manufacturing

Support structures are necessary for many AM (additive manufacturing) processes to maintain the overhanging areas or resist deformation caused by thermal stress in printing. The design of support structures affects not only the printing quality but also material consumption and post-processing time. Current research had proposed many support structure designs and optimization methods to meet varying optimization requirements. However, no research has investigated yet how to determine the support points, or contact points for optimal support design. The number and position of support points will directly affect the support structure’s performance and volume, and therefore the final printing quality. To fill this gap, this paper proposes a determination method, which integrates overhang detection, analysis of periodic support point patterns and AM constraints to optimize support point distribution with ensured manufacturability. It is particularly critical for complex and porous structures in medical applications. The proposed method is illustrated and validated through a complex dental component. It can be used with support structure design methods to further improve the support structure performance and reduce support volume in printing, especially for metallic AM processes.

Integration of Design for Additive Manufacturing Constraints With Multimaterial Topology Optimization of Lattice Structures for Optimized Thermal and Mechanical Properties

Abstract The design of multimaterial lattice structures with optimized elasticity tensor, coefficient of thermal expansion (CTE), and thermal conductivity is the main objective of the research presented in this article. In addition, the additive manufacturability of the lattice structure is addressed using a prismatic density filter to eliminate support structures, and an octant symmetry filter is used to design symmetric lattices. A density-based topology optimization model is formulated with a homogenization method and solved using a sequential linear programming method to obtain the desired unit cell geometry of the lattice structure. The optimized unit cell obtained has high mechanical stiffness, a low CTE, and low thermal conductivity. A finite element analysis is carried out on the optimized lattice structure and an equivalent cube of computed effective properties (with the same loading and boundary conditions) to validate the computed homogenized material properties. The results from the finite element analysis show that the methodology followed to generate the lattice structure is accurate. Such lattice structures with tailored material properties can be used in aerospace parts that are subjected to mechanical and thermal loads. The complex multimaterial geometry produced from the topology optimization routine presented here is intended explicitly for the manufacture of parts using the directed energy deposition process with multiple material deposition nozzles.

Topology Optimization for additive manufacturing with distortion constraints

An important cause of failure in powder bed additive manufacturing is the distortion of the part due to thermal shrinkage during printing and the relaxation of residual stresses after its release from the base plate. In this paper, Additive Manufacturing simulations are coupled with Topology Optimization in order to generate designs that are not susceptible to failure associated with distortion. Two possible causes of failure are accounted for: recoater collision and global distortion of the product. Both are considered by simulation of the build process and defined as constraints in the context of a Solid Isotropic Material with Penalization method based topological optimization. The adjoint method is used to derive the sensitivities of the additive manufacturing constraints. The method is demonstrated with the 2D and 3D optimization of a bracket. Next to global topological changes, the obtained designs show features that are aimed at facilitating the printing process. These features resemble supports that are routinely applied to powder bed additive manufacturing. The formulated constraints were found to prevent excessive part distortion and associated build failures in all cases, against a modest increase in the compliance of the bracket.

Design and Development of a PCM-Based Two-Phase Heat Exchanger Manufactured Additively for Spacecraft Thermal Management Systems

For spacecraft thermal management systems, it is crucial to diminish the overall mass of on-board thermal storage system and minimize the temperature fluctuations when the environmental temperature changes drastically. Since there is no atmosphere in outer space, heat can only be rejected to space using radiation (e.g., radiators). The heat sink conditions, and the heating power subjected to be rejected vary continuously at the orbiting stage of the spacecraft. Without thermal storage capability, the radiator is required to be large enough to release the highest power at the hottest of the heat sink. By engaging and integrating phase-change materials (PCMs) into a passive two-phase heat exchanger, the radiator can be designed and sized for the average rather than the maximum power. To meet the NASA needs, the prototype developed and fabricated in the present study is a novel two-phase heat exchanger that integrates PCMs within a vapor chamber through multiple thin drawers loaded with bulk PCM. The entire solid structure of the prototype is manufactured additively using Maraging Steel with the Direct Laser Metal Sintering (DLMS) technique. The chosen working fluid is Methanol to undertake two-phase heat transfer via saturated vapor from the heated surface of evaporator to the condenser surface, and the condensate then returns back to the heated region and keeps recirculated using the capillary pumping pressure performed by the wick structure without any reliance on the gravitational magnitudes. Depending on the heat sink temperature and PCM melting temperature, the developed heat exchanger can either function as a thermal capacitor or a two-phase heat exchanger with thermal storage capability, that is, two modes of operation to be engaged within the spacecraft's launching/landing stages or orbiting stage, respectively.In the present study, the geometry and number of the PCM-loaded drawers enclosed inside the heat exchanger's casing are optimized based on the mass ratio (the ratio of PCM mass to the total heat exchanger mass), additive manufacturing (AM) constraints, and total thermal resistances imposed on the system for either of the operation modes. The AM printed prototype with its all components are also represented along with technical drawings and specifications. Furthermore, the support structure model generated for metal printing of the proposed heat exchanger will be exposed and discussed. The wick structure design procedure is also presented and its main performance characteristics (e.g., effective pore size, effective thermal conductivity, porosity, and permeability) are reported to analytically examine the capillary pumping limit for the proposed heat exchanger.