Design For Additive Manufacturing Research Articles

AI-aided design and multi-scale optimization of mechanical metastructures with controllable anisotropy

Anisotropic mechanical metamaterials with controllable properties are crucial for additive manufacturing design. However, manually regulating microstructural anisotropy remains challenging. This study introduces a method for artificial intelligence-aided design (AIAD) and mechanical metastructures optimization (MMO) to achieve extensive multi-scale structural enhancements. The approach involves compiling a comprehensive database of lattice materials with anisotropic characteristics. This is achieved by manipulating the central node position and rod diameter of a cubic-BCC microstructure. Homogenization theory then determines the elastic tensor of each microstructure. A 3D convolutional neural network (3D-CNN) maps the relationship between geometric properties and mechanical performance. An inverse design model based on a backpropagation neural network (NN) and parametric design acquires microstructures with desired elastic tensor attributes. Finally, a novel optimization approach for large-scale multiscale structures applies this method to control structural anisotropy. The resulting material distribution resembles a truss, significantly improving structural performance.

Impact behavior of periodic, stochastic, and anisotropic minimal surface-lattice sandwich structures

Recent advancements in 3D printing technologies have made it possible to fabricate intricate lattice architectures with high precision. These lattices can now be utilized to design lightweight sandwich structures that serve multiple functions. To enhance the impact loading performance of these structures, it is crucial to understand how the lattice's topological properties, particularly those with minimal surface attributes like periodic or stochastic Primitive and Gyroid triply periodic minimal surfaces (TPMS) and spinodal-like stochastic cellular materials, associate with the mechanical properties of sandwich structures while keeping the skin thickness fixed. Thus, this paper explores the low-velocity impact behavior of various sheet/shell-based minimal surface-latticed cores of sandwich structures with woven composite skins. The elasto-plastic-damage numerical simulations consider lattice core periodicity, randomness, and anisotropy while keeping the relative density constant. Core lattice randomness and anisotropy are designed using the Gaussian Random Field (GRF) method for spinodal-based stochastic cellular materials and stochastic TPMS. The simulation results showed that the periodic Primitive-lattice core exhibits high out-of-plane shearing strength, enabling the sandwich structure to demonstrate the highest perforation limit. GRF spinodal-based core achieved the highest peak load due to its anisotropic mechanical properties. However, the post-yielding bending of the lattice sheet limited its ability to resist perforation, and absorb and dissipated energy. Interestingly, the stochastic Gyroid TPMS topology, with its inherent densely-distributed microstructure, showed high sensitivity to loading rate, resulting in enhanced energy absorption and dissipation of the sandwich structure. These findings offer valuable insights for optimizing multifunctional sandwich structures with superior impact performance and their design for additive manufacturing.

A Mixed-Methods Investigation of How Digital Immersion Affects Design for Additive Manufacturing Evaluations

Abstract Applications for additive manufacturing (AM) continue to increase as more industries adopt the technology within their product development processes. There is a growing demand for designers to acquire and hone their design for AM (DfAM) intuition and generate innovative solutions with AM. Resources that promote DfAM intuition, however, historically default to physical or digitally non-immersive modalities. Immersive virtual reality (VR) naturally supports 3D spatial perception and reasoning, suggesting its intuitive role in evaluating geometrically complex designs and fostering DfAM intuition. However, the effects of immersion on DfAM evaluations are not well-established in the literature. This study contributes to this gap in the literature by examining DfAM evaluations for a variety of designs across modalities using varying degrees of immersion. Specifically, it observes the effects on the outcomes of the DfAM evaluation, the effort required of evaluators, and their engagement with the designs. Findings indicate that the outcomes from DfAM evaluations in immersive and non-immersive modalities are similar without statistically observable differences in the cognitive load experienced during the evaluations. Active engagement with the designs, however, is observed to be significantly different between immersive and non-immersive modalities. By contrast, passive engagement remains similar across the modalities. These findings have interesting implications on how organizations train designers in DfAM, as well as on the role of immersive modalities in design processes. Organizations can provide DfAM resources across different levels of immersion, enabling designers to customize how they acquire DfAM intuition and solve complex engineering problems.

Tailoring surface roughness through the temporal variation of additive manufacturing process parameters

The benefits that additive manufacturing (AM) offers to the industry are generally well understood and appreciated. However, the current design for additive manufacturing (DfAM) methodologies and computer-aided manufacturing (CAM) packages neglect to exploit the full potential that AM can offer through its unique ability to vary material characteristics whilst the final component geometry is being formed. The purpose of this research is to demonstrate that additional design control can be gained through temporal DfAM (TDfAM). In this study, the ability to tailor the surface roughness of fused deposition modelling (FDM) AM polylactic acid (PLA) parts through the variation of two process parameters, nozzle temperature and print speed, is explored. The underpinning hypothesis is that variation of temperature and printing speed, can provide a significant change of surface roughness within one homogeneous part. This research demonstrated that nozzle temperature and print speed have a statistically significant effect on the surface roughness of the top and side surfaces. By increasing temperature and speed, the roughness of the side surfaces decreased and the roughness of the top surface increased. Furthermore, the in-silico implementation of TDfAM is demonstrated. As such, the research supports the hypothesis that TDfAM can enable additional control over the surface characteristics of a homogeneous part.

Design Optimization of Lattice Structures Under Impact Loading for Additive Manufacturing

Abstract Additive manufacturing (AM) has enabled the production of intricate lattice structures with excellent performance and minimal mass. Design approaches that consider static loading, including lattice-based topology optimization (TO), have been well-researched recently. However, to date, there appears to be no widely accepted method of optimizing lattice structures for high-strain rate loading, especially when the design for additive manufacturing (DFAM) principles are considered. This study proposes a computational framework for the design of lattice structures under specified impact loading. To manage dimensionality while achieving sufficient generality, a heuristic design space is developed that relies on traditional TO to govern the design's macrostructure and standard dimensioning to govern its mesostructure. DFAM principles are then incorporated into a Bayesian optimization scheme wrapped around traditional TO to achieve manufacturable designs that absorb high-impact loading. Because this approach does not require analytical gradient information, the framework can be used to optimize directly on complex objectives, such as injury metrics calculated from the acceleration curve. A series of case studies is formulated around a mass-performance tradeoff and involves individual unit cell design as well as full-part design. The proposed design parameterization is found to enable sufficient flexibility to achieve consistently good performance regardless of AM build orientation.

Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing.

In recent years, there has been widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in various additive manufacturing (AM) techniques. These ML models excel at recognizing complex patterns from extensive, well-curated datasets, thereby unveiling latent knowledge crucial for informed decision-making during the AM process. The collaborative synergy between ML and AM holds the potential to revolutionize the design and production of AM-printed parts. This review delves into the challenges and opportunities emerging at the intersection of these two dynamic fields. It provides a comprehensive analysis of the publication landscape for ML-related research in the field of AM, explores common ML applications in AM research (such as quality control, process optimization, design optimization, microstructure analysis, and material formulation), and concludes by presenting an outlook that underscores the utilization of advanced ML models, the development of emerging sensors, and ML applications in emerging AM-related fields. Notably, ML has garnered increased attention in AM due to its superior performance across various AM-related applications. It is envisioned that the integration of ML into AM processes will significantly enhance 3D printing capabilities across diverse AM-related research areas.

Non-Conventional Wing Structure Design with Lattice Infilled through Design for Additive Manufacturing.

Lightweight structures with a high stiffness-to-weight ratio always play a significant role in weight reduction in the aerospace sector. The exploration of non-conventional structures for aerospace applications has been a point of interest over the past few decades. The adaptation of lattice structure and additive manufacturing in the design can lead to improvement in mechanical properties and significant weight reduction. The practicality of the non-conventional wing structure with lattices infilled as a replacement for the conventional spar-ribs wing is determined through finite element analysis. The optimal lattice-infilled wing structures are obtained via an automated iterative method using the commercial implicit modeling tool nTop and an ANSYS workbench. Among five different types of optimized lattice-infilled structures, the Kelvin lattice structure is considered the best choice for current applications, with comparatively minimal wing-tip deflection, weight, and stress. Furthermore, the stress distribution dependency on the lattice-unit cell type and arrangement is also established. Conclusively, the lattice-infilled structures have shown an alternative innovative design approach for lightweight wing structures.

Solution strategy for sustainable additive manufacturing design problem using Pythagorean fuzzy MCGDM methodology

AbstractTo solve difficulties involving various groups’ decision-making problems, this work has been proposed to develop a logical aggregation approach to aggregate decision-makers’ crisp data into Pythagorean fuzzy numbers. By combining the established strategy with the Pythagorean fuzzy TOPSIS method, a hybrid Pythagorean fuzzy multiple criteria group decision-making methodology is presented. Based on fuzzy rules inference and the Takagi–Sugeno technique, a novel function is created to represent the degrees of uncertainty in decision-makers’ data. As an example, the material selection process in practical additive manufacturing designs is provided to show how the proposed methodology may be applied to actual applications. Sensitivity analysis is used to evaluate the effectiveness of the suggested methodology. The outcomes demonstrate that the plan was successful in producing a PFN that accurately reflects the decision-maker’s knowledge.

Design for Additive Manufacturing (DFAM) applied in the manufacture of Master Sample for the automotive industry

In order to supply auto parts to the automotive industry the companies must meet all Advanced Product Quality Planning (APQP) requirements. One of the biggest difficulties in obtaining the approval of the APQP is in the preparation of the “Master Sample,” which will be used for the validation of jigs, production, and dimensional control devices. The procedures for manufacturing Master Samples through conventional manufacturing processes are outdated, slow, and cost-effective, which goes against the concepts of Industry 4.0. Therefore, this work aims to propose a procedure for Design for Additive Manufacturing (DFAM) that analyzes the feasibility and systematizes the manufacture of Master Sample through Additive Manufacturing (AM). Two model parts were submitted to the procedure, manufactured by AM and validated as Master Samples. In this work, a comparative analysis between the parts produced conventionally and those produced by AM showed that the time and costs in order to obtain the Master Samples using the proposed procedure was significantly shorter. The reduction in time to obtain Master Samples speeds up the evaluation and validation of control devices from suppliers, can speed up the acquisition of APQP documentation and reduce the time in the development of the serial parts by manufacturing process. Furthermore, the use of the proposed DFAM procedure is innovative in the context of the automotive industry, as it suggests a change in the production concept and inserts AM as another option in the manufacturing process and not just as a rapid prototyping tool.

Effects of Fe Contents on the Microstructure and Precipitate of Ti–Al–V Alloys Prepared by Direct Energy Deposition

This study investigated the influence of Fe content on the microstructure and mechanical properties of Ti–6Al–4V(TC4) + 25Ti alloys prepared by low-energy-density direct energy deposition (DED) technology. With the incorporation of the Fe elements, the α-Ti phases exhibited significant changes in size and morphology, while the numerous β-Ti phases and some triclinic-Ti precipitates were retained. With the refinement of the α-Ti phase, retainment of the β-Ti phase and the presence of triclinic-Ti precipitates, the mechanical properties of DED samples can be significantly improved compared with DED TC4 alloys. The room-temperature mechanical property tests showed that the ultimate tensile strength (UTS) of 3Fe + TC4 + 25Ti achieved 1298.64 ± 5.26 MPa with an elongation of 4.82% ± 0.20%, and the maximum elongation of 1Fe + TC4 + 25Ti reached 10.82% ± 0.82% with a UTS of 1076.95 ± 11.69 MPa. The strengthening mechanism of DED Ti-Al-V-Fe alloys were further discussed, providing new insights into the microstructure control and the composition design of additive manufacturing of Ti alloys.

Biodigital stool series: Nature-based growing morphologies through additive manufacturing and generative design

Biodigital Architecture and Design emerges in the intersection between nature-based design and digital technologies. This article presents the results of a project that aimed to integrate three-dimensional growth patterns from nature into a stool design series through Computational Design and Additive Manufacturing methods. The project methodology includes (1) Pattern from nature selection, (2) Generative design, (3) 3D printing, including scale models and prototypes, (4) Compression tests, and (5) Generative optimization. Findings indicate that the branching pattern was the lightest-weight pattern while showing the highest specific resistance compared to the other models evaluated. Branching pattern also took the least amount of time and material to print, these findings contribute to the decision-making process for future work. Regarding computational design method, it resulted in several design alternatives, with complex, unpredictable and efficient mechanical behavior geometries. Future work would include to a variety of patterns from nature, generative design, optimizations, and prototypes.

Critical Control Points and Case Studies in the Production of Metal Additive Manufacturing Medical Device

The critical control points in the production quality management of metal additive manufacturing medical devices, including personnel, hardware, design and development, procurement control, production management and quality control are summarized from the perspective of supervision. The typical cases are analyzed to provide reference for medical device regulatory authorities and enterprises.

Powder bed fusion integrated product and process design for additive manufacturing: a systematic approach driven by simulation

Powder bed fusion integrated product and process design for additive manufacturing: a systematic approach driven by simulation

A comparative analysis between backpropagation and general regression neural networks to predict the tensile properties of fused filament fabricated parts

The Fused Filament Fabrication (FFF) process comprises a large number of process parameters that affect the resultant mechanical properties of the parts, generating uncertainties during the Design for Additive Manufacturing (DfAM) practice. Several studies have used Artificial Neural Networks (ANN) based on classification machine learning techniques such as Backpropagation Neural Network (BPNN), to evaluate the dimensional accuracy, surface roughness, compressive, flexural and tensile strength of FFF parts. As an alternative, in this paper a new General Regression Neural Network (GRNN) approach, based on a regression machine learning technique, is proposed to estimate the tensile structural properties of polylactic acid (PLA)-FFF parts using variable process parameters. The performance of the new proposed GRNN is compared with the performance of a BPNN. The comparison and evaluation are based on their capability to accurately predict the experimental Ultimate Tensile Stress (UTS) and the Elastic Modulus ( E) of FFF parts. The outcomes of this evaluation have shown that although the GRNN and the BPNN are able to estimate with high accuracy the structural behaviour of FFF parts, the performance of the proposed GRNN is superior (0.001 Mean Square Error, MSE) than the BPNN (0.0031 MSE). Moreover, the new proposed GRNN fits the experimental test values with a minimal average error of 0.62%. Thus, the proposed GRNN can be used during the design of components intended to be manufacture by FFF.

Microstructural Control of a Multi‐Phase PH Steel Printed with Laser Powder Bed Fusion

AbstractThe established approach to materials design for additive manufacturing (AM) consists of attempting to reproduce the uniform structures and properties of conventionally processed materials. While this certainly helped facilitate material certification and the rapid introduction of AM technologies in several industries, the opportunity to exploit unique features of specific AM processes to generate spatially varying microstructures–and hence novel materials, remains largely untapped. This work presents a method for manufacturing materials through laser powder bed fusion (LPBF), in which control over the spatial variation in phase composition and mechanical properties is achieved. This technique is demonstrated using 17‐4 precipitation‐hardened stainless steel (17‐4PH), by controlling spatial modulation of energy densities during printing. This results in local control of ferrite/martensite volume fractions, allowing the fabrication of metal/metal architected composites with hard/brittle regions interspersed with soft/tough regions. Local variations of ~20% in tensile strength and ~150% in elongation are achieved, with a spatial resolution of ~100 microns. The approach is general and robust, fully compatible with commercially available LPBF equipment, and applicable to virtually any multi‐phase alloy system. This work shifts the paradigm from attempting to print components with uniform properties to manufacturing alloys with controlled spatial property gradients.

A novel laser cladding AlMgZnCuErZr alloy: Material genetic design, strengthening andtoughening mechanisms

Aluminum alloy with high strength, toughness, and corrosion resistance is one of the new materials for laser additive manufacturing of lightweight high-speed train brake discs. However, due to the non-equilibrium solidification characteristics during the laser melting process, the commercial Al alloys with high performance and laser formability face several bottlenecks in alloy composition design and preparation. This paper employs a material genetic design method to develop a novel laser cladding Al14Mg9Zn6Cu2Mn0.5Si0.6Er0.4Zr alloy, which exhibits excellent laser formability and better strength, toughness, and corrosion resistance under an optimized laser energy density of 1157 J/mm3. Specifically, an in-situ generated T-AlMgZnCuZrEr genetic phase with internal cell-like α-Al was first discovered, which significantly improves the properties of the alloy. The alloy sample demonstrates a remarkable compressive strength of 663 MPa along with a deformation of 15 %. The compressive strength and deformation product of the laser cladding sample reaches 9.9 GPa%, which is 13.44 % higher than that of the high-strength AlZnMgCu alloy prepared by other subtractive manufacturing processes. The mechanisms of matching strength-toughness and corrosion resistance of the alloy have been elucidated. This study provides a feasible material design and preparation pathway for laser additive manufacturing of high-performance and lightweight Al alloy high-speed train brake discs.

The Current Design for Additive Manufacturing Research Frontier

Design for additive manufacturing (DFAM) seeks to develop designs that take advantage of the unique capabilities of additive manufacturing (AM) processes to maximize benefits. In this paper, several issues at the frontier of DFAM research are highlighted. First, the need is described to include as-manufactured mechanical and other physical properties, such as topology and shape optimization and generative design, during computational design. AM processes rarely produce parts with homogeneous composition and isotropic properties, so design methods and tools should not assume them. Second, the topic of designing for the AM process chain is important since AM-fabricated parts typically need postprocessing operations, such as support removal, finish machining, heat treatments, etc. DFAM methods need to incorporate the entire process chain, not just the AM process, which is explored with an example from metal powder bed fusion. Third, potentially, the largest benefit of AM can be realized by radically rethinking product architectures. Examples of an electric motorcycle and an assistive exosuit illustrate the ideas and potential benefits. Finally, some thoughts on design for 4D printing are offered, specifically for 3D printed morphing and deployable systems that utilize the shape memory effect.

An Ontology-based Knowledge Modeling towards Eco-Design for Additive Manufacturing

Owing to the unique technological merits of additive manufacturing (AM), design for AM, particularly eco-design for AM, has been gaining attention in improving the sustainable performance of AM-fabricated (AMed) parts. The main challenge in harvesting potential sustainability benefits is how to capture the impact of design on sustainable performance. Though several existing studies adopted quantitative evaluation on AM sustainability and investigated the relationships between design information and sustainability information of AMed part, the sustainability information is still unavailable in the design phase. This hardly guides eco-design for AM in practice. To support the increasing industrial applications of AM, this paper illustrates related knowledge, that is, material-process-structure-ecoProperty (MPSeP) relationships to connect design and sustainability information in eco-design for AM. An ontology-based method is proposed to model the MPSeP relationships of the AMed part in a structured way. An example of an AMed hydraulic valve body was used to demonstrate the MPSeP modeling process. It is discussed that how the constructed model effectively assists AM designers through providing eco-design recommendation, and enables design software tools to perform sustainability analysis towards eco-design for AM.

Exploring the impact of design tool usage on design for additive manufacturing processes and outcomes

Abstract Improving designers’ ability to identify manufacturing constraints during design can help reduce the time and cost involved in the development of new products. Different design for additive manufacturing (DfAM) tools exist, but the design outcomes produced using such tools are often evaluated without comparison to existing tools. This study addresses the research gap by directly comparing design performance using two design support tools: a worksheet listing DfAM principles and a manufacturability analysis software tool that analyzes compliance with the same principles. In a randomized-controlled study, 49 nonexpert designers completed a design task to improve the manufacturability of a 3D-printed part using either the software tool or the worksheet tool. In this study, design outcome data (creativity and manufacturability) and design process data (task load and time taken) were measured. We identified statistically significant differences in the number of manufacturability violations in the software and worksheet groups and the creativity of the designs with novel build orientations. Results demonstrated limitations associated with lists of principles and highlighted the potential of software in promoting creativity by encouraging the exploration of alternative build orientations. This study provides support for using software to help designers, particularly nonexpert designers who rely on trial and error during design, evaluate the manufacturability of their designs more effectively, thereby promoting concurrent engineering design practices.

DETAILED DESIGN AND ANALYSIS FOR ADDITIVE MANUFACTURING OF TOPOLOGICALLY OPTIMIZED AND GENERATIVELY DESIGNED TI-6AL-4V HIP JOINT IMPLANT

In this paper, a comprehensive investigation of the design and analysis of Ti-6Al-4V hip joint implants using generative design and topology optimization, along with laser powder bed fusion (LPBF), an additive manufacturing technique, has been presented. The study employed the NSGA-II genetic algorithm for generative design, enabling the generation of diverse optimized designs and topology optimization with the solid isotropic material penalization approach, efficiently reducing implant mass of the design space by up to 75% while maintaining structural integrity. Finite element analysis revealed comparable von Mises stress and deformation levels between geometries obtained with generative design and topology optimization. However, the combined approach exhibited superior performance, namely, topology optimization followed by generative design, with a 40% reduction in deformation and a 15% reduction in von Mises stress compared to conventional models. LPBF simulations demonstrated the superiority of the optimized geometries, with a 30% reduction in thermal stress and a 66% reduction in deformation compared to conventional designs. It is observed that design input for generative design significantly impacts the output design. Also, geometry has a notable impact on the quality of the printed part.