Design For Additive Manufacturing Research Articles

Die steel design for additive manufacturing

A novel printable die steel was computationally designed and experiemtnally validated for selective laser melting (SLM), utilizing the advantages of the rapid solidification processes. During gas atomization, nanoscale TiN particles are intended to be insitu precipitated at 1790°C, nucleating δ-ferritic grains. Additionally, the chemical composition is adjusted to stabilize a complete δ-ferritic solidification via Scheil modeling to enhance the printability of the die steel. This work further introduces the concept of the matrix die steels aiming to dissolve solidification and primary carbides during solutionizing at a targeted temperature of 1100°C. Thus, the C-content is reduced to 1.4 mol.-% (0.3 wt.-%) compared to the benchmark H13 die steel with which contains 1.85 mol.-% (0.4 wt.-%). Even though a lower C-content is used, optimizing M2C driving force during tempering enables the die steel to achieve a peak hardness of 536 HV is achieved. Lastly, a superior thermal conductivity of 40 W m-1 K-1 is predicted at 450°C for the BCC matrix of the printable matrix die steel. The material design is based on thermo-chemical models interfaced with thermodynamic calculations as implemented in the Calculated Phase Diagram (CALPHAD) method.

Novel sensorized additive manufacturing-based enlighted tooling concepts for aeronautical parts

This paper presents lightweight tooling concepts based on additive manufacturing, with the aim of developing advanced tooling systems as well as installing sensors for real-time monitoring and control during the anchoring and manufacturing of aeronautical parts. Leveraging additive manufacturing techniques in the production of tooling yields benefits in manufacturing flexibility and material usage. These concepts transform traditional tooling systems into active, intelligent tools, improving the manufacturing process and part quality. Integrated sensors measure variables such as displacement, humidity and temperature allowing data analysis and correlation with process quality variables such as accuracy errors, tolerances achieved and surface finish. In addition to sensor integration, additive manufacturing by directed energy arc and wire deposition (DED-arc) has been selected for part manufacturing. The research includes the mechanical characterisation of the material and the microstructure of the material once manufactured by DED-arc. Design for additive manufacturing" principles guide the design process to effectively exploit the capabilities of DED-arc. These turrets, equipped with sensors, allow real-time monitoring and control of turret deformation during clamping and manufacturing of aeronautical parts. As a first step, deformation monitoring is carried out within the defined tolerance of ± 0.15, which allows a control point to be established in the turret. Future analysis of the sensor data will allow correlations with process quality variables to be established. Remarkably, the optimised version of the turret after applying DED technology weighed only 2.2 kg, significantly lighter than the original 6 kg version. Additive manufacturing and the use of lightweight structures for fixture fabrication, followed by the addition of sensors, provide valuable information and control, improving process efficiency and part quality. This research contributes to the development of intelligent and efficient tool systems for aeronautical applications.

3D printing of sustainable wood-based bio-composites for high-end custom carton

This research delves into an upscale, personalised packaging paradigm employing 3D printing and a bio-composite derived from esteemed rosewood. The study incorporates a design for additive manufacturing, enhancing designers’ flexibility, and fostering innovation. To address global sustainability imperatives, including efficiency, energy conservation, a low carbon footprint and ecological compatibility, a premium and sustainable rosewood/polyethersulfone (PES) composite is developed. Laser sintering (LS), renowned for its processing flexibility, is applied for crafting intricate packages. A weight ratio of 1:4 of rosewood to PES is highly suitable for LS processing. The resultant custom carton, embodying natural rosewood aesthetics and fragrance, exhibits commendable mechanical robustness and precision. Tensile and bending strengths reach peaks of 4.880 MPa and 7.870 MPa, respectively, under a 9-W laser power. Scanning electron microscope (SEM) analysis confirms the favourable dispersion of rosewood fibres in LS specimens, providing insights into the microstructure of the composite powder.

DeltaFlex—An Additively Manufactured Delta Robot With Compliant Joints: Virtual Prototyping and Experimental Evaluation

Abstract The current study presents the development and validation of a compliant Delta robot with a monolithic structure, which has been fabricated using additive manufacturing (AM). The monolithic design and the use of AM accelerate the robot development cycle by enabling rapid prototyping and deployment while also facilitating experimentation with novel or different robot kinematics. The use of flexible joints for robots presents a challenge in achieving sufficient workspaces. However, parallel architectures are well suited for incorporating compliant joints, as they require lower ranges of motion for individual joints compared to serial architectures. Therefore, the Delta configuration has been chosen for this study. Multibody flexible dynamics (MfBD) simulations have been used as a means to guide design choices and simulate the structural behaviour of the robot. A design for additive manufacturing (DfAM) technique has been adopted to minimize the need for support structures and maximize mechanical strength. The quantitative evaluation of the Delta’s overall performance has been conducted in terms of stiffness and precision. The stiffness test aimed to gauge the robot’s ability to withstand applied loads, whereas the repeatability test assessed its precision and accuracy. This approach offers a promising path for robot design with significant potential for future advancements and practical applications while highlighting the trade-offs that designers should consider when adopting this methodology.

Designing for financial inclusion in developing countries: Digital financial service for low-income women in Ghana

Digital financial inclusion initiatives in developing countries have gained salience because of their potential to improve the socioeconomic condition of marginalized groups such as low-income women. However, persistent challenges remain in overcoming the digital divide in developing countries and enhancing access and participation of women in digital financial services. Despite the growing scholarly attention, little is known about how digital technologies might be designed to enable financial inclusion of women in developing countries. Using the technology affordances approach, we extend previous theorizing on inclusive information system, and introduce a relational approach to designing for inclusion. Specifically, we conduct a case study of a digital finance initiative in Ghana involving the design of an interactive voice response system (IVR) for low-income women where systemic barriers to technology adoption and use are pervasive. We show the significance of user feedback, environmental factors, and affordances for more inclusive information system design. We contribute a theoretically grounded framework that takes holistic account of the sociotechnical context of IS design for inclusion.

Design for additive manufacturing of topology-optimized structures based on deep learning and transfer learning

PurposeThis paper aims to improve the manufacturability of additive manufacturing (AM) for topology-optimized (TO) structures. Enhancement of manufacturability focuses on modifying geometric constraints and classifying the building orientation (BO) of AM parts to reduce stresses and support structures (SSs). To this end, artificial intelligence (AI) networks are being developed to automate design for additive manufacturing (DfAM).Design/methodology/approachThis study considers three geometric constraints for their correction by convolutional autoencoders (CAEs) and transfer learning (TL). Furthermore, BOs of AM parts are classified using generative adversarial (GAN) and classification networks to reduce the SS. To verify the results, finite element analysis (FEA) is performed to compare the stresses of modified components with the original ones. Moreover, one sample is produced by the laser-based powder bed fusion (LB-PBF) in the BO predicted by the AI to observe its SSs.FindingsCAE and TL resulted in promoting the manufacturability of TO components. FEA demonstrated that enhancing manufacturability leads to a 50% reduction in stresses. Additionally, training GAN and pre-training the ResNet-18 resulted in 80%, 95% and 96% accuracy for training, validation and testing. The production of a sample with LB-PBF demonstrated that the predicted BO by ResNet-18 does not require SSs.Originality/valueThis paper provides an automatic platform for DfAM of TO parts. Consequently, complex TO parts can be designed most feasibly and manufactured by AM technologies with minimal material usage, residual stresses and distortions.

Development and Evaluation of a Novel Method for Reinforcing Additively Manufactured Polymer Structures with Continuous Fiber Composites

Additively manufactured polymer structures often exhibit strong anisotropies due to their layered composition. Although existing methods in additive manufacturing (AM) for improving the mechanical properties are available, they usually do not eliminate the high degree of structural anisotropy. Existing methods for continuous fiber (cF) reinforcement in AM can significantly increase the mechanical properties in the strand direction, but often do not improve the interlaminar strength between the layers. In addition, it is mostly not possible to deposit cFs three-dimensionally and curved (variable–axial) and, thus, in a path that is suitable for the load case requirements. There is a need for AM methods and design approaches that enable cF reinforcements in a variable–axial way, independently of the AM mounting direction. Therefore, a novel two-stage method is proposed in which the process steps of AM and cF integration are decoupled from each other. This study presents the development and validation of the method. It was first investigated at the specimen level, where a significant improvement in the mechanical properties was achieved compared to unreinforced polymer structures. The Young’s modulus and tensile strength were increased by factors of 9.1 and 2.7, respectively. In addition, the design guidelines were derived based on sample structures, and the feasibility of the method was demonstrated on complex cantilevers.

Fully Printed Multilayer Ceramic Capacitors Based on High-k Perovskite Nanosheets.

Printing technology enables the integration of chemically exfoliated perovskite nanosheets into high-performance microcapacitors. Theoretically, the capacitance value can be further enhanced by designing and constructing multilayer structures without increasing the device size. Yet, issues such as interlayer penetration in multilayer heterojunctions constructed using inkjet printing technology further limit the realization of this potential. Herein, a series of multilayer configurations, including Ag/(Ca2NaNb4O13/Ag)n and graphene/(Ca2NaNb4O13/graphene)n (n = 1-3), are successfully inkjet-printed onto diverse rigid and flexible substrates through optimized ink formulations, inkjet printing parameters, thermal treatment conditions, and rational multilayer structural design using high-k perovskite nanosheets, graphene nanosheets and silver. The dielectric performance is optimized by fine-tuning the number of dielectric layers and modifying the electrode/dielectric interface. As a result, the graphene/(Ca2NaNb4O13/graphene)3 multilayer ceramic capacitors exhibit a remarkable capacitance density of 346±12nFcm-2 and a high dielectric constant of 193±18. Additionally, these devices demonstrate moderate insulation properties, flexibility, thermal stability, and chemical sensitivity. This work shed light on the potential of multilayer structural design in additive manufacturing of high-performance 2D material-based ceramic capacitors.

Advancements in Fused Deposition Modeling for Aerospace: Optimizing Lightweight and High- Strength Components

Fused Deposition Modeling (FDM) has emerged as a pivotal technology in aerospace manufacturing, enabling the creation of lightweight and high-strength components. Recent advancements in FDM materials, process optimization, and design methodologies have significantly enhanced its applicability in producing aerospace parts that meet stringent performance criteria. This paper reviews the latest developments in FDM technology, focusing on material innovations, structural optimization techniques, design for additive manufacturing and practical applications in the aerospace sector. Key advancements include the use of high-performance thermoplastics, carbon fiber composites, and hybrid materials, as well as improved printing techniques that reduce defects and enhance mechanical properties. The potential of FDM to revolutionize aerospace manufacturing through cost- effective and efficient production of complex geometries is explored, highlighting ongoing research and future directions in this dynamic field.

High‐throughput preparation for alloy composition design in additive manufacturing: A comprehensive review

AbstractAdditive Manufacturing (AM) is revolutionizing aerospace, transportation, and biomedical sectors with its potential to create complex geometries. However, the metallic materials currently used in AM are not intended for high‐energy beam processes, suggesting performance improvement. The development of materials for AM still faces challenge because of the inefficient trial‐and‐error conventional methods. This review examines the challenges and current state of materials including aluminum alloys, titanium alloys, superalloys, and high‐entropy alloys (HEA) in AM, and summarizes the high‐throughput methods in alloy development for AM. In addition, the advantages of high‐throughput preparation technology in improving the properties and optimizing the microstructure mechanism of major additive manufacturing alloys are described. This article concludes by emphasizing the importance of high‐throughput techniques in pushing the boundaries of AM materials development, pointing toward a future of more effective and innovative material solutions.

Making cyber-human systems smarter

The term smart is often used carelessly in relation to systems, devices, and other entities such as cities that capture or otherwise process or use information. This conceptual paper treats the idea of smartness in a way that suggests directions for making cyber-human systems smarter. Cyber-human systems can be viewed as work systems. This paper defines work system, cyber-human system, algorithmic agent, and smartness of systems and devices. It links those ideas to challenges that can be addressed by applying ideas that managers and IS designers discuss rarely, if at all, such as dimensions of smartness for devices and systems, facets of work, roles and responsibilities of algorithmic agents, different types of engagement and patterns of interaction between people and algorithmic agents, explicit use of various types of knowledge objects, and performance criteria that are often deemphasized. In combination, those ideas reveal many opportunities for IS analysis and design practice to make cyber-human systems smarter.

Adapted Design Process for Continuous Fiber-Reinforced Additive Manufacturing-A Methodological Framework.

Continuous fiber-reinforced material extrusion is an emerging additive manufacturing process that builds components layer by layer by extruding a continuous fiber-reinforced thermoplastic strand. This novel manufacturing process combines the benefits of additive manufacturing with the mechanical properties and lightweight potential of composite materials, making it a promising approach for creating high-strength end products. The field of design for additive manufacturing has developed to provide suitable methods and tools for such emerging processes. However, continuous fiber-reinforced material extrusion, as a relatively new technology, has not been extensively explored in this context. Designing components for this process requires considering both restrictive and opportunistic aspects, such as extreme anisotropy and opportunities for functional integration. Existing process models and methods do not adequately address these specific needs. To bridge this gap, a tailored methodology for designing continuous fiber-reinforced material extrusion is proposed, building on established process models. This includes developing process-specific methods and integrating them into the process model, such as a process selection analysis to assess the suitability of the method and a decision model for selecting the process for highly stressed components. Additionally, a detailed design process tailored to continuous fiber-reinforced material extrusion is presented. The application of the developed process model is demonstrated through a case study.

Study on Topology Optimization Design for Additive Manufacturing

Study on Topology Optimization Design for Additive Manufacturing

Topology Optimization of Structural Drive-Train Component of an Electric Driven Vehicle for Additive Manufacturing

Additive Manufacturing (AM) is an emerging technology and an important alternative to conventional manufacturing methods as it enables the production of lighter parts that are potentially more durable. In this context, the design for additive manufacturing (DFAM) has been drawing a considerable amount of attention mainly in the aerospace, and automotive industries as well as in academia. On the other hand, the ability of additive manufacturing to manufacture complex topology is often the outcome of topology optimization, which makes topology optimization a good design tool for additive manufacturing. The main objective of the present work is to redesign a structural component of the drivetrain of the Shell Eco-Marathon vehicle, with the use of Altair Inspire™, an industrial generative design tool, by application of Topology Optimization for Additive Manufacturing aiming mass reduction and does not cover the print process.

Investigating and Characterizing the Systemic Variability When Using Generative Design for Additive Manufacturing

Investigating and Characterizing the Systemic Variability When Using Generative Design for Additive Manufacturing

A novel design model of flow channel paths for additive manufacturing

PurposeThis paper aims to propose a method for automatic design of additive manufacturing (AM) flow channel paths driven by path length and pressure loss. The research focuses on the automatic design of channel paths, intending to achieve the shortest flow channel length or minimum pressure loss and improve the design efficiency of AM parts.Design/methodology/approachThe initial layout of the flow channels is redesigned to consider the channels print supports. Boundary conditions and constraints are defined according to the redesigned channels layout, and the equation consisting of channel length and pressure loss is used as the objective function. Then the path planning simulation is performed based on particle swarm algorithm. The proposed method describes the path of flow channels using spline cures. The spline curve is controlled by particle (one particle represents a path), and the particle is randomly generated within the design space. After the path planning simulation is completed, the generated paths are used to create 3D parts.FindingsCase study 1 demonstrates the automatic design of hydraulic spool valve. Compared to conventional spool valve, the pressure loss was reduced by 86% and the mass was reduced by 83%. The design results of case study 2 indicate that this approach is able to find the shortest channel path with lower computational cost.Originality/valueThe automatic design method of flow channel paths driven by path length and pressure loss presented in this paper provides a novel solution for the creation of AM flow components.

An innovative approach to a UAV tails structural design for additive manufacturing

AbstractThe innovative approach used for the design and fabrication of a UAV tails through additive manufacturing (AM) completely changes the concept of designing and manufacturing products through conventional methods. AM based processes can reduces costs, lead times, and increases design freedom, allowing parts to be tailored to specific needs. A key benefit of AM processes is the ability to produce optimized designs with reduced mass and without compromising structural effectiveness. This is possible thanks to the combination of high‐performance AM materials and the extraordinary manufacturing capability of AM technologies combined with an appropriate design‐for‐additive manufacturing (DfAM) approach, overcoming traditional manufacturing techniques. This article presents the development process of a UAV tails, based on a Design for Additive Manufacturing (DfAM) approach in order to reduce structural mass and guarantee safe operation under service loads. Therefore, by replacing the metallic component of the UAV with a techno‐polymeric one, several configurations have been developed. The optimal additive configuration achieved a weight reduction of 60% compared to a metal configuration of equivalent volume, without loss of mechanical properties. In conclusion, a feasibility study of the proposed configuration was carried out by 3D printing the vertical tail redesigned.Highlights An innovative approach by combining DfAM processes with FFF technology. Metal replacement of UAV tails with advanced technopolymers. Efficient Weight Reduction about 60% compared to metal structure. Single‐piece design simplifies assembly and manufacturing. UAV redesigned tails with enhanced efficiency, adaptability, and feasibility.

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.

Providing a knowledge-based design catalog as an approach to support the development of design for additive manufacturing skills

AbstractProficiency in design for additive manufacturing (DfAM) requires training and a lot of trial and error. To support the development of DfAM skills, we redesigned 47 design artifacts from case studies and derived tacit knowledge from successful and unsuccessful redesigns. All knowledge about these artifacts was then collected in a design catalog. In a workshop with a total of 48 graduates and students, 45 participants deemed the design catalog supportive. After evaluating their designs, we concluded that the use of a knowledge-based design catalog can develop and improve individual DfAM skills.