Generative Design of a Gas-Cooled Micro Reactor Based on Multi-Physics Analysis

Research in additive manufacturing (AM) has increased the use of AM in many industries, resulting in a commensurate need for a workforce skilled in AM. In order to meet this need, educational institutions have undertaken different initiatives to integrate design for additive manufacturing (DfAM) into the engineering curriculum. However, limited research has explored the impact of these educational interventions in bringing about changes in the technical goodness of students' design outcomes, particularly through the integration of DfAM concepts in an engineering classroom environment. This study explores this gap using an experimental study with 193 participants recruited from a junior-level course on mechanical engineering design. The participants were split into three educational intervention groups: (1) no DfAM, (2) restrictive DfAM, and (3) restrictive and opportunistic (dual) DfAM. The effects of the educational intervention on the participants' use of DfAM were measured through changes in (1) participants' DfAM self-efficacy, (2) technical goodness of their AM design outcomes, and (3) participants' use of DfAM-related concepts when describing and evaluating their AM designs. The results showed that while all three educational interventions result in similar changes in the participants' opportunistic DfAM self-efficacy, participants who receive only restrictive DfAM inputs show the greatest increase in their restrictive DfAM self-efficacy. Further, we see that despite these differences, all three groups show a similar decrease in the technical goodness of their AM designs, after attending the lectures. A content analysis of the participants' design descriptions and evaluations revealed a simplification of their design geometries, which provides a possible explanation for the decrease in their technical goodness, despite the encouragement to utilize the design freedom of AM to improve functionality or optimize the weight of the structure. These results emphasize the need for more in-depth DfAM education to encourage the use of both opportunistic and restrictive DfAM during student design challenges. The results also highlight the possible influence of how the design problem is stated on the use of DfAM in solving it.

The unique tool-less and layer-upon-layer capabilities of additive manufacturing (AM) have largely lessened manufacturing constraints and significantly broadened design freedom. AM offers new opportunities for developing customized products by achieving significant improvements in product performances and lower overall costs. The AM-enabled customization opportunities inevitably challenge traditionally design theories and methodologies (DTMs) that lack design knowledge for AM products. Here, to counteract this limitation, this study presents a novel methodology for constructing knowledge supporting DTMs for AM-enabled customization: customized design for AM (CDfAM). The CDfAM supports two main phases of design for additive manufacturing (DfAM): (1) opportunistic DfAM in a conceptual design phase and (2) restrictive DfAM in an embodiment design phase. In terms of the knowledge construction for the opportunistic DfAM, it is essential to construct knowledge that supports to explore a customized design space enabled by AM’s design freedom from the conceptual design phase. To address the challenge, this study provides a novel method for capturing knowledge of customized AM product behaviors. First, this study proposes an affordance-based DfAM framework by integrating the concept of affordance into three link chain models. Second, an affordance structure is proposed to develop a formal method for representing the knowledge of the customized AM product behaviors utilizing finite state automata. In terms of the knowledge construction for the restrictive DfAM, this study presents a formal method for representing and reasoning customizable modular knowledge of design rules for AM. First, a novel structured modular hierarchy and associated formalisms of AM design rule elements are presented. Second, machine learning is employed to automatically learn design rules from AM data, which leverages the proposed design rule formalisms. A knowledge graph is designed and built to demonstrate the proposed methodology. Case studies with AM products such as a transtibial prosthesis leg and AM data such as ex-situ AM measurement data provide illustrations in this study. The case studies show that the proposed methodology explicitly constructs structured and correlated AM design knowledge of customized AM product behaviors, product geometries, and additive manufacturability constraints. It is also shown that qualified design knowledge in the knowledge graph enhances automation of DfAM decision-making and data-driven knowledge constructions. The proposed CDfAM methodology enables incorporating both customized AM product functionalities and additive manufacturability constraints into product designs. This study provides a foundation for AM knowledge construction and data- and knowledge-driven automation of DfAM decision-making. Validated design rules and representations are expected to contribute to developing international standards on AM design rules.

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.

Additive manufacturing (AM) and generative design tools are gaining popularity in reverse engineering and design optimization. Many industries, including aerospace and automotive, seek to reduce the mass of critical components while maintaining desired strength. The benefits of more efficient structural components include manufacturing cost savings across industries, increased fuel economy in transportation, and greater payload capacity in aerospace applications. This study utilized techniques such as 3D scanning, Fusion-based generative design simulations, and 3D printing to create a more efficient and equally strong component using advanced computer and manufacturing technologies. This study focused on an engine mount bracket for a Honda comfortable runabout vehicle (CRV), part number 11910-PPA-000, selected for generative design simulations and 3D printing as its dimensions were optimal for a commercially available desktop 3D printer and defined features ideal for 3D scanning and material testing. Once scanned, the stereolithography (STL) file was processed using Autodesk ReCap Photo to convert the triangular STL facets to quadrilateral facets before conversion to a meshed object in Fusion for further processing in the Design and Generative Design workspaces. The bolt and tapped holes were then isolated from the body of the mount bracket using the Fusion body splitting tool before post-processing to remove scanning defects from the inside face of the fixture points. The mounting bracket was then processed in the Fusion Generative Design workspace using acrylonitrile-butadiene-styrene (ABS) as the assigned material. The entire part was designated as the starting shape, with each bolt hole designated as preserved geometry, and established obstacle geometry to set the boundaries for the generative design. After assigning the part geometry, structural constraints and loads were applied to be consistent with the expected fixture points during testing, and the manufacturing type was designated for additive manufacturing. A model was then selected from 10 different generative design studies based on the mass saved and maximum Von Mises stresses. The chosen outcome had a mass reduction of 34%, from an original 131 grams to 86 grams when designed from ABS plastic. The maximum von Mises stress for the generative design was shown to be 9.8 MPa with a minimum factor of safety of 2. Three parts were printed out of ABS using 0°, 90°, and 45°/−45° raster orientations from the generative design. In addition, the legacy mount bracket was printed out of ABS using a 45°/−45° raster orientation from the generative design. The legacy part was tested on an Instron universal testing system as a benchmark for future testing of the generative design parts. The comparative study of a 3D scanned legacy part with generative design models demonstrated the feasibility of reverse engineering parts. This study also enabled advanced simulation tools to reduce the processing chain compared to traditional manufacturing and increase efficiency for critical parts across multiple industries.

This paper investigates additive manufacturing (AM) via material extrusion (MEX) with high-performance polymers (HPP) as a prototyping and final application tool for aerospace and, specifically, space applications. AM of HPP offers low-cost, lightweight solutions for low-load, zero-g applications, rapidly enabling advanced geometries without additional expense. This paper explores new materials and design solutions for space by: 1) Investigate manufacturing time and cost with identical CubeSat structures fabricated from aluminum, Nylon with carbon fiber (PA6-CF), and PEEK with carbon fiber (PEEK-CF) with a comparative case study. 2) Explore new design solutions for space enabled by high-performance, low-cost materials manufactured by MEX. This case study design was modified with generative design and design for AM (DfAM) and numerically simulated to assess random vibration response improvements and the impact of varying CF content. 3) Evaluate PA6-CF strict space requirements capabilities by outgassing and evaluating how PA6-CF exhibits enhanced mechanical properties, acceptable total mass loss post-outgassing in thermal-vacuum conditions, and post-machining surfaces have the required surface roughness. AM of HPP integrated with DfAM has the potential to satisfy stringent aerospace requirements. This research shows that PA6-CF can accelerate design cycles, reduce costs, and enable advanced design solutions for satellites and other space systems, highlighting its potential as a space polymer. These findings contribute to the broader discussion on how low-cost materials and advanced manufacturing techniques can revolutionize space mission design and development.

The unique design freedom offered by additive manufacturing (AM) technologies enables engineers to develop more innovative products with relatively lower costs within a shorter period of processing time in comparison with conventional manufacturing methods. On the other hand, the unique capabilities of AM have created a platform for researchers to combine several engineering methods with the new manufacturing technique to grow industrial applications as well as resolve the existing issues with AM processes. Understanding the research values that AM offers academic environments, this paper performs a systematic survey on AM-related research topics in the fields of mechanical engineering and materials science that have attracted much attention from research teams over the last few years. These topics, namely process modelling in AM, innovative research in AM, generative design by AM, material characterisation in AM processes, and finally, design for additive manufacturing (DfAM), are notably investigated through this study.

Design for Additive Manufacturing (DfAM) encompasses two primary strategies: adapting traditional designs for 3D printing and developing designs specifically optimized for additive manufacturing. The latter emphasizes consolidating assemblies and reducing weight, leveraging complex geometries and negative space through advanced techniques such as generative design and topology optimization. Critical considerations in the design phase include printing methods, material selection, support structures, and post-processing requirements. DfAM offers significant advantages over conventional subtractive manufacturing, including enhanced complexity, customization, and optimization, with transformative applications in aerospace, medical devices, and automotive industries. This review focuses on the automotive sector, systematically examining DfAM’s potential to redefine vehicle design, production processes, and industry standards. By conducting a comprehensive analysis of the existing literature and case studies, this research identifies gaps in the integration of additive manufacturing into broader manufacturing frameworks. The study contributes to the literature by providing insights into how 3D printing is currently reshaping automotive production by offering a forward-looking perspective on its future implications for the industry.

The cyclic manufacturing processes of Additive Manufacturing (AM) create three-dimensional objects layer by layer, offer a large freedom in design, and suffer from very different restrictions compared to conventional manufacturing processes. Nowadays, AM is a mature manufacturing technology capable of being applied in industrial series production. However, the AM technology enters the industrial series production only slowly. A main reason is the fact that engineers in industry have little to no experience in Design for Additive Manufacturing (DfAM) and an integrated experience transfer in engineering education is not yet widespread. To implement the AM technology, a course for graduate students was developed and established, mainly in the master studies and in the field of mechanical engineering. The goal of the course is that the students gain experience in the possibilities and the restrictions of DfAM. The course is based on the Experience Transfer Model (ETM) and is divided in two parts, a lecture and a team project. In the lecture, the students gain explicit knowledge for DfAM. In the practical team project, the students develop and produce AM prototypes based on a variety of AM processes and gain implicit knowledge. The concept of the team project is based on Problem-Based Learning (PBL). The whole team project follows a product development process, including presentations of milestone. The physical outcomes showed that the students are able to design AM optimized parts without falling back into old design patterns. In a survey at the end of the lecture series, the students gave the authors feedback. The large majority of the feedback was positive.

Of Narrow Windows and Acrobats: COVID-Responsiveness and Knock-Ons via Additive Manufacturing Capabilities

Additive manufacturing (AM) is widely used in various industries and has transformed the way products are designed and manufactured.Training workshops in conjunction with a part assessment framework encourage designers to use design for additive manufacturing (DfAM) considerations during the process of idea generation.In this respect, a training framework will assist researchers, educators, and students to evaluate and improve designs and encourage exploration of changes that need to be made during the design process.Successful implementation of DfAM considerations in engineering design classes are an important step in preparing students for professional careers.This research investigates the effect of DfAM integration in engineering curriculum on student design in the first-year engineering classes.Students are given a pre-intervention survey to gather information on their self-efficacy and prior experience with AM.Students complete a design challenge prior to the DfAM educational intervention that is then offered in the form of a design workshop in conjunction with a brief lecture.Ideas generated are collected and assessed using an assessment framework that encourages the use of DfAM considerations.Parts are scored on a scale of 1 to 4 in the following categories: part complexity, assembly complexity, number of separate parts, functionality, thin/smallest feature size, smallest tolerance, unsupported features, support material removal and the largest build plate contact.A score is generated and assigned to each design pre and post intervention.A post intervention survey is given to participants to gather information on changes in DfAM self-efficacy after the intervention as well as to gather feedback on suggested improvements and usefulness of the intervention workshops.The student outcomes before and after intervention are collected and compared for improvement in use of DfAM concepts.Gender differences are also investigated.Results show that DfAM educational intervention improves design scores and student DfAM self-efficacy.These results will enable educators to incorporate DfAM workshops in the engineering curriculum.

Lattice structures fabricated by additive manufacturing provide unprecedented opportunities for lightweight design, mechanical regulation, and functional integration. However, their efficient development remains constrained by vast design spaces and complex structure-property relationships. Inspired by the Materials Genome Initiative, we propose a Lattice Genome framework, a data-driven approach that integrates high-throughput simulations and performance databases to systematically map lattice properties. Specifically, we develop a component-level, regionally programmable, multi-objective design strategy for coordinated structure-property regulation. The efficacy of the proposed framework is demonstrated through two representative cases: (1) tailoring internal stress distributions to match preset targets under constant relative density, and (2) enhancing load capacity (62% higher than the conventional design) while redirecting failure away from critical zones in cavity-containing components. Overall, this work establishes a generalizable and scalable paradigm for intelligent lattice design and provides a transferable, data-centric platform that lays the groundwork for efficient, multifunctional applications in additive manufacturing.

The design methodologies and part shape algorithms for additive manufacturing (AM) are rapidly growing fields, proven to be of critical importance for the uptake of additive manufacturing of parts with enhanced performance in all major industrial sectors. The current trend for part design is a computationally driven approach where the parts are algorithmically morphed to meet the functional requirements with optimized performance in terms of material distribution. However, the manufacturability restrictions of AM processes are not considered at the primary design phases but at a later post-morphed stage of the part’s design. This paper proposes an AM design method to ensure: (1) optimized material distribution based on the load case and (2) the part’s manufacturability. The buildability restrictions from the direct energy deposition (DED) AM technology were used as input to the AM shaping algorithm to grant high AM manufacturability. The first step of this work was to define the term of AM manufacturability, its effect on AM production, and to propose a framework to estimate the quantified value of AM manufacturability for the given part design. Moreover, an AM design method is proposed, based on the developed internal stresses of the build volume for the load case. Stress tensors are used for the determination of the build orientation and as input for the part morphing. A top-down mesoscale geometric optimization is used to realize the AM part design. The DED Design for Additive Manufacturing (DfAM) rules are used to delimitate the morphing of the part, representing at the same time the freeform mindset of the AM technology. The morphed shape of the part is optimized in terms of topology and AM manufacturability. The topology optimization and AM manufacturability indicator (TMI) is introduced to screen the percentage of design elements that serve topology optimization and the ones that serve AM manufacturability. In the end, a case study for proof of concept is realized.

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.

With the broader industrial application of Additive Manufacturing (AM), designers are facing new challenges in conceptual design for AM. To better understand the problematic, the authors organised a design workshop with six experts in AM. The paper presents the results of the conducted design workshop and discusses the current and future trends in research on the conceptual design for AM.

The demand for additive manufacturing (AM) continues to grow as more industries look to integrate the technology into their product development. However, there is a deficit of designers skilled to innovate with this technology due to challenges in supporting designers with tools and education for their development in design for AM (DfAM). There is a need to introduce intuitive tools and knowledge to enable future designers to DfAM. Immersive virtual reality (VR) shows promise to serve as an intuitive tool for DfAM to aid designers during design evaluation. The goal of this research is to, therefore, identify the effects of immersion in design evaluation and study how evaluating designs for DfAM between mediums that vary in immersion, affects the results of the DfAM evaluation and the mental effort experienced from evaluating the designs. Our findings suggest that designers can use immersive and non-immersive mediums for DfAM evaluation without experiencing significant differences in the outcomes of the evaluation and the cognitive load experienced from conducting the evaluation. The findings from this work thus have implications for how industries can customize product and designer-talent development using modular design evaluation systems that leverage capabilities in immersive and non-immersive DfAM evaluation.