A framework for mapping design for additive manufacturing knowledge for industrial and product design

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.

Additive Manufacturing (AM) brought new design freedom and possibilities that enable design and manufacturing of products with new forms and functionalities. To utilise these possibilities a new design approach emerged, Design for Additive Manufacturing (DfAM), that contains methods and tools for supporting AM oriented design process. Designers working with AM are aware of the need to apply DfAM and AM possibilities in conceptual design phase where they have the most significant influence on product architecture and form but are facing a lack of suitable DfAM approaches for early design phases. Therefore, the presented research is investigating possibilities of storing and representing AM knowledge in the form of design principles to be used in the conceptual design phase. The paper proposes conceiving of Design Principles for Additive Manufacturing repository where formalised AM knowledge is stored in the form of design principles and structured based on function criteria. In the paper, various elements of design principle representation are discussed, as well as their role in the conceptual design process.

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.

Design for additive manufacturing (DfAM) creates opportunities for improved product design. Part consolidation (PC) is an important design opportunity in DfAM. The existing methodologies for part consolidation in DfAM are mostly based on expert judgment or heuristic rules. In addition, they only suggest the candidates for part consolidation. They do not assess the manufacturability of the consolidated parts. This paper proposes a two-level approach for part consolidation using complexity measures at the system level and part level. At the system level, the centrality score (a complex network measure) is used to identify parts with high potential for consolidation, and at the part level, a geometry-based complexity measure—modified complexity factor (MCF)—is used to assess the manufacturability. This two-step approach is expected to improve efficiency and effectiveness in DfAM since it uses minimal information at the system level and detailed assessment of manufacturability is done only for those parts where AM potential is high. Such an approach can be extremely useful to product designers at the early stages of design. Two case studies are presented to illustrate the effectiveness of the proposed approach.

Purpose This paper aims to provide a multi-objective optimization problem in design for manufacturing (DFM) approach for fused deposition modeling (FDM). This method considers the manufacturing criteria and constraints during the design by selecting the best manufacturing parameters to guide the designer and manufacturer in fabrication with FDM. Design/methodology/approach Topological optimization and bi-objective optimization problems are suggested to complete the DFM approach for design for additive manufacturing (DFAM) to define a product. Topological optimization allows the shape improvement of the product through a material distribution for weight gain based on the desired mechanical behavior. The bi-objective optimization problem plays an important role to evaluate the manufacturability by quantification and optimization of the manufacturing criteria and constraint simultaneously. Actually, it optimizes the production time, required material regarding surface quality and mechanical properties of the product because of two significant parameters as layer thickness and part orientation. Findings A comprehensive analysis of the existing DFAM approaches illustrates that these approaches are not developed sufficiently in terms of manufacturability evaluation in quantification and optimization levels. There is no approach that investigates the AM criteria and constraints simultaneously. It is necessary to provide a decision-making tool for the designers and manufacturers to lead to better design and manufacturing regarding the different AM characteristics. Practical implications To assess the efficiency of this approach, a wheel spindle is considered as a case study which shows how this method is capable to find the best design and manufacturing solutions. Originality/value A multi-criteria decision-making approach as the main contribution is developed to analyze FDM technology and its attributes, criteria and drawbacks. It completes the DFAM approach for FDM through a bi-objective optimization problem which deals with finding the best manufacturing parameters by optimizing production time and material mass because of the product mechanical properties and surface roughness.

Design for manufacture and assembly (DfMA) in the architectural, engineering, and construction (AEC) industry is attracting the attention of designers, practitioners, and construction project stakeholders. Digital fabrication (Dfab) and design for additive manufacturing (DfAM) practices are found in current need of further research and development. The DfMA’s conceptual function is to maximize the process efficiency of Dfab and AM building projects. This work reviewed 171 relevant research articles over the past few decades. The concepts and the fundamentals of DfMA in building and construction were explored. In addition, DfMA procedures for Dfab, DfAM, and AM assembly processes were discussed. Lastly, the current machine learning research on DfMA in construction was also highlighted. As Dfab and DFAM are innovated, practical DFMA techniques begin to develop to a great extent. Large research gaps in the DfMA for Dfab and DfAM can be filled in terms of integrating them with product structural performance, management, studied cases, building information modeling (BIM), and machine learning to increase operational efficiency and sustainable practices.

Design for manufacturing to design for Additive Manufacturing: Analysis of implications for design optimality and product sustainability

The present study explores the phenomenon of remixing in product design for additive manufacturing (AM). In contrast to other manufacturing techniques, AM offers unprecedented flexibility in adapting existing product designs to changing requirements. However, in order to benefit from this potential, structured design procedures and tools are indispensable. As a possible solution, online platforms for collaborative 3D model creation are increasingly implementing features for remixing, a concept describing the creation of new models on the foundation of existing design elements. Against this backdrop, the objective of this research is to provide evidence for the value of remixing as an organizational intervention for improving product design processes. To this end, we present a mixed methods approach using data from Thingiverse, the world's largest AM‐related online community. In a first step, we investigate qualitative data from 81 individual remix‐based designs to identify the underlying mechanisms of remixing. We identify six such mechanisms that can further be grouped by the intended outcome of the respective process (creativity‐oriented: inspiration, play, learning;productivity‐oriented: speed, improvement, empowerment). In a second step, we turn to a quantitative analysis of platform data, which indicates that remixing may lead to better design process outcomes in terms of quantity and diversity of designs. Furthermore, we find that designs created by remixing designers are significantly more often printed by community members suggesting that remixing helps ensure manufacturing compatibility akin to continuous process improvement. Our research has several implications for individual designers and organizations engaging with product design for AM.

The emerging additive manufacturing (AM) technology works in a layer-wise fashion which makes it possible to manipulate material distribution and composition. The resulting effects are reflected on the potential of innovative shape design, consolidated assembly, optimized topology, and functionally graded material. These new characteristics force designers to rethink about how to make a better engineering design. However, existing design theory and methodology cannot take these potentials provided by AM into account. To fill this void, various design for additive manufacturing (DFAM) approaches are reported. Unfortunately, majority of them focused on part-level redesign without potential of being extended to assembly-level applications. In order to shed a light into this emerging field, an overview of current assembly-level DFAM is summarized in this paper. After that, existing issues including the absent analysis of AM’s impact on conceptual design, the lack of explicit functional analysis method, the shortage of decision-making support for part consolidation, the deficiency of functional reasoning approaches to generate AM-enabled features, and the scarcity of integrating manufacturing and assembly knowledge into design stage are analyzed and discussed. However, it seems that addressing these issues is such a large scope that collaborative efforts are in need from both design and manufacturing communities. Therefore, this paper serves as a call to action for the research community to establish a comprehensive assembly-level/ product-level DFAM method to realize product evolution. As an initial benchmark, authors propose a three-stage design methodology on the basis of the Systematic Design approach. In the presented framework, functional analysis, part consolidation, and structural optimization with process knowledge integration are much highlighted. Moreover, a simple redesign case study is exemplified to clarify existing issues and how the benchmark method works. In the end, this paper is wrapped up with future research.

A Design for Additive Manufacturing Framework: Product Function Integration and Structure Simplification

The vertical lunchbox (VL) project, propose an innovative and functional solution for movement. Traditional lunchboxes often fail to meet the diverse needs of today’s consumers, including the desire for convenience, portability, and meal freshness. This project uses the advantages of additive manufacturing to create a lunchbox that not only offers practicality and usability but also incorporates distinctive features tailored to present lifestyles. The design process employs a concept of Design for Additive Manufacturing (DfAM) and Design for Manufacturing and Assembly (DfMA) principles. These concepts enable the development of required features and at the same time reducing assembly time and improve the functionality. Important features of the vertical lunchbox include customizable compartments that can accommodate ice packs, the lid that can be used as a handphone holder, and removable containers for easy cleaning. These features are catering to the needs of busy professionals and students. Parametric modeling and topological optimization helped to ensure that the lunchbox design is appealing and at the same time lightweight and strong. The adoption of 3D printing technology enabled faster production processes with required dimensional accuracy. Test results indicate that the vertical lunchbox meets most of the user requirements. The important features such as a secured locking and snap-fit lid design ensured the reliable transport containers for carrying food. In Addition, the project proved that additive manufacturing can be used for time to market approach of the product development. Finally, the vertical lunchbox project demonstrates how advanced manufacturing techniques can transform traditional product design to meet modern consumer needs.

Activity Modeling of Preliminary Additive Manufacturing Process Planning

This study establishes a systematic and reproducible workflow for topology optimization (TO) of indexable face milling cutter bodies with integrated internal coolant channels, designed for Additive Manufacturing (AM) of metallic parts. Grounded in Design for Additive Manufacturing (DfAM) principles, the workflow combines displacement-based TO and computational fluid dynamics analysis to generate simulation-driven tool geometries tailored to the constraints of AM. By leveraging iterative design knowledge, the proposed methodology enhances the scalability and repeatability of the design process, reducing development time and supporting rapid adaptation across various tool geometries. AM is explicitly exploited to integrate support-free internal coolant channels directed toward the insert cutting edge, thereby achieving a 20% mass reduction relative to the initial milling tool designs, and improving material usage efficiency at the design stage. The workflow yields numerically optimized geometries that maintain simulated global stiffness under the considered loading conditions and exhibit coolant flow distributions that effectively target the exposed cutting edges. These simulation results demonstrate the feasibility of an AM oriented, workflow-based approach for the numerical design of milling tools with internal cooling, mass reduction and provide a focused basis for subsequent experimental validation and comparison with conventionally manufactured counterparts.

Smart manufacturing involves the use of emergent technologies and requires dynamic feedback of customer’s demands. These concerns need a rapid Decision Support System (DSS) considering emergent manufacturing processes such as Additive (AM) and Hybrid (HM) Manufacturing and tracking the product changes. This paper proposes a DSS for process selection based on manufacturing complexity and cost. The complexity parameters, deduced from design for manufacturing (DFM), design for additive manufacturing (DFAM) and design for hybrid manufacturing (DFHM) rules, are automatically extracted from computer aided design (CAD) model to follow the product changes. Cost models are defined for each manufacturing process type. In design phase, the manufacturing cost estimation allows considering the cost as a selection factor. The combined complexity based on manufacturing difficulty and cost represents a new paradigm for process selection. The case studies show the reliability of the proposed DSS and its ability to respect the company resources and strategy.

Additive manufacturing (AM) is a “game-changing” digital fabrication technology that can fundamentally alter the way products are designed and manufactured. In nuclear engineering, the application of additive manufacturing can enable advanced reactor designs with better performance, lower cost, and quicker deployment. To exploit the maximum potential of additive manufacturing, design for additive manufacturing (DfAM) is needed, DfAM can leverage the complexity freedom offered by additive manufacturing technology to break traditional design constraints and achieve enhanced performance. Generative design, one of the key technologies in DfAM, is an iterative exploration method using AI-driven algorithms to generate a series of design solutions that meet specified constraints. In this work, a generative design platform for micro-reactors is developed based on DASSULT SIMULIA Isight. To simulate and analyze the multi-physics performance of the reactor design, various multi-physics simulation codes are integrated on this generative design platform, including RMC for neutron physics analysis, ANSYS Fluent for computational fluid dynamics analysis, and ANSYS Mechanical for structural mechanics analysis. A multi-objective optimization method is used to explore geometric parameters and fuel configurations to achieve better neutronics, thermal-fluidics, and structural mechanics performance. The generative design solution is a 4 MW helium-cooled nuclear reactor which includes 3D-printed silicon carbide (SiC) matrix embedded with tri-structural isotropic (TRISO) particle fuel. Helium at 3 MPa is chosen as the coolant, and the cross-shaped coolant channels are distributed within the matrix to remove the heat generated by fission. According to the multiphysics analysis, the reactor has a initial keff of 1.0571, power peaking factor FQ of 1.50, and operating lifetime of 1200 EFPD (Effective Full Power Days), with a maximum temperature of 1331 K and a peak thermal stress of 141.3 MPa. The generative design method and platform provide a systematic and efficient approach for the design of additively manufactured micro-reactors, and the generative design solution demonstrates the feasibility and effectiveness of the proposed method.