Part Consolidation in Design for Additive Manufacturing: A Two-Level Approach Using Complexity Metrics

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.

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.

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.

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

Additive manufacturing (AM) technologies have seen fast growth in the last few decades. AM needs the implementation of new methods in design, fabrication, and delivery to end-users. Hence, AM techniques have given great flexibility to designers as the design of complex components and highly customized products are no longer binding from a manufacturability point of view. In addition to high material variety, this allows multi-material and variable mechanical characteristics of product manufacturing. This review paper addresses the design for additive manufacturing (DfAM) rules, guidelines, and tools to guide the designer to take advantage of the opportunities provided by AM whether in the early design stages (EDS) or in the later phase using computer-aided design (CAD) tools. It discusses issues related to the design for AM and proposes a DfAM framework applied in the design for the additive manufacturing process.

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.

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.

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.

The deployment of additive manufacturing (AM) processes had a rapid and broad increase in the last years, and the same trend is expected to hold in the near future. A way to better exploit the advantages of such technology is the use of appropriate information tools. However, today there is a lack of software applications devoted to this innovative manufacturing process. To overcome this issue, in the present work the application of manufacturing execution systems (MES), a tool commonly used in traditional manufacturing processes, is extended to AM. Furthermore, a framework for the deployment of shop-floor data, acquired through a monitoring system, in the design phase is presented: hence, MES should cooperate with design for additive manufacturing (DFAM), a set of methods and tools helpful to design a product and its manufacturing process taking into account AM specificities from the early design stages. In order to better understand the advantages of such cooperation, a case study for a proof of concept has been developed: the obtained results are promising, thus an online implementation would be recommended.

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.

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

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.

Considering Part Orientation in Design for Additive Manufacturing

Opportunities are offered by multiple Additive Manufacturing (AM) processes nowadays. Design rules are evolving to lead to lighter and stiffer parts with really more complex shapes than those obtained by conventional processes. Worldwide, new methodologies/tools of assistance for the design are developed such as Design for Additive Manufacturing (DfAM). Additive manufacturing can allow the development of new metamaterials and health-matter evaluation based on energy flow evaluation. In this paper, the objective is to generate a new methodology with DfAM based on mesoscale knowledge. It is generated with open lab bench and simple object characterization. A methodology is presented to formalize and quantify information at multilayer dimension. A database is also generated following Design of Experiments (DoE) to obtain metamodels. They are developed for specific features representative of AM geometric class such as overhanging, holes or walls for instance. Mereotopological primitives with their AM definitions are used to define features in term of space and time variables. This theory enables the formalization of knowledge at the mesoscopic scale taken into consideration layer by layer build-up. It is then possible to use it to integrate data and information to the different feature juxtapositions using recognition algorithm. Information for each feature can then be included and explicitly used to help the designer during detailed design phase. A global 4-steps DfAM methodology maximizing the potential of AM is presented and validated through a part from the space industry use case. It includes the definition of skeleton/skin entities, pattern decomposition, information associated based on material evaluation and decision for AM part.