Design For Additive Manufacturing Methods Research Articles

The Current Design for Additive Manufacturing Research Frontier

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

A New Hybrid Generative Design Method for Functional & Lightweight Structure Generation in Additive Manufacturing

Additive Manufacturing (AM) technologies give more and more design freedom to designers and engineers to define and fabricate highly complex functional geometries and material compositions by using advanced Design for AM (DfAM) methods, such as generative design, lattice structure configuration, and bio-inspired design. However, it is still hard for designers to realize a complex design with ensured manufacturability via the use of a single DfAM method, especially for some complex functional lightweight structures. To fully exploit the advantages of AM processes, this paper proposes a new hybrid generative design method, which combines many existing DfAM methods and printing preparation optimization methods. Firstly, functional features are designed and analyzed with the use of AM processing knowledge and numerical simulation. Then, lattice structure configuration is used to achieve a lightweight and qualified part consolidation. In this process, to avoid the cumbersome AM preprocessing stages, support point determination and bio-inspired support generation methods are integrated into the part consolidation design stage. Finally, multiple designed volumes, realized by using different methods, are combined into a complete single structure. To demonstrate the applicability of the proposed method, a design case of a complex functional hydraulic component is presented at the end of the paper.

A new design for additive manufacturing method: applied on the bound metal deposition process

ABSTRACT To the best of our knowledge, most of the design methods that are currently used in the field of additive manufacturing only consider the final part, rather than the manufacturing process. Furthermore, these methods are less efficient when the bound metal deposition (BMD) process is used. We, therefore, came up with a new additive manufacturing design method specifically for BMD. This 6-step method allows designers to collect all the specs, optimise the part, and iteratively choose the best design, using the analytical hierarchy process. We assessed the performance of our new method using an aircraft canopy lever as a case study. Results indicated that it markedly outperformed conventional design methods, by allowing for a 27% reduction in mass, a 34% price reduction, and a 35% decrease in manufacturing time. Our promising methodology could enhance the use of BMD and, more broadly, the use of additive manufacturing. In the next step, this procedure will be further improved by defining and integrating other additive manufacturing process rules, depending on which process is used.

Industrial evaluation of design heuristics for additive manufacturing

Abstract Additive manufacturing (AM) has gained prominence over the last 15 years and become a viable manufacturing option. Since there is great industrial interest to implement serial production of products using AM, the education of engineers in design for additive manufacturing (DfAM) is important today. DfAM extends on design for manufacturing to provide knowledge about the new design opportunities enabled by AM. A set of design heuristics for additive manufacturing (DHAM) to assist designers with conceptual-level DfAM has previously been proposed. In this paper, these DHAM are evaluated through workshops with 3 engineering companies and 19 engineers, in which the participants re-design existing company products for AM using the DHAM as an aid, and then give feedback. The DHAM are well received by the workshop participants, and they find that the DHAM are good for teaching others about the capabilities of AM, provide a checklist of items to consider to help avoid oversights, and encourage the generation of new ideas. Criticisms include the number of examples provided and the lack of information about how to implement the ideas concretely. It is also found that the DHAM fulfil 16 of 18 criteria for early design phase DfAM methods, identified in academic literature.

Development of Technical Creativity Featuring Modified TRIZ-AM Inventive Principle to Support Additive Manufacturing

Abstract The design for additive manufacturing (DFAM) processing was introduced to fully utilize the design freedom provided by additive manufacturing (AM). Consequently, appropriate design methodologies have become essential for this technology. Recently, many studies have identified the importance of DFAM method utilization to produce AM parts, and theory of inventive problem-solving (TRIZ) is a strategy used to formalize design methodologies. TRIZ is a problem-solving tool developed to assist designers to find innovative and creative solutions. However, the pathway for synergizing TRIZ and DFAM is not clearly explained with respect to AM capabilities and complexities. This is mainly because most methods continue to involve use of the classical TRIZ principle, which was developed early in 1946, 40 years before AM technologies were introduced in the mid-1980s. Therefore, to tackle this issue, this study aims to enhance the 40 principles of classical TRIZ to accommodate AM design principles. A modified TRIZ-AM principle has been developed to define the pathway to AM solutions. TRIZ-AM cards are tools that assist designers to select inventive principles in the early phases of product design and development. The case study illustrates that even inexperienced AM users can creatively design innovative AM parts.

INVESTIGATING PERCEIVED MEANINGS AND SCOPES OF DESIGN FOR ADDITIVE MANUFACTURING

AbstractThe concept of Design for Additive Manufacturing (DfAM) is gaining popularity along with AM, despite its scopes are not well established. In particular, in the last few years, DfAM methods have been intuitively subdivided into opportunistic and restrictive. This distinction is gaining traction despite a lack of formalization. In this context, the paper investigates experts' understanding of DfAM. In particular, the authors have targeted educators, as the perception of DfAM scopes in the future will likely depend on teachers' view. A bespoke survey has been launched, which has been answer by 100 worldwide-distributed respondents. The gathered data has undergone several analyses, markedly answers to open questions asking for individual definitions of DfAM, and evaluations of the pertinence of meanings and acceptations from the literature. The results show that the main DfAM aspects focused on by first standardization attempts have been targeted, especially products, processes, opportunities and constraints. Beyond opportunistic and restrictive nuances, DfAM different understandings are characterized by different extents of cognitive endeavor, convergence vs. divergence in the design process, theoretical vs. hands on approaches.

Machine learning integrated design for additive manufacturing

For improving manufacturing efficiency and minimizing costs, design for additive manufacturing (AM) has been accordingly proposed. The existing design for AM methods are mainly surrogate model based. Due to the increasingly available data nowadays, machine learning (ML) has been applied to medical diagnosis, image processing, prediction, classification, learning association, etc. A variety of studies have also been carried out to use machine learning for optimizing the process parameters of AM with corresponding objectives. In this paper, a ML integrated design for AM framework is proposed, which takes advantage of ML that can learn the complex relationships between the design and performance spaces. Furthermore, the primary advantage of ML over other surrogate modelling methods is the capability to model input–output relationships in both directions. That is, a deep neural network can model property–structure relationships, given structure–property input–output data. A case study was carried out to demonstrate the effectiveness of using ML to design a customized ankle brace that has a tunable mechanical performance with tailored stiffness.

Systematic manufacturability evaluation using dimensionless metrics and singular value decomposition: a case study for additive manufacturing

Additive manufacturing has been presented as a novel and competitive method to achieve unprecedented part shapes and material complexities. Though this holds true in niche markets, the economic viability of additive manufacturing for large-scale industrial production is still in question. Companies often struggle to justify their investment in additive manufacturing due to challenges in the integration of such technologies into mainstream production. First, most additive technologies exhibit a relatively low production rate when compared with traditional production processes. Second, there is a lack of robust design for additive manufacturing methods and tools that enable the leveraging of the attendant unique capabilities, including the ability to form organic part geometries and automated part consolidations. Third, there is a dearth of systematic part screening methods to evaluate manufacturability in additive manufacturing. To tackle the challenge of manufacturability evaluation, the present work proposes a novel approach derived from latent semantic analysis and dimensional analysis to evaluate parts and their production for a variety of selected metrics. The selected metrics serve as descriptors of design features and manufacturing functions, which are developed using functional modeling and dimensional analysis theory. Singular-value decomposition and Euclidean distance measurement techniques are used to determine the relative manufacturability for a set of parts for a specified manufacturing process technology. The utility of the method is demonstrated for laser powder bed fusion technology. While demonstrated for additive manufacturing here, the developed approach can be expanded for any given set of manufacturing processes. Expansion of this systemic manufacturability analysis method can support part design decision-making, process selection, and design and manufacturing optimization.

G-DfAM: a methodological proposal of generative design for additive manufacturing in the automotive industry

Metal additive manufacturing is an emerging technology in the industry and has a great potential. Moreover, new technologies like generative design can maximize this potential by computing complex optimized parts for additive manufacturing solutions. However, there is a lack of methodologies combining both recent and promising technologies. This paper first establishes a state of the art of design for additive manufacturing (DfAM) and generative design methodologies. Then it proposes a generic workflow for generative design tools and it proposes a challenge approach to develop a new DfAM method including generative design tools. Finally, a global 4-step methodology maximizing the potential of generative design and additive manufacturing, the G-DfAM method, is presented and validated through an automotive use case.

Semi-supervised deep learning based framework for assessing manufacturability of cellular structures in direct metal laser sintering process

In recent years, metal cellular structures have drawn attentions in various industrial sectors due to their design freedoms and abilities to achieve multi-functional mechanical properties. However, metal cellular structures are difficult to fabricate due to their complex geometries, even with modern additive manufacturing technologies such as the direct metal laser sintering (DMLS) process. Assessing the manufacturability of metal cellular structures via a DMLS process is a challenging task as the geometric features of the structures are complex. Besides, via a DMLS process, the manufacturability also depends on the cumulative deformation of the layers during the manufacturing process. Existing methods on Design for Additive Manufacturing (DFAM) provide design guidelines that are based on past successful printed designs. However, they are not effective in predicting the manufacturability of metal cellular structures. In this paper, we propose a semi-supervised deep learning based manufacturability assessment (SSDLMA) framework to assess whether a metal cellular structure can be successfully manufactured from a given DMLS process. To enable efficient learning, we represent the complex cellular structures as 3D binary arrays with a simple yet efficient voxelisation method. We then train a deep learning based classifier using only a small amount of experimental data by adopting a semi-supervised learning approach. By running real experiments and comparing with existing DFAM methods and machine learning models, we demonstrate the advantages of the proposed SSDLMA framework. The proposed framework can be extended to predict the manufacturability of various other complex geometries beyond cellular structure in a reliable way even with a small number of training data.

Design for additive manufacturing: Framework and methodology

In recent decades additive manufacturing (AM) has evolved from a prototyping to a production technology. It is used to produce end-use-parts for medical, aerospace, automotive and other industrial applications from small series up to 100,000 of commercially successful products. Metal additive manufacturing processes are relatively slow, require complex preparation and post-processing treatment while using expensive machinery, resulting in high production costs per product. Design for Additive Manufacturing (DfAM) aims at optimizing the product design to deal with the complexity of the production processes, while also defining decisive benefits of the AM based product in the usage stages of its life cycle. Recent investigations have shown that the lack of knowledge on DfAM tools and techniques are seen as one of the barriers for the further implementation of AM. This paper presents a framework for DfAM methods and tools, subdivided into three distinct stages of product development: AM process selection, product redesign for functionality enhancement, and product optimization for the AM process chosen. It will illustrate the applicability of the design framework using examples from both research and industry.

A Design Framework for Additive Manufacturing: Integration of Additive Manufacturing Capabilities in the Early Design Process

Additive manufacturing has emerged as an integral part of modern manufacturing because of its unique capabilities in various application domains. As efforts to effectively apply additive manufacturing, design for additive manufacturing (DfAM) has risen to provide a set of guidelines based on a practical design framework or a methodology during the product design process of additive manufacturing. However, most existing DfAM methods do not effectively consider the capabilities of extant additive manufacturing technologies in the early design stages, and therefore it is hard to map functional requirements from customer needs onto a product design for additive manufacturing. Moreover, available DfAM methods tend to rely on the direct application of a specific decision method rather than a systematic approach with appropriate deployment and transformation of available design decision methods considering the additive manufacturing environment. Consequently, existing DfAM methods lack suitability for use by additive manufacturing novices. To tackle these issues, this study develops a design framework for additive manufacturing through the integration of axiomatic design and theory of inventive problem-solving (TRIZ). This integrated approach is effective because the axiomatic design approach can be used to systematically define and analyze a design problem, while the TRIZ problem-solving approach combined with an additive manufacturing database can be used as an idea generation tool to generate innovative solutions for the design problem. A case study for a housing cover redesign is presented to apply and validate the proposed design framework.

Design for additive manufacturing – a review of available design methods and software

PurposeThis paper aims to review recent research in design for additive manufacturing (DfAM), including additive manufacturing (AM) terminology, trends, methods, classification of DfAM methods and software. The focus is on the design engineer’s role in the DfAM process and includes which design methods and tools exist to aid the design process. This includes methods, guidelines and software to achieve design optimization and in further steps to increase the level of design automation for metal AM techniques. The research has a special interest in structural optimization and the coupling between topology optimization and AM.Design/methodology/approachThe method used in the review consists of six rounds in which literature was sequentially collected, sorted and removed. Full presentation of the method used could be found in the paper.FindingsExisting DfAM research has been divided into three main groups – component, part and process design – and based on the review of existing DfAM methods, a proposal for a DfAM process has been compiled. Design support suitable for use by design engineers is linked to each step in the compiled DfAM process. Finally, the review suggests a possible new DfAM process that allows a higher degree of design automation than today’s process. Furthermore, research areas that need to be further developed to achieve this framework are pointed out.Originality/valueThe review maps existing research in design for additive manufacturing and compiles a proposed design method. For each step in the proposed method, existing methods and software are coupled. This type of overall methodology with connecting methods and software did not exist before. The work also contributes with a discussion regarding future design process and automation.

Streamlined frameworks for advancing metal based additive manufacturing technologies

Abstract Metal-based additive manufacturing (AM) technologies such as selective laser melting (SLM) have seen successful applications in the gas turbine industry over the past years. The rapidly growing demand in AM requires in-depth knowledge of the process, materials and design for additive manufacturing (DFAM). However, the material characterization and process development are highly specific to a particular AM system, even for a number of standard alloys such as IN718 that are suitable for gas turbine applications. When the AM system changes or a new material becomes available, the whole development workflow needs to start almost “from scratch,” which consumes considerable time and effort. To address these issues, Siemens Power & Gas has established cross-divisional competence centers for AM to enhance collaborative material and process development. The article describes this framework and its effectiveness in streamlining the AM process and materials development. To close the design and manufacturing process chain, it is also critical to ensure that the full AM potential is accessible in design stages. In this article, a DFAM framework is proposed to drive the design paradigm shift to AM. In the framework, a complete DFAM process is defined based on existing studies of Siemens gas turbine applications. By integrating a set of DFAM methods, tools and considerations into the current gas turbine design processes, the AM-driven product design is enabled. We use Siemens large gas turbine applications to demonstrate the development and industrialization of AM using the frameworks. The benefits in reducing cost, expediting time to market, improving component performance and enabling new design freedom will be highlighted.

Towards additive manufacturing of intermediate objects (AMIO) for concepts generation

According to an analysis of existing Design For Additive Manufacturing (DFAM) methods, we first highlight that they present limits regarding product innovation. This paper then presents a creative approach to be integrated in the early stages of DFAM methods. Two case studies A and B are presented as the experimental application of the first stage of our creative approach. The results of these case studies highlight that designers need a new kind of Intermediate Representation (IR), especially to represent dynamic features. To address this need, we introduce the concept of AMIO Additive Manufacturing of Intermediate Objects. This new kind of IR is an expected output of the ideas generation stage. These intermediate objects are meant to be manipulated by all the design stakeholders, as an input for the concept generation stage, to enhance the generation of creative concepts for additive manufacturing.

Design for Additive Manufacturing Method for a Mechanical System Downsizing

Thanks to the opportunities offered by additive manufacturing (AM) processes, design rules are evolving to lead to lighter and stiffer parts with really more complex shapes than those obtained by conventional processes. Worldwide, new tools of assistance to the design are developed, gathered under the naming “Design for Additive Manufacturing” (DfAM). However, most of the DfAM methods suggested in the literature remain focused on only one component and are not considering the product as a system of components. Moreover, optimizations are mainly limited on reducing the mass or the number of parts, and more rarely on adding some functions.In this article, a new approach is presented to realize a multifunctional optimization of a mechanical system (MS). A methodology is first proposed in order to improve a product by using the AM opportunity. Then, to quantify the improvements of an optimized system, a new design indicator appointed “functional improvement rate” is defined. Finally, a case study, applied to a compressed-air Wobbler engine, is presented to demonstrate the relevance of the methodology and the functional improvement rate. The design adapted to traditional manufacturing is compared to a part-by-part optimized design and a multifunctional optimized design, both adapted to additive manufacturing.

Assembly Based Methods to Support Product Innovation in Design for Additive Manufacturing: An Exploratory Case Study

Additive manufacturing (AM) is emerging as an important manufacturing process and a key technology for enabling innovative product development. Design for additive manufacturing (DFAM) is nowadays a major challenge to exploit properly the potential of AM in product innovation and product manufacturing. However, in recent years, several DFAM methods have been developed with various design purposes. In this paper, we first present a state-of-the-art overview of the existing DFAM methods, then we introduce a classification of DFAM methods based on intermediate representations (IRs) and product's systemic level, and we make a comparison focused on the prospects for product innovation. Furthermore, we present an assembly based DFAM method using AM knowledge during the idea generation process in order to develop innovative architectures. A case study demonstrates the relevance of such approach. The main contribution of this paper is an early DFAM method consisting of four stages as follows: choice and development of (1) concepts, (2) working principles, (3) working structures, and (4) synthesis and conversion of the data in design features. This method will help designers to improve their design features, by taking into account the constraints of AM in the early stages.

Design for Additive Manufacturing of Cellular Structures

AbstractAdditive Manufacturing technologies enable the fabrication of parts and devices that are geometrically complex, have graded material compositions, and can be customized. To take advantage of these capabilities, it is important to assist designers in exploring unexplored regions of design spaces. This paper explores the opportunities and challenges in design for additive manufacturing. A new computer-aided design for additive manufacturing (DFAM) method is presented that is based on the process-structure-property-behavior model common in the materials design community. Examples of cellular materials design and manufacturing are used to illustrate the DFAM method.