scholarly journals INVESTIGATING PERCEIVED MEANINGS AND SCOPES OF DESIGN FOR ADDITIVE MANUFACTURING

2021 ◽  
Vol 1 ◽  
pp. 1937-1946
Author(s):  
Aurora Berni ◽  
Yuri Borgianni ◽  
Martins Obi ◽  
Patrick Pradel ◽  
Richard Bibb

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.

2021 ◽  
Vol 1 ◽  
pp. 2571-2580
Author(s):  
Filip Valjak ◽  
Angelica Lindwall

AbstractThe advent of additive manufacturing (AM) in recent years have had a significant impact on the design process. Because of new manufacturing technology, a new area of research emerged – Design for Additive Manufacturing (DfAM) with newly developed design support methods and tools. This paper looks into the current status of the field regarding the conceptual design of AM products, with the focus on how literature sources treat design heuristics and design principles in the context of DfAM. To answer the research question, a systematic literature review was conducted. The results are analysed, compared and discussed on three main points: the definition of the design heuristics and the design principles, level of support they provide, as well as where and how they are used inside the design process. The paper highlights the similarities and differences between design heuristics and design principles in the context of DfAM.


2017 ◽  
Vol 5 (1) ◽  
pp. 3-18 ◽  
Author(s):  
Germain Sossou ◽  
Frédéric Demoly ◽  
Ghislain Montavon ◽  
Samuel Gomes

Abstract Firstly introduced as a prototyping process, additive manufacturing (AM) is being more and more considered as a fully-edged manufacturing process. The number of AM processes, along with the range of processed materials are expanding. AM has made manufacturable shapes that were too difficult (or even impossible) to manufacture with conventional technologies. This has promoted a shift in engineering design, from conventional design for manufacturing and assembly to design for additive manufacturing (DFAM). Research efforts into the DFAM field have been mostly dedicated to part's design, which is actually a requirement for a better industrial adoption. This has given rise to topologically optimized and/or latticed designs. However, since AM is also capable of manufacturing fully functional assemblies requiring a few or no assembly operations, there is a need for DFAM methodologies tackling product's development more holistically, and which are, therefore, dedicated to assembly design. Considering all the manufacturing issues related to AM of assembly-free mechanisms and available post-processing capabilities, this paper proposes a top-down assembly design methodology for AM in a proactive manner. Such an approach, can be seen as the beginning of a shift from conventional design for assembly (DFA) to a new paradigm. From a product's concept and a selected AM technology, the approach first provides assistance in the definition of the product architecture so that both functionality and successful manufacturing (including post-processing) are ensured. Particularly, build-orientation and downstream processes' characteristics are taken into account early in the design process. Secondly, for the functional flow (energy, material, signal) to be appropriately conveyed by the right amount of matter, the methodology provides guidance into how the components can be designed in a minimalism fashion leveraging the shape complexity afforded by AM. A mechanical assembly as case study is presented to illustrate the DFAM methodology. It is found that clearances and material (be it raw unprocessed material or support structures) within them plays a pivotal role in a successful assembly's design to be additively manufactured. In addition, the methodology for components' design proves to be an efficient alternative to topology optimization. Though, the approach can be extended by considering a strategy for part consolidation and the possibility to manufacture the assemblies with more than one AM process. As regards components' design, considering anisotropy can also improved the approach. Highlights Additive manufacturing is capable of printing fully functional assemblies without any assembly operations. It is found that Design For Additive Manufacturing is currently mainly focused on part's design. A process-independent, structured and systematic method for designing assembly-free mechanisms (for AM) is proposed. Build orientation and downstream processes (including post-processing capabilities) are taken into account early in the design process. A method - based on functional flows - for part's design in a minimalist fashion, is proposed.


2014 ◽  
Vol 635-637 ◽  
pp. 97-100 ◽  
Author(s):  
Shi Kai Jing ◽  
Guo Hua Song ◽  
Ji Hong Liu ◽  
Jing Tao Zhou ◽  
He Zhang

Additive Manufacturing (AM) is the digital manufacturing technology by which products are fabricated directly from computer models by selectively curing, depositing or consolidating materials in successive layers. The technology has provided an opportunity to rethink the methods of product design to maximize the product performance through the synthesis of material compositions, structure, and sizes. This overview is created to relate the unique capabilities of AM technologies and discuss the methods of product design. Finally, the current problems and difficulties in this field are discussed in this paper, and this paper proposes the development direction of the product design for additive manufacturing in the future.


2021 ◽  
pp. 1-56
Author(s):  
Anastasia Schauer ◽  
Kenton Fillingim ◽  
Katherine Fu

Abstract The goal of this work is to study the way student designers use design for additive manufacturing (DfAM) rules, or heuristics. It can be challenging for novice designers to succeed at creating successful designs for additive manufacturing (AM), given its differences from traditional manufacturing methods. A study was carried out to investigate the way novices apply DfAM heuristics when they receive them at different points in the design process. A design problem was presented to students, and three different groups of student participants were given a lecture on DfAM heuristics at three different points in the design process. The novelty and quality of each of the resulting designs was evaluated. Results indicate that although the DfAM heuristics lecture had no impact on the overall quality of the designs generated, participants who were given the heuristics lecture after the initial design session produced designs that were better suited for 3D printing in the second phase of the design activity. However, receiving this additional information appears to prevent students from creatively iterating upon their initial designs, as participants in this group did not experience an increase in novelty between the two sessions. Additionally, receiving the heuristics lecture increased all students' perceptions of their ability to perform DfAM-related tasks. These results validate the practicality of design heuristics as AM training tools while also emphasizing the importance of iteration in the design process.


2019 ◽  
Vol 25 (6) ◽  
pp. 1080-1094 ◽  
Author(s):  
Anton Wiberg ◽  
Johan Persson ◽  
Johan Ölvander

Purpose This 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/approach The 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. Findings Existing 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/value The 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.


Author(s):  
Rohan Prabhu ◽  
Scarlett R. Miller ◽  
Timothy W. Simpson ◽  
Nicholas A. Meisel

Design for manufacturing provides engineers with a structure for accommodating the limitations of traditional manufacturing processes. However, little emphasis is typically given to the capabilities of processes that enable novel design geometries, which are often a point of focus when designing products to be made with additive manufacturing (AM) technologies. In addition, limited research has been conducted to understand how knowledge of both the capabilities (i.e., opportunistic) and limitations (i.e., restrictive aspects) of AM affects design outcomes. This study aims to address this gap by investigating the effect of no, restrictive, and both, opportunistic and restrictive (dual) design for additive manufacturing (DfAM) education on engineering students’ creative process. Based on the componential model of creativity [1], these effects were measured through changes in (1) motivation and interest in AM, (2) DfAM self-efficacy, and (3) the emphasis given to DfAM in the design process. These metrics were chosen as they represent the cognitive components of ‘task-motivation’ and ‘domain relevant skills’, which in turn influence the learning and usage of domain knowledge in creative production. The results of the study show that while the short (45 minute) DfAM intervention did not significantly change student motivation and interest towards AM, students showed high levels of motivation and interest towards AM, before the intervention. Teaching students different aspects of DfAM also resulted in an increase in their self-efficacy in the respective topics. However, despite showing a greater increase in self-efficacy in their respective areas of training, the students did not show differences in the emphasis they gave to these DfAM concepts, in the design process. Further, students from all three education groups showed higher use of restrictive concepts, in comparison to opportunistic DfAM.


2019 ◽  
Vol 141 (10) ◽  
Author(s):  
Yi Xiong ◽  
Pham Luu Trung Duong ◽  
Dong Wang ◽  
Sang-In Park ◽  
Qi Ge ◽  
...  

Recently, design for additive manufacturing has been proposed to maximize product performance through the rational and integrated design of the product, its materials, and their manufacturing processes. Searching design solutions in such a multidimensional design space is a challenging task. Notably, no existing design support method is both rapid and tailored to the design process. In this study, we propose a holistic approach that applies data-driven methods in design search and optimization at successive stages of a design process. More specifically, a two-step surrogate model-based design method is proposed for the embodiment and detailed design stages. The Bayesian network classifier is used as the reasoning framework to explore the design space in the embodiment design stage, while the Gaussian process regression model is used as the evaluation function for an optimization method to exploit the design space in detailed design. These models are constructed based on one dataset that is created by the Latin hypercube sampling method and then refined by the Markov Chain Monte Carlo sampling method. This cost-effective data-driven approach is demonstrated in the design of a customized ankle brace that has a tunable mechanical performance by using a highly stretchable design concept with tailored stiffnesses.


Author(s):  
Marvin Drewel ◽  
Leon Özcan ◽  
Jürgen Gausemeier ◽  
Roman Dumitrescu

AbstractHardly any other area has as much disruptive potential as digital platforms in the course of digitalization. After serious changes have already taken place in the B2C sector with platforms such as Amazon and Airbnb, the B2B sector is on the threshold to the so-called platform economy. In mechanical engineering, pioneers like GE (PREDIX) and Claas (365FarmNet) are trying to get their hands on the act. This is hardly a promising option for small and medium-sized companies, as only a few large companies will survive. Small and medium-sized enterprises (SMEs) are already facing the threat of losing direct consumer contact and becoming exchangeable executers. In order to prevent this, it is important to anticipate at an early stage which strategic options exist for the future platform economy and which adjustments to the product program should already be initiated today. Basically, medium-sized companies in particular lack a strategy for an advantageous entry into the future platform economy.The paper presents different approaches to master the challenges of participating in the platform economy by using platform patterns. Platform patterns represent proven principles of already existing platforms. We show how we derived a catalogue with 37 identified platform patterns. The catalogue has a generic design and can be customized for a specific use case. The versatility of the catalogue is underlined by three possible applications: (1) platform ideation, (2) platform development, and (3) platform characterization.


2021 ◽  
Vol 1 ◽  
pp. 1657-1666
Author(s):  
Joaquin Montero ◽  
Sebastian Weber ◽  
Christoph Petroll ◽  
Stefan Brenner ◽  
Matthias Bleckmann ◽  
...  

AbstractCommercially available metal Laser Powder Bed Fusion (L-PBF) systems are steadily evolving. Thus, design limitations narrow and the diversity of achievable geometries widens. This progress leads researchers to create innovative benchmarks to understand the new system capabilities. Thereby, designers can update their knowledge base in design for additive manufacturing (DfAM). To date, there are plenty of geometrical benchmarks that seek to develop generic test artefacts. Still, they are often complex to measure, and the information they deliver may not be relevant to some designers. This article proposes a geometrical benchmarking approach for metal L-PBF systems based on the designer needs. Furthermore, Geometric Dimensioning and Tolerancing (GD&T) characteristics enhance the approach. A practical use-case is presented, consisting of developing, manufacturing, and measuring a meaningful and straightforward geometric test artefact. Moreover, optical measuring systems are used to create a tailored uncertainty map for benchmarking two different L-PBF systems.


2020 ◽  
Vol 11 (1) ◽  
pp. 238
Author(s):  
Yun-Fei Fu ◽  
Kazem Ghabraie ◽  
Bernard Rolfe ◽  
Yanan Wang ◽  
Louis N. S. Chiu

The smooth design of self-supporting topologies has attracted great attention in the design for additive manufacturing (DfAM) field as it cannot only enhance the manufacturability of optimized designs but can obtain light-weight designs that satisfy specific performance requirements. This paper integrates Langelaar’s AM filter into the Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT) algorithm—a new element-based topology optimization method capable of forming smooth boundaries—to obtain print-ready designs without introducing post-processing methods for smoothing boundaries before fabrication and adding extra support structures during fabrication. The effects of different build orientations and critical overhang angles on self-supporting topologies are demonstrated by solving several compliance minimization (stiffness maximization) problems. In addition, a typical compliant mechanism design problem—the force inverter design—is solved to further demonstrate the effectiveness of the combination between SEMDOT and Langelaar’s AM filter.


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