Assembly-Level Design for Additive Manufacturing: Issues and Benchmark

2016 ◽  
Author(s):  
Sheng Yang ◽  
Yunlong Tang ◽  
Yaoyao Fiona Zhao
Keyword(s):  
Functional Analysis ◽  
Additive Manufacturing ◽  
Design Theory ◽  
Knowledge Integration ◽  
Functionally Graded ◽  
Design Stage ◽  
Future Research ◽  
Design For Additive Manufacturing ◽  
Product Evolution ◽  
Part Consolidation

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 Additive Manufacturing in the Cloud Platform

2017 ◽  
Author(s):  
Yuanbin Wang ◽  
Robert Blache ◽  
Xun Xu
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Additive Manufacturing ◽  
Full Advantage ◽  
New Technologies ◽  
Design Stage ◽  
Design For Additive Manufacturing ◽  
Cloud Platform ◽  
Am Processes

Additive manufacturing (AM) has experienced a phenomenal expansion in recent years and new technologies and materials rapidly emerge in the market. Design for Additive Manufacturing (DfAM) becomes more and more important to take full advantage of the capabilities provided by AM. However, most people still have limited knowledge to make informed decisions in the design stage. Therefore, an interactive DfAM system in the cloud platform is proposed to enable people sharing the knowledge in this field and guide the designers to utilize AM efficiently. There are two major modules in the system, decision support module and knowledge management module. A case study is presented to illustrate how this system can help the designers understand the capabilities of AM processes and make rational decisions.

Towards a Numerical Approach of Finding Candidates for Additive Manufacturing-Enabled Part Consolidation

2018 ◽  
Vol 140 (4) ◽  
Author(s):  
Sheng Yang ◽  
Florian Santoro ◽  
Yaoyao Fiona Zhao
Keyword(s):  
Additive Manufacturing ◽  
Ad Hoc ◽  
Numerical Approach ◽  
Design Guidelines ◽  
Design Stage ◽  
Physical Interaction ◽  
Physical Attributes ◽  
Conflict Handling ◽  
Part Consolidation ◽  
Typical Design

Part consolidation (PC) is one of the typical design freedoms enabled by additive manufacturing (AM) processes. However, how to select potential candidates for PC is rarely discussed. This deficiency has hindered AM from wider applications in industry. Currently available design guidelines are based on obsolete heuristic rules provided for conventional manufacturing processes. This paper first revises these rules to take account of AM constraints and lifecycle factors so that efforts can be saved and used at the downstream detailed design stage. To automate the implementation of these revised rules, a numerical approach named PC candidate detection (PCCD) framework is proposed. This framework is comprised of two steps: construct functional and physical interaction (FPI) network and PCCD algorithm. FPI network is to abstractly represent the interaction relations between components as a graph whose nodes and edges have defined physical attributes. These attributes are taken as inputs for the PCCD algorithm to verify conformance to the revised rules. In this PCCD algorithm, verification sequence of rules, conflict handling, and the optimum grouping approach with the minimum part count are studied. Compared to manual ad hoc design practices, the proposed PCCD method shows promise in repeatability, retrievability, and efficiency. Two case studies of a throttle pedal and a tripod are presented to show the application and effectiveness of the proposed methods.

scholarly journals A design for additive manufacturing case study: fingerprint stool on a BigRep ONE

2019 ◽  
Vol 25 (6) ◽  
pp. 1069-1079 ◽  
Author(s):  
James I. Novak ◽  
Jonathon O’Neill
Keyword(s):  
Additive Manufacturing ◽  
Large Scale ◽  
Future Research ◽  
Design For Additive Manufacturing ◽  
Fused Filament Fabrication ◽  
Content Type ◽  
Layer Height ◽  
Design Engineers ◽  
Final Design

Purpose This paper aims to present new qualitative and quantitative data about the recently released “BigRep ONE” 3 D printer led by the design of a one-off customized stool. Design/methodology/approach A design for additive manufacturing (DfAM) framework was adopted, with simulation data iteratively informing the final design. Findings Process parameters can vary manufacturing costs of a stool by over AU$1,000 and vary print time by over 100 h. Following simulation, designers can use the knowledge to inform iteration, with a second variation of the design being approximately 50 per cent cheaper and approximately 50 per cent faster to manufacture. Metrology data reveal a tolerance = 0.342 per cent in overall dimensions, and surface roughness data are presented for a 0.5 mm layer height. Research limitations/implications Led by design, this study did not seek to explore the full gamut of settings available in slicing software, focusing predominantly on nozzle diameter, layer height and number of walls alongside the recommended settings from BigRep. The study reveals numerous areas for future research, including more technical studies. Practical implications When knowledge and techniques from desktop 3 D printing are scaled up to dimensions measuring in meters, new opportunities and challenges are presented for design engineers. Print times and material costs in particular are scaled up significantly, and this study provides numerous considerations for research centers, 3 D printing bureaus and manufacturers considering large-scale fused filament fabrication manufacturing. Originality/value This is the first peer-reviewed study involving the BigRep ONE, and new knowledge is presented about the practical application of the printer through a design-led project. Important relationships between material volume/cost and print time are valuable for early adopters.

scholarly journals Design for Additive Manufacturing: A Systematic Review

2020 ◽  
Vol 12 (19) ◽  
pp. 7936 ◽  
Author(s):  
Abdullah Alfaify ◽  
Mustafa Saleh ◽  
Fawaz M. Abdullah ◽  
Abdulrahman M. Al-Ahmari
Keyword(s):  
Additive Manufacturing ◽  
Review Paper ◽  
Waste Minimization ◽  
Design For Additive Manufacturing ◽  
Great Flexibility ◽  
Sustainable Products ◽  
Novel Method ◽  
Part Consolidation ◽  
Method Of Production ◽  
Part Complexity

The last few decades have seen rapid growth in additive manufacturing (AM) technologies. AM has implemented a novel method of production in design, manufacture, and delivery to end-users. Accordingly, AM technologies have given great flexibility in design for building complex components, highly customized products, effective waste minimization, high material variety, and sustainable products. This review paper addresses the evolution of engineering design to take advantage of the opportunities provided by AM and its applications. It discusses issues related to the design of cellular and support structures, build orientation, part consolidation and assembly, materials, part complexity, and product sustainability.

Manufacturing Fixation in Design: Exploring the Effects of Manufacturing Assumptions on Design Ideas

2021 ◽  
Author(s):  
Jennifer Bracken Brennan ◽  
William B. Miney ◽  
Timothy W. Simpson ◽  
Kathryn W. Jablokow
Keyword(s):  
Additive Manufacturing ◽  
Cognitive Bias ◽  
The United States ◽  
Manufacturing Technology ◽  
Future Research ◽  
Manufacturing Processes ◽  
Design For Additive Manufacturing ◽  
Design Fixation ◽  
Design Ideas

Abstract Designing successfully for any new or unfamiliar manufacturing technology requires an ability to look beyond the manufacturing limitations that have constrained one’s design ideas in the past. However, potential cognitive bias or fixation on familiar manufacturing processes may make this a challenge for designers. In this paper we introduce the novel concept of Manufacturing Fixation in Design (MFD), which we define as unconscious and often unintentional adherence to a limited set of manufacturing processes and/or constraints and capabilities during the design ideation process. This concept is explored as a subset of design fixation, a cognitive bias often experienced by designers and engineers. After reviewing related literature in design fixation, we introduce MFD as a type of design fixation and explore ways in which fixation on manufacturing might be assessed. We then offer an exploratory case study involving design for additive manufacturing, an advanced manufacturing technology that has seen considerable interest lately. The case study involves a Design for Additive Manufacturing workshop given at an aerospace technology company headquartered in the United States with participants who are professional engineering designers. Results from the study are used to explore how MFD manifests and how its impact in design and optimization for manufacturing might be measured. Future research and next steps to validate the existence of MFD are also discussed.

Data-Driven Design Space Exploration and Exploitation for Design for Additive Manufacturing

2019 ◽  
Vol 141 (10) ◽  
Author(s):  
Yi Xiong ◽  
Pham Luu Trung Duong ◽  
Dong Wang ◽  
Sang-In Park ◽  
Qi Ge ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Design Process ◽  
Sampling Method ◽  
Design Space ◽  
Data Driven ◽  
Design Stage ◽  
Design For Additive Manufacturing ◽  
Detailed Design ◽  
Embodiment Design ◽  
Ankle Brace

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.

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