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Applied Sciences
Special Issues
Design for Additive Manufacturing: Latest Advances and Prospects
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Design for Additive Manufacturing: Latest Advances and Prospects

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Additive Manufacturing Technologies".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 381

Special Issue Editors

Dr. Sangjin Jung

E-Mail Website
Guest Editor
School of Mechanical, Aerospace, and Materials Engineering, Southern Illinois University Carbondale, Carbondale, IL 62901, USA
Interests: additive manufacturing; design for additive manufacturing; engineering design; design optimization
Dr. Giangiacomo Minak

E-Mail Website
Guest Editor
Department of Industrial Engineering (DIN), Alma Mater Studiorum, Università di Bologna, 47121 Forli, Italy
Interests: structural design; composite materials; smart materials; fluid–structure interaction; mechanical behavior of materials; design for additive manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Additive manufacturing (AM) is recognized as an advanced manufacturing process that can provide tremendous design freedom and dramatically reduce material waste. It also has the advantage of eliminating tooling costs compared to conventional manufacturing processes such as injection molding and metal forming, so AM has been actively utilized in industry—particularly in the aerospace, defense, automotive, and medical sectors. The advantage of AM is that it can produce a wide variety of products with intricate geometries that cannot be produced using traditional manufacturing methods. Design for Additive Manufacturing (DFAM) means that designers should tailor their designs to eliminate additive manufacturing difficulties and minimize AM production costs. DFAM involves exploring new designs to make them AM-ready or providing design guidelines for AM. DFAM can also include part consolidation or design optimization methods that can significantly reduce weight while maintaining or improving mechanical performance compared to simple solid shapes of parts. This Special Issue will publish original research articles, review articles, and short communications on the latest advances and prospects in DFAM, including, but not limited to, design optimization for AM, part consolidation, DFAM guidelines, design space exploration, topology optimization, and generative design.

Dr. Sangjin Jung
Dr. Giangiacomo Minak
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • additive manufacturing
  • design for additive manufacturing
  • design guidelines
  • part consolidation
  • support structure
  • design optimization
  • design space exploration
  • lattice structure
  • topology optimization
  • generative design

Published Papers (1 paper)

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Research

20 pages, 3929 KiB  
Article
Investigating and Characterizing the Systemic Variability When Using Generative Design for Additive Manufacturing
by Owen Peckham, Christer W. Elverum, Ben Hicks, Mark Goudswaard, Chris Snider, Martin Steinert and Sindre W. Eikevåg
Appl. Sci. 2024, 14(11), 4750; https://doi.org/10.3390/app14114750 - 31 May 2024
Abstract
This paper demonstrates the unpredictability of outcomes that result from compounding variabilities when using generative design (GD) coupled with additive manufacturing (AM). AM technologies offer the greatest design freedom and hence are most able to leverage the full capability of generative design (GD) [...] Read more.
This paper demonstrates the unpredictability of outcomes that result from compounding variabilities when using generative design (GD) coupled with additive manufacturing (AM). AM technologies offer the greatest design freedom and hence are most able to leverage the full capability of generative design (GD) tools and thus maximize potential improvements, such as weight, waste and cost reduction, strength, and part consolidation. Implicit in all studies reported in the literature is the fundamental assumption that the use of GD, irrespective of user experience or approach followed, yields high-performing and/or comparable design outputs. This work demonstrates the contrary and shows that achieving high performance with GD tools requires careful consideration of study setup and initial conditions. It is further shown that, when coupled with the inherent variability of AM parts, the potential variation in the performance of the design output can be significant, with poorer designs achieving only a fraction of that of higher-performing designs. This investigation shows how AM by Material Extrusion (MEX), which is used to manufacture components with polylactic acid (PLA), varies through different design pathways, bridging MEX and GD. Through a practical study across nine independently generated designs, the breadth of performance—due to initial GD conditions and MEX part strength unpredictability—is shown to reach 592%. This result suggest that current GD tools, including their underlying workflows and algorithms, are not sufficiently understood for users to be able to generate consistent solutions for an input case. Further, the study purports that training and consideration on GD setup are necessary to apply GD toolsets to achieve high-performing designs, particularly when applied in the context of MEX. Full article
(This article belongs to the Special Issue Design for Additive Manufacturing: Latest Advances and Prospects)
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