scholarly journals The Design for Additive Manufacturing Worksheet

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Joran W. Booth ◽  
Jeffrey Alperovich ◽  
Pratik Chawla ◽  
Jiayan Ma ◽  
Tahira N. Reid ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Prior Knowledge ◽  
Real World ◽  
High Volume ◽  
Visual Design ◽  
Design For Additive Manufacturing ◽  
Novice Users ◽  
The Cost ◽  
Am Processes

Additive manufacturing (AM) technologies have become integral to modern prototyping and manufacturing. Therefore, guidelines for using AM are necessary to help users new to the technology. Many others have proposed useful guidelines, but these are rarely written in a way that is accessible to novice users. Most guidelines (1) assume the user has extensive prior knowledge of the process, (2) apply to only a few AM technologies or a very specific application, or (3) describe benefits of the technology that novices already know. In this paper, we present a one-page, visual design for additive manufacturing worksheet for novice and intermittent users which addresses common mistakes as identified by various expert machinists and additive manufacturing facilities who have worked extensively with novices. The worksheet helps designers assess the potential quality of a part made using most AM processes and indirectly suggests ways to redesign it. The immediate benefit of the worksheet is to filter out bad designs before they are printed, thus saving time on manufacturing and redesign. We implemented this as a go-no-go test for a high-volume AM facility where users are predominantly novices, and we observed an 81% decrease in the rate of poorly designed parts. We also tested the worksheet in a classroom, but found no difference between the control and the experimental groups. This result highlights the importance of motivation since the cost of using AM in this context was dramatically lower than real-world costs. This second result highlights the limitations of the worksheet.

The Design for Additive Manufacturing Worksheet

2016 ◽  
Author(s):  
Joran W. Booth ◽  
Jeffrey Alperovich ◽  
Tahira N. Reid ◽  
Karthik Ramani
Keyword(s):  
Additive Manufacturing ◽  
3D Printing ◽  
Prior Knowledge ◽  
High Volume ◽  
Visual Design ◽  
Design For Additive Manufacturing ◽  
Manufacturing Errors ◽  
Novice Users ◽  
Modern Manufacturing

Additive manufacturing (AM) technologies have become integral to the modern manufacturing process. These roles are filled both in prototyping and production. Many studies have been conducted and lists been written on guidelines for AM. While these lists are useful, virtually none are written in a way that is accessible to novice users of AM, such as Makers. Most guidelines assume the user has extensive prior knowledge of the process, apply to only a few AM technologies, or describe benefits of the technology that novices already know. In this paper, we present a short, visual design-for-additive-manufacturing worksheet for novice and intermittent users. It addresses common mistakes and problems as identified by various expert machinists and additive manufacturing facilities. The worksheet helps designers accurately assess the potential quality of a part that is to be made using an AM process by giving intuitive feedback and indirectly suggest changes to improve a design. The immediate benefit of this worksheet is that it can help to streamline designs and reduce manufacturing errors. We validated it in a high-volume 3D-printing facility (Boilermaker Lab) where users are predominantly novice or intermittent. After the worksheet was implemented in the Boilermaker Lab, both the rate of print failures and reprinted parts fell roughly 40%.

Consideration of the Manufacturing Trajectories in a Global Design for Additive Manufacturing Methodology

2012 ◽  
Author(s):  
R. Ponche ◽  
O. Kerbrat ◽  
P. Mognol ◽  
J. Y. Hascoet
Keyword(s):  
Additive Manufacturing ◽  
Cost Reduction ◽  
Design Problem ◽  
Manufacturing Processes ◽  
Strong Impact ◽  
Direct Impact ◽  
Design For Additive Manufacturing ◽  
Physical Phenomena ◽  
Am Processes

Additive Manufacturing (AM) is a new way of part production which opens up new perspectives of conception as mass and cost reduction and increase of functionalities. However these processes have their own characteristics which as for all the manufacturing processes have a direct impact on the manufactured parts quality. Especially, because the manufacturing trajectories have a influence on the physical phenomena during the process, they have also a strong impact on the quality of the produced parts in terms of geometry. In this paper, the choice of manufacturing trajectories and their impacts on the final shape and quality of the parts is integrated into a global Design For Additive Manufacturing (DFAM) methodology which allows to move from functional specifications of a design problem to a proposition of an adapted part for AM processes.

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.

scholarly journals Do We Get What We Pay For? Examining the Relationship Between Payments and Clinical Outcomes in High-Volume Elective Surgery in a Commercially-Insured Population

2020 ◽  
Vol 57 ◽  
pp. 004695802096878
Author(s):  
Madison Arenchild ◽  
Anaeze C. Offodile ◽  
Lee Revere
Keyword(s):  
Elective Surgery ◽  
High Volume ◽  
Quality Of Healthcare ◽  
Healthcare Services ◽  
Care Provision ◽  
Quality Outcomes ◽  
Prolonged Length ◽  
The Cost ◽  
The Relationship

Studies evaluating the cost and quality of healthcare services have produced inconsistent results. We seek to determine if higher paid hospitals have higher quality outcomes compared to those receiving lower payments, after accounting for clinical and market level factors. Using inpatient commercial claims from the IBM® MarketScan® Research Databases, we used an ordinal logistic regression to analyze the association between hospital median payments for elective hip and knee procedures and 3 quality outcomes: prolonged length of stay, complication rate, and 30-day readmission rate. Patient-level and market factor covariates were appropriately adjusted. Hospital-level payments were found to be not significantly correlated with hospital quality of care. This research suggests that higher payments cannot predict higher quality outcomes. This finding has implications for provider-payer negotiations, value-based insurance designs, strategies to increase high-value care provision, and consumer choices in an increasingly consumer-oriented healthcare landscape.

scholarly journals Review of Powder Bed Fusion Additive Manufacturing for Metals

Metals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1391
Author(s):  
Leila Ladani ◽  
Maryam Sadeghilaridjani
Keyword(s):  
Additive Manufacturing ◽  
Three Dimensional ◽  
Disruptive Technology ◽  
Powder Bed ◽  
Powder Particles ◽  
Metallic Parts ◽  
The Cost ◽  
Scientific Questions ◽  
Metal Additive

Additive manufacturing (AM) as a disruptive technology has received much attention in recent years. In practice, however, much effort is focused on the AM of polymers. It is comparatively more expensive and more challenging to additively manufacture metallic parts due to their high temperature, the cost of producing powders, and capital outlays for metal additive manufacturing equipment. The main technology currently used by numerous companies in the aerospace and biomedical sectors to fabricate metallic parts is powder bed technology, in which either electron or laser beams are used to melt and fuse the powder particles line by line to make a three-dimensional part. Since this technology is new and also sought by manufacturers, many scientific questions have arisen that need to be answered. This manuscript gives an introduction to the technology and common materials and applications. Furthermore, the microstructure and quality of parts made using powder bed technology for several materials that are commonly fabricated using this technology are reviewed and the effects of several process parameters investigated in the literature are examined. New advances in fabricating highly conductive metals such as copper and aluminum are discussed and potential for future improvements is explored.

A Knowledge Management System to Support Design for Additive Manufacturing Using Bayesian Networks

2018 ◽  
Vol 140 (5) ◽  
Author(s):  
Yuanbin Wang ◽  
Robert Blache ◽  
Pai Zheng ◽  
Xun Xu
Keyword(s):  
Knowledge Management ◽  
Additive Manufacturing ◽  
Bayesian Networks ◽  
Management System ◽  
Knowledge Management System ◽  
Critical Parameters ◽  
Design For Additive Manufacturing ◽  
Fused Deposition ◽  
Information Layer ◽  
Am Processes

Design for additive manufacturing (DfAM) is gaining increasing attention because of the unique capabilities that additive manufacturing (AM) technologies provide. While they have the ability to produce more complex shapes at no additional cost, AM technologies introduce new constraints. A detailed knowledge of the AM process plays an important role in the design of parts in order to achieve the desired print result. However, research on knowledge management in this area is still limited. The large number of different AM processes, their individual sets of critical parameters and the variation in printing all contribute to a high level of uncertainty in this knowledge domain. Applying AM at the early stages of design projects introduces another source of uncertainty, as requirements are often not well defined at that point. In this paper, a knowledge management system using Bayesian networks (BNs) is proposed to model AM knowledge in cases where there is some uncertainty and fill the knowledge gap between designers and AM technologies. The structure of the proposed model is defined here by introducing the overview layer and detailed information layer. In each layer, different types of nodes and their causal relationships are defined. The system can learn conditional probabilities in the model from different sources of information and inferences can be conducted in both forward and backward directions. To verify the accuracy of the BNs, a sample model for dimensional accuracy in the fused deposition modeling (FDM) process is presented and the results are compared with other methods. A case study is provided to illustrate how the proposed system can help designers with different design questions understand the capabilities of AM processes and find appropriate design and printing solutions.

The Earlier the Better? Investigating the Importance of Timing on Effectiveness of Design for Additive Manufacturing Education

2018 ◽  
Author(s):  
Rohan Prabhu ◽  
Scarlett R. Miller ◽  
Timothy W. Simpson ◽  
Nicholas A. Meisel
Keyword(s):  
Additive Manufacturing ◽  
Self Efficacy ◽  
Educational Interventions ◽  
Layer By Layer ◽  
Design For Additive Manufacturing ◽  
Layer Deposition ◽  
Subtractive Manufacturing ◽  
The Impact ◽  
Design Ideas ◽  
Am Processes

Additive Manufacturing (AM) is a novel process that enables the manufacturing of complex geometries through layer-by-layer deposition of material. AM processes provide a stark contrast to traditional, subtractive manufacturing processes, which has resulted in the emergence of design for additive manufacturing (DfAM) to capitalize on AM’s capabilities. In order to support the increasing use of AM in engineering, it is important to shift from the traditional design for manufacturing and assembly mindset, towards integrating DfAM. To facilitate this, DfAM must be included in the engineering design curriculum in a manner that has the highest impact. While previous research has systematically organized DfAM concepts into process capability-based (opportunistic) and limitation-based (restrictive) considerations, limited research has been conducted on the impact of teaching DfAM on the student’s design process. This study investigates this interaction by comparing two DfAM educational interventions conducted at different points in the academic semester. The two versions are compared by evaluating the students’ perceived utility, change in self-efficacy, and the use of DfAM concepts in design. The results show that introducing DfAM early in the semester when students have little previous experience in AM resulted in the largest gains in students perceiving utility in learning about DfAM concepts and DfAM self-efficacy gains. Further, we see that this increase relates to greater application of opportunistic DfAM concepts in student design ideas in a DfAM challenge. However, no difference was seen in the application of restrictive DfAM concepts between the two interventions. These results can be used to guide the design and implementation of DfAM education.

Impact of Timing in the Design Process on Students' Application of Design for Additive Manufacturing Heuristics

2021 ◽  
pp. 1-56
Author(s):  
Anastasia Schauer ◽  
Kenton Fillingim ◽  
Katherine Fu
Keyword(s):  
Additive Manufacturing ◽  
Design Process ◽  
Design Activity ◽  
Second Phase ◽  
Design For Additive Manufacturing ◽  
Additional Information ◽  
Design Heuristics ◽  
Traditional Manufacturing ◽  
The Way

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.

Assessment of Dimensional Integrity and Spatial Defect Localization in Additive Manufacturing Using Spectral Graph Theory

2015 ◽  
Vol 138 (5) ◽  
Author(s):  
Prahalad K. Rao ◽  
Zhenyu Kong ◽  
Chad E. Duty ◽  
Rachel J. Smith ◽  
Vlastimil Kunc ◽  
...  
Keyword(s):  
Graph Theory ◽  
Additive Manufacturing ◽  
Standardized Test ◽  
Statistical Significance ◽  
Spectral Graph Theory ◽  
Unique Challenge ◽  
Integrity Assessment ◽  
Spectral Graph ◽  
Am Processes

The ability of additive manufacturing (AM) processes to produce components with virtually any geometry presents a unique challenge in terms of quantifying the dimensional quality of the part. In this paper, a novel spectral graph theory (SGT) approach is proposed for resolving the following critical quality assurance concern in the AM: how to quantify the relative deviation in dimensional integrity of complex AM components. Here, the SGT approach is demonstrated for classifying the dimensional integrity of standardized test components. The SGT-based topological invariant Fiedler number (λ2) was calculated from 3D point cloud coordinate measurements and used to quantify the dimensional integrity of test components. The Fiedler number was found to differ significantly for parts originating from different AM processes (statistical significance p-value <1%). By comparison, prevalent dimensional integrity assessment techniques, such as traditional statistical quantifiers (e.g., mean and standard deviation) and examination of specific facets/landmarks failed to capture part-to-part variations, proved incapable of ranking the quality of test AM components in a consistent manner. In contrast, the SGT approach was able to consistently rank the quality of the AM components with a high degree of statistical confidence independent of sampling technique used. Consequently, from a practical standpoint, the SGT approach can be a powerful tool for assessing the dimensional integrity of the AM components, and thus encourage wider adoption of the AM capabilities.

scholarly journals A bibliometric analysis of research in design for additive manufacturing

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Martins Ugonna Obi ◽  
Patrick Pradel ◽  
Matt Sinclair ◽  
Richard Bibb
Keyword(s):  
Additive Manufacturing ◽  
Bibliometric Analysis ◽  
Knowledge Development ◽  
Design For Additive Manufacturing ◽  
Global Knowledge ◽  
Content Type ◽  
Subject Areas ◽  
Manufacturing Knowledge ◽  
Bibliometric Approach

Purpose The purpose of this paper is to understand how Design for Additive manufacturing Knowledge has been developing and its significance to both academia and industry. Design/methodology/approach In this paper, the authors use a bibliometric approach to analyse publications from January 2010 to December 2020 to explore the subject areas, publication outlets, most active authors, geographical distribution of scholarly outputs, collaboration and co-citations at both institutional and geographical levels and outcomes from keywords analysis. Findings The findings reveal that most knowledge has been developed in DfAM methods, rules and guidelines. This may suggest that designers are trying to learn new ways of harnessing the freedom offered by AM. Furthermore, more knowledge is needed to understand how to tackle the inherent limitations of AM processes. Moreover, DfAM knowledge has thus far been developed mostly by authors in a small number of institutional and geographical clusters, potentially limiting diverse perspectives and synergies from international collaboration which are essential for global knowledge development, for improvement of the quality of DfAM research and for its wider dissemination. Originality/value A concise structure of DfAM knowledge areas upon which the bibliometric analysis was conducted has been developed. Furthermore, areas where research is concentrated and those that require further knowledge development are revealed.

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