| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | July 31, 2015 |
| Latest Amendment Date: | July 31, 2015 |
| Award Number: | 1546985 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Bruce Kramer
CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | September 1, 2015 |
| End Date: | August 31, 2017 (Estimated) |
| Total Intended Award Amount: | $99,976.00 |
| Total Awarded Amount to Date: | $99,976.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
300 TURNER ST NW BLACKSBURG VA US 24060-3359 (540)231-5281 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
635 Prices Fork Road Blacksburg VA US 24061-0001 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | ENG IDR-Eng Interdisciplin Res |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
Additive manufacturing, in which physical products are fabricated in a layer-by-layer fashion, is creating renewed interest in manufacturing. Additive manufacturing offers numerous advantages over traditional manufacturing processes, such as machining, casting, and forging, but it is not always the most cost effective method for fabricating parts. In many cases, subtractive manufacturing techniques (i.e., fabrication processes like machining that remove material to create a finished part) are more cost-effective, but they require additional expertise and set-up compared to the "push button" technology available on many additive manufacturing systems. In order to further improve accessibility to all digital fabrication technologies, and to better inform "makers" of the manufacturability of their designs, the team of this EArly-concept Grant for Exploratory Research (EAGER) project will create a set of design tools and software applications that provide rapid manufacturing feedback in terms of geometry, material, quality, lead time, and cost to fabricate a component. The cybermanufacturing system will be integrated into undergraduate mechanical engineering design studios at three partner institutions (Georgia Tech, Penn State University, and Virginia Tech) and will be formally assessed in order to answer the research question, "How does this cybermanufacturing system and resulting design assessment capability enable innovation?" This work will lay the foundation for future, larger-scale cybermanufacturing efforts that will connect engineering students and the broader maker community with a larger array of manufacturing capabilities and know-how, making them better prepared for the engineering and entrepreneurial workforce of tomorrow.
The project's cloud-based cybermanufacturing system will be comprised of applications (apps) for additive and subtractive manufacturing that will provide an integrated view of the semantic information needed to assess manufacturability of part designs. The cybermanufacturing system leverages state-of-the-art advances in tool path planning for multi-axis machine tools based on novel voxel-based flexible data representations for product geometry, which will further the flexibility and accessibility of subtractive manufacturing. The resulting system will be deployed in the partner institutions' vibrant, yet distinct, design studio environments that offer hands-on design/build experiences to several hundred undergraduate engineering students each year. The impact of the system on the design process will be measured directly using novel assessment instruments designed to probe salient aspects of innovation in product design and their impact on the students engaged. This project's approach will inform requirements for mapping more expansive networks and manufacturing capabilities in future cybermanufacturing service systems.
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Low-cost digital fabrication technologies such as Additive Manufacturing (AM, also referred to as 3D printing) and CNC machining are sparking a renewed interest in manufacturing and, as a result, entrepreneurship. However, the choice of which technology to use for a given product is challenging given the tradeoffs among the many fabrication options. In order to further improve accessibility to all digital fabrication technologies, and to better inform “makers” of the manufacturability of their designs, the team created a cloud-based cybermanufacturing system comprised of applications for additive and subtractive manufacturing that provided an integrated view of the semantic information needed to assess manufacturability of part designs. These “apps” provide a user rapid manufacturing feedback in terms of geometry, material, quality, lead time, and cost to fabricate a component.
As part of the collaborative research team, Virginia Tech led the creation of an algorithm for automated manufacturability analysis of parts to be manufacturing using AM. While AM processes provide unparalleled design freedom, they still impose some constraints on the geometries that can be successfully fabricated. Existing algorithms based on surface representations require several computationally intensive manipulations to predict a geometry’s manufacturability. Using a voxel-based representation, the research team created a software application that analyzes provided part files and alerts a user to violations of minimum feature size constraint, internal enclosed voids, and suggests a preferred print orientation based on build time and support material consumption estimations. Essentially a three-dimensional pixel, a voxel is a base feature for volumetric representation of part models, which can be used to simplify part manufacturability analysis and thus reduce the computational cost involved in performing similar operations via traditional polygonal surface models. This is of merit as existing AM software applications are focused only in checking the validity of the STL file and performing necessary repairs; they are rarely helpful for guiding a user in making manufacturability decisions.
The cybermanufacturing system was integrated into undergraduate mechanical engineering design courses at the three partner institutions and assessed for its impact on students’ understanding of (i) factors that affect the manufacturability of a given part for both Additive and Subtractive manufacturing and (ii) how to appropriately choose between them. The cybermanufacturing system was deployed in the curricula to support new educational Design for Manufacturing (DfM) modules and to support students’ design project efforts.
The DfM module was implemented in a sophomore Mechanical Engineering Design course at Virginia Tech over two semesters (~400 students total). From pre- and post-assessment, it was discovered that:
- The DfM instruction had a statistically significant effect on students’ understanding of manufacturing selection and part redesign for maunfacturability.
- Prior experience with machining significantly affected students’ understanding of manufacturability principles, whereas prior experience with 3D Printing did not. This indicates that, although currently students are utilizing 3D printing technology available to them, they do not clearly understand the geometric considerations and manufacturability constraints in 3D printing technology.
- The use of the DfM software can positively affect design decisions (i.e., students are less likely to specify small features and inefficient print orientations when using the software).
For engineering students, access to and familiarity with these manufacturer-centric applications presents a significant barrier toward implementation of complex features in product design. The DfM educational modlues and the automated manufacturability analysis cybermanufacturing tools created in this project have successfully (i) increased students’ awareness of manufacturing capabilities of various available machines, (ii) increased their ability to select appropriate manufacturing technologies for a given design, and (iii) reduced the number of iterations needed to specify a manufacturable design.
Last Modified: 02/14/2018
Modified by: Christopher B Williams
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