1 / 19

P13552 Projected Image Prototyping System

P13552 Projected Image Prototyping System . Faculty Guide John Kaemmerlen Primary Customer Denis Cormier Michael DiRoma ISE (PM). Rachel Levine ME (Lead Engineer ) Sam Perry ME Amy Ryan ISE (Lead Programmer ) Kwadwo Opong Mensah EE. Project Description.

oberon
Download Presentation

P13552 Projected Image Prototyping System

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. P13552 Projected Image Prototyping System Faculty Guide John Kaemmerlen Primary Customer Denis Cormier Michael DiRoma ISE (PM) Rachel Levine ME (Lead Engineer) Sam Perry ME Amy Ryan ISE (Lead Programmer) KwadwoOpongMensah EE

  2. Project Description The primary objective of this project is to create a projected image 3D printer with the ability to produce a three-dimensional part.

  3. Customer Needs

  4. Engineering Specifications Specification Target Value • Build a 3D Part • Built with Photopolymer • Layer Thickness • Feature Resolution • Part Size Dimension • Software to control all aspects • Yes • Yes • 25 – 250 Microns • Able to report process capabilities • Max Size of 3” by 4” • Fully Automated

  5. Concept Summary • Photopolymer: Light sensitive resin that cures when light hits it • A black and white image or “slice” is a way to selectively cure the resin

  6. Black and white image from DLP projector

  7. System Architecture

  8. System Architecture • Physical Setup • Bath glass is covered with Teflon • Bath glass is secured between top and bottom bath assembly • Bath assembly is attached to the platform • Bath filled with resin • Build Platform lowered Completely • Platform is lowered using the jog buttons

  9. System Architecture • Inputs to LabView • STL file • Units (millimeters and inches) • Slice Thickness – 50 Microns (ideal conditions) • Base Speed – 1600 (ideal conditions) • Cure Time – 3 seconds (ideal conditions)

  10. System Architecture • Windows Command Line and Slicer • Information Gathered • 3D Object size (length, width, and height) • Functions Performed • Object sliced • Images background changed to black and core changed to white • Images saved to desktop

  11. System Architecture • LabView, the projector, and the Microcontroller • Steps to move determined by layer thickness • Build Platform moves up to far enough to separate from the Teflon then down to the appropriate distance • The image will project for the set cure time then it will display a black screen.

  12. Stepper Motor Lead Screw, Linear Guide Sandblasted Aluminum Build Platform Resin Bath and Teflon Film Jog Buttons

  13. Motion Assembly Motion Controller Macro Lens Optics DLP Projector

  14. Features • Sandblasted Aluminum Build Platform • Increased adhesion • Teflon Film • Decreased adhesion • Lead Screw • Anti-backlash nut • 12.7μm (0.0005”) resolution per step • Linear Guide • High loading support with low deflection • Motion Controller • Integration with software

  15. System Testing Results

  16. Project Evaluation Successes Failures • Multiple complete parts produced • System fully automated • Utilizing past teams materials (projector, case, formula, etc) • Inconsistent adhesion to build platform • Unable to use different photopolymers • Resolution of parts is low

  17. Recommendations • Improve Teflon Film • Permanent glass coating • Hard limit switches • Dedicated software • Encoder support • Test different photopolymer resin formulas • Clean up LabViewPrograming Code • Continue Detailed testing to determine optimal settings

  18. Lessons Learned • All risks can not be accounted for • Unidentified risks, external to the project • Overlooked sources of help • “Secret formula” found on past teams poster in week 8 of the second quarter • Better testing structure earlier in the quarter • Manipulate one variable at a time in a controlled manor

  19. Any Questions?

More Related