1 / 19

Cost-Effective Designs for Robustness Applications in Multistage Processes

This research paper explores the use of post-fractionated strip-block designs to reduce experimentation costs and improve the quality of products and processes. The study includes a case study on battery cells and proposes a new arrangement for experimentation. The results show that this approach is a cost-effective method for gathering knowledge and improving performance.

hanchett
Download Presentation

Cost-Effective Designs for Robustness Applications in Multistage Processes

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. Post-Fractionated Strip-Block Designs: A Tool for Robustness Applications and Multistage Processes Carla A. Vivacqua vivacqua@cae.wisc.edu University of Wisconsin-Madison Federal University of Rio Grande do Norte-Brazil Søren Bisgaard University of Massachusetts-Amherst Harold J. Steudel University of Wisconsin-Madison

  2. Outline • Motivation • Research Question • Battery Cells Case Study • New Arrangement: Post-Fractionated Strip-Block Designs • Conclusions

  3. Motivation • Competitive environment requires: • Design of high-quality products and processes at low cost • Design of experiments (DOE) plays a critical role

  4. Research Question • How to reduce costs of experimentation? • Robust Design • Products insensitive to different sources of variation • Multistage Processes

  5. Battery Cells Case Study Begin Task 1 • Defective rate: 5% • Cause of cells rejection: high open circuit voltage (OCV) • Consequences of high OCV: self-discharging, leading to low performance or dead cells. Task 2 Assembly Process Task n Storage Process End

  6. Process Characteristics • Two shifts for production • One storage room • Storage cycle: at least five days • Six factors for investigation • Assembly process: A, B, C, D • Storage process: E, F

  7. Approach 1 • Completely randomized design • 26 = 64 independent trials • 64 changes in assembly configuration • Could not be run in one shift • 64 changes in storage conditions • Data collection: 64 * 5 = 320 days

  8. Approach 2 } 22 full factorial design • Advantages: • only 16 changes in the assembly configuration • only 4 changes in the storage configuration 24 full factorial design 16 trials

  9. Strip-Block Design

  10. Scenario • Space restrictions in storage room • Only 8 sub-lots can be placed in the storage room simultaneously

  11. State-of-the-Art Approach – Use of Fractional Factorials Generator: D = ABC Resolution IV design

  12. New Approach: Post-Fractionated Strip-Block Design Generator: EF = ABCD Resolution VI design

  13. Post-Fractionated Strip-Block Design (2) Generators: E = ABC, F = BCD Reduces to a split-plot design

  14. Maximum Post-Fractionation Order • Base strip-block design: 2k-p x 2q-r • Maximum value for post-fractionation order to preserve the strip-block structure: f = min(k-p, q-r) - 1. Ex.: 24 x 22 base design f = min(4, 2) – 1 = 2 – 1 = 1

  15. Analysis of Post-Fractionated Strip-Block Designs • Compute main effects and interactions • Not all effects with same precision • Group effects with same variance • Separate analyses for each stratum • Four different strata

  16. q-r = 2 basic generators of column design k-p = 4 basic generators of row design Remaining Contrasts Contrast Estimates f = 1 basic generator of post-fraction

  17. Variances

  18. Conclusions • Post-fractionated strip-block designs • Cost-effective method to gather knowledge about products and processes • Attention to conduct appropriate analysis

  19. Before vs. After Implementation New percentage of rejects  0.92% Improvement of 82%

More Related