Case Study

Pen Injector Design Study

Challenge

  • Pen injector design was outside of specification limits for user push force.
  • Several improvements to the design were being contemplated – e.g., lubricant-loaded plastics
    • ROI was indeterminant

KDPD Approach

  • Created analytical kinetic model for the pen injector
  • Verified the analytical model with customized test apparatus and a rigorous, systematic, test plan
    • Testing at subsystem level verified low level model inputs
      • Characterized critical subsystem values such as coefficients of friction
    • Tested pen at a system level
      • Analytical and empirical model were well aligned
  • The verified analytical model enabled a sensitivity study on independent variables including the coefficients of friction (COF) for each component
    • Allowed us to determine which components will benefit most from more costly lubricant loaded materials
    • Analytical model Informed client on impact of future design changes on device performance

Results

Plot above shows good correlation between high level model and experimental results
Variable Sensitivity Units
COF Component 1
0.018
N/σ
COF Component 2
0.019
N/σ
COF Component 3
0.002
N/σ
COF Component 4
0.001
N/σ
COF Component 5
0.010
N/σ
COF Component 6
0.007
N/σ
COF Component 7
0.113
N/σ
COF Component 8
0.213
N/σ
COF Component 9
0.013
N/σ

The results of the sensitivity study clearly show that the coefficient of friction for 2 components in particular have a significantly higher impact on the required user force.

  • Motivates higher cost materials where they are most impactful

Key Takeaways

  • By performing tests on the various subsystems of the device, FPrin was able to systematically verify the accuracy of the analytical model
  • The analytical model created by FPrin gave the client the ability to make informed design decisions for this device
    • Specifically, material choices and dimensional tolerances
      • The sensitivity study also gave insight into the impact on performance of the device to dimensional variation, thereby informing dimensional tolerance requirements
  • With parametric updates to the model the designer can effectively explore the design space of this architecture without the need for exploratory testing of each of them
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