Case Studies

MODEL-BASED DESIGN:

CASE STUDY: Insertion of a Subcutaneous Sensor

Project Objective: Automate the insertion of a subcutaneous sensor to improve usability and acceptance. Modeling and test supported
the objective of ensuring reliable insertion of the needle and sensor as well as needle extraction without stalls and in
the prescribed time.

Empirical insertion and retraction velocity vs. position.

Modeled insertion and extraction velocity vs. position.

Monte Carlo simulation of 40K virtual units shows response to variation in inputs and demonstrates robust response to requirements. Usable as a design & reliability tool

Result & Benefit to the Client:   model provided objective evidence that the design was robust given the Client assurance as they scaled to production.

CASE STUDY: Needle Insertion Mechanism

  • Challenge
    • Client trapped in a Design Build Test….iterate loop.
      • “Test-to-success” mindset.
    • Required a more effective and predictable approach to develop a robust actuator for needle insertion.
    • Required drop-in solution, limited power available, complex load profile
  • KDPD Approach:
    • Empirically based analytical load profile with ±4-sigma limits
    • Research available motors, identify viable gear reduction ratios
    • Define key functional and testable requirements – “system curve”
      • τ, No Load Speed, stall force
    • Dynamic model of system
      • M-C simulation to predict robust performance
    • Developed tester and protocols for empirical verification.
  • Result:
    • Identified most robust configurations for implementation
    • Built and tested 10 units
    • Expected ~2x robustness increase
    • Measured 2 to 3x performance increase
  • Key Take-aways:
    • Analytical model informed by empirical results provided practical and cost-effective down-select process
    • Models predicted Performance!

Example : Positive-Displacement Wearable Pump

  • Challenge
    • Requirement for accurate small bolus delivery.
    • Understand key drivers to system accuracy in complex micro gear-train
  • KDPD Approach:
    • Model transmission accuracy
      • 1300:1, spur gears and worms/worm-wheels
    • Developed model and identified critical accuracy requirements based on kinematics
    • Identified critical gear inspection metrics
    • Verified that model represents measured performance
  • Result:
    • Redesigned final stage for better accuracy (metal vs. plastic)
    • Researched manufacturing methods for capability to meet required accuracy
    • Expected ~2x robustness increase
    • Measured 2 to 3x performance increase
  • Key Take-aways:
    • Client, realizing that they were pursuing a flawed architecture, was able to retire the project and cut losses
    • An upfront analytical approach would have saved $$$ and would have motivated team to look for alternative architectures.
      • Worms/worm wheels near motor could have been easily explored in model environment
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