A New Take on Prototyping that Could Save You Millions. Doug Fankell, Ph.D.

Transcription

1 A New Take on Prototyping that Could Save You Millions Doug Fankell, Ph.D.

2 Introduction Founded in 1983 Over 250 highly technical staff located across the country Specialize in advanced non-linear mechanics and FEA of highly regulated industries Originally began in the nuclear power industry Have since expanded to several highly regulated industries that rely on advanced, predictive, non-linear FEA

3 OVERVIEW Background Cost of bringing a device to market Benefits of Using Predictive Computational Modeling Capabilities of Predictive Computational Modeling Examples Simulating Arterial Cutting Composite Tube to Artery Interaction Future of Computational Modeling

4 Background Cost of bringing a surgical device to market In 2010 it cost an average of $31 Million to take a 510(k) product and $94 Million to take a PMA product from concept to FDA clearance. Makeover et. al. 2010

5 Background Time to bring a product to market In 2010 it took an average of 31 months to take a 510(k) product and 54 months to take a PMA product from concept to FDA clearance. Makeover et. al. 2010

6 Background Estimated Costs of Delays for a 30 person company Sheldon et. al., 2014

7 How Advanced Predictive Computational Modeling Can Help Reduce time to market Speed up the design iteration process Foresee potential errors Reduce experimental testing Improve experimental testing and likelihood of FDA Approval Produce safer, more effective products faster

8 Predictive Modeling Until recently, modeling of medical devices meant simulating the device its self or the tissue itself. Hip Implant Colic and Sedmak, 2016 Pederson, 2006

9 Predictive Modeling Predictive modeling NOW not only models the medical device physics, but the physics within the biological tissue when acted upon by the device. Examples Deformation, stresses and strains in the biological tissue Fluid flow through the biological tissue Temperature in the tissue Chemical reactions in the tissue Tissue remodeling All or a combination of these things (Multiphysics) Fankell et. al. 2016

10 The Design Iteration Process NEED NEW NEEDS SIMULATION OF DESIGN PERFORMANCE NEW NEEDS IDEATION PROTOTYPE DESIGN MORE REFINEMENT BEST DESIGN TEST PROTOTYPE PRODUCE PROTOTYPE CLINICAL TESTING, FDA, ETC EXPERIMENTAL DESIGN INPUT, ADDITIONAL VERIFICATION/VALIDATION

11 Example I Arterial Cutting and Fusion Conmed Electrosurgery was interested in developing a model to inform and speed up the design process of their tissue fusion devices. Goals To develop a predictive FEA model using minimal experimental work, then use this model to inform device design. (Fankell et al., ABME, 2016)

12 Example I Arterial Cutting and Fusion Isochoric Strain Energy Temperature (Fankell et al., 2016)

13 Case Study Arterial Cutting and Fusion (Fankell et al., ABME, 2016)

14 Example I Arterial Cutting and Fusion These FEA Models were then used to iterate the jaw shape and control parameters Could run 10 s s of simulations in the time it took to produce 1 physical prototype Parametric studies of design inputs (applied pressures and temperatures) Up-front experimental V and V costs existed, but several of these tests were being conducted anyway. (Fankell et al., ABME, 2016)

15 Example II Tubes A client produces intravenous therapies to apply energy to arthroscopic plaque. Needs a composite tube casing around tooling to be stiff enough to be pushed through an artery with out crimping on the tools, but flexible enough to not damage the artery. It takes 6 to 8 weeks to produce a custom composite tube housing prototype. Including initial benchmarking of material (conducted in each physical iteration too), numerous design iterations can be conducted in the time it takes to produce 1 prototype.

16 Summary Predictive Computational Modeling Faster design iterations More iterations Additional insight into performance Better Designed Product/Prototypes Better designed testing Better device performance during testing Less testing needed Final Outcomes Better end product Product reaches market faster Less expensive in both time and resources

17 Future ASME V and V 40 - Verification and Validation in Computational Modeling of Medical Devices FDA The FDA also believes that computational modeling is poised to become a critical tool for accelerating regulatory decision making. Continued adoption will be essential for advancing FDA s mission. Morrison et.al., FDA, 2018 Advancements in cloud high performance computing (HPC), multiphysics modeling capabilities, and material characterization are paving the way for high fidelity modeling capabilities.

18 Questions?