Using Probabilistic Computational Durability Modeling and Simulation to Create a Virtual Design of Experiments Based on Limited Laboratory Tests

Abstract

Laboratory testing is essential in understanding the fatigue durability limits of a medical device. Only a limited number of tests, however, are feasible considering the time and cost of manufacturing and testing the device. Many geometries and materials are available as design alternatives and testing is typically constrained to a limited number of devices. Additionally, physical test method capability typically has limitations and uncertainty for such breath of design alternatives. The time and cost is greatly magnified if the designs do not meet the durability test requirements. This leads to an important question, “How do we combine results from the laboratory testing of similar designs and similar materials with state-of-the-art computational methods to create the best possible understanding of the design space?” The Virtual Life Management® (VLM) methodology accomplishes this by creating a Virtual Design of Experiment: a computational method that combines structural analysis (FEA) data, material characterization models and probabilistic methods.For a specific Context of Use, using a computational approach, a) the structural analysis (FEA) models are used to understand the effect of the boundary conditions and loads on the stresses, stress gradients and stressed volumes, b) the material models are used to understand the damage induced by the stresses, and c) probabilistic and Monte Carlo methods are used to extend the design of experiment to high reliability levels and understand the design space at the statistical tails of the durability distribution i.e., Minimum Device Lifetime. The sensitivity of the minimum lifetime to the design parameter will be different than the sensitive at nominal lifetimes. Only by predicting the tails of the distribution can we quantify the durability of the design in the marketplace. This process is pictorially depicted in Fig. 1.Boston Scientific has been working with VEXTEC to create probabilistic microstructural material models for computational strength prediction and then combining these with structural analysis (FEA) and Virtual Life Management to predict risk i.e., probability of failure of a device. Two specific devices and Contexts of Use include: a) pacemaker leads made of MP35N material subject to two amplitudes of bending fatigue and b) endoscopic airway stent made of Nitinol material subject to a crushing load to represent stent deformation depicting a cough. For the airway stent effort, VLM was able to estimate the fatigue lifetime of stent design before stent is manufactured and tested. The simulation was able to show difference in average fatigue lifetime for two Nitinol alloy lots with different inclusion contents (and both complied with the specification (ASTM2063-05)). This was confirmed by the few (∼10) experiments completed. Significantly, VLM was able to predict a large difference for the −3σ fatigue lifetime for the two flavors of Nitinol. (Experimentally this would have required a large number of tests.) For the pacemaker leads project, VLM was able to characterize the lead design as a function of material microstructure (grain size, inclusion size and density) while using residual stress as a calibrating parameter (shown in Fig. 2). This study was able to demonstrate VLM's ability to iterate with material substitution, vendor management, sensitivity analysis, design trade studies and design optimization. To summarize, merging material science with computational methods opens a whole new level of engineering capabilities specifically uncertainty quantification and probabilistic distributions associated with microstructural characterization; a key step in supporting the verification and validation framework being developed by the ASME V&V 40 standard for medical devices.Although the work to date is early stage, the favorable results have provided confidence in the methodology and toolset; and insight to the potential application in the formal medical device product development process.