We Take the Design & Testing of Medical Products Seriously. We build analytical and empirical models to support Knowledge Driven Product Development (KDPD). This allows us to identify and manage risk and travel a more linear path to devices that can, and WILL, work.
Medical Product Expertise
We derive satisfaction from developing technologies that help people to manage chronic disease states and live better lives.
Drug Delivery Devices
We appreciate the rigor required to develop technologies and products that must function at a high level – “kinda working”, or “most of them are good” is not good enough. Because many of these devices are produced at very high volume, and reimbursement is a consideration, cost is a major consideration. Therefore, anything other than cost-effective designs that make good use of materials, are readily assembled and offer high yields and high reliability is not good enough.
Point-of-Care Diagnostic Systems
We habitually verify simulations with simple tests. We stress the design in virtual simulation environments to start learning the weaknesses and opportunities. We focus on iterating virtually to explore the parameter space thoroughly and efficiently. This allows us to explore sensitivity to parametric/design input variation – because there are no perfect parts – and allows us to combine them into virtual devices and explore design capability and robustness in a way that is difficult or impossible without lots of time and money and building many many thousands of parts and assemblies.
Mechanical Engineers…being Mechanical Engineers
We are capable of contributing to a wide range of engineering challenges – precisely because we break things down to basics – from transmission line magnetic field models; a wide range of instrumentation and test challenges including characterization of active and passive thermal systems; thermodynamics and energy storage/release and suitability as the energy and power source for handheld devices; shape memory alloy wire actuators; materials engineering expertise; planar mechanism modeling and design; tribology; DFMA; simulation and test of dynamic systems, including creative high-speed instrumentation for acquisition of relevant information for fast (ms and faster) events; RF and antenna simulation and design. With our inhouse expertise or combined with our network of engineers, scientists, user-centric designers, and experienced regulated development experts we are flexible, efficient and cost effective at solving your most difficult challenges.
What Our Clients Say...
Model Based Design & Development
Our model based design and development process is the difference between a quirky system that works some of the time, and a system that is mature and highly reliable.
At FPrin we have extensive Model-Based Design experience. We apply tools like Mathcad and Matlab/Simulink/Simscape to system design challenges from a first-principles perspective, and sophisticated FEA tools for CFD, thermal analysis, and solid-mechanics for a closer look. Our modeling capabilities range from simple scoping models to evaluate component and sub-systems to full “digital twin”, system-level models that help to predict device performance. These models also provide insight into design capability and are valuable to guide the design team to robust design decisions. We are also expert in Monte Carlo simulations, with well-developed, standard, structures to feed the system model with randomly selected parametric ‘components’ parts. This allows our clients to gain a deeper understanding of the response of the system to variation in inputs – i.e., real-world manufactured parts – that will be encountered in manufacturing.
Together, these capabilities provide a level of understanding to our clients that makes the difference between quirky systems that work some of the time, and systems that robustly and consistently work. This is the difference between devices that WILL WORK when scaled to manufacturing versus ones that CAN WORK.FPrin Equipment List:
- Instron 34SC2 with 1 N and 10 kN load cells
- Formlabs Form3 SLA
- Prusa i3 MK3S 3D
- 5-Axis Pocket NC V2-10
- Bridgeport Series 1, 42” bed, Variable Speed Head, boring head, and other assorted tooling
- Engine Lathe 14” x 40” Gap Bed with collet closer and full set of 5C collets
- Alicat Flow meter
- Labjack DAQs
- 10 μsec measurement computing DAQ
- Pressure transducers (Stellartech), 4x up to 2000 psi
- Metrology equipment – gauge pins and blocks, micrometers, balances, etc.
- 7 Seats of Solidworks, 2 Premium with Premium simulation (non-linear)
- 5 seats of Matlab
- 1 Seat of Simulink and SimScape
We get useful data from both analysis and test, and our understanding is much more complete when we have both of those things. If they don’t agree we may have a flawed analytical model (perhaps with poor assumptions) or a flawed implementation in our test apparatus or misinterpretation of the data. (We also have good appreciation for the statistical validity of experimental outputs – “is that data point real?” – and whether apparent differences between test conditions are meaningful or not.) In either case we know we have to dig a bit deeper for real understanding – it’s only when we have good alignment between empirical and analytical outputs that we are comfortable saying “we get it”.
- With a good mental model – so that we know what to expect and when we see something different, we take notice immediately
- With test apparatus that are “qualified” so that we know a measured response is real
- A well-constructed test matrix with interleave, randomization and repeat conditions
- We typically start with low-level testing but can – and have – taken it all the way to formal DVT – plans, protocols, reports
- Rigorous data analysis, looking for main effects, interaction effects, sensitivity, statistical validity etc.
- Effective visualization tools to give us the insight into “what is really going on” – looking at the topography of the response curve or surface, cliffs, etc.
Design Analysis & Quality Assurance
ISO structures encourage us to “say what we intend to do, do what we intended, and document it”. We apply these disciplines in defining and guiding product development (“start with requirements”), designing an experiment (“what do I hope to learn?”), designing a test fixture – almost anything we do as engineers. “Know where you want to go before you start your journey".
We work using ISO-compliant methods so that we can readily provide content to our client’s DHF, and because we value the rigor that ISO requires.
Engineers today have many amazing tools at their disposal – CAD, FEA, etc. – to help in the design process. But we think that there is another class of tools that support good decision making starting with design architecture and with value all the way through DVT, transfer to manufacturing, scale-up, and IPQC. These are the analytical models and simulations that inform the engineer how to execute the design. These are tools that help us to understand why something should be stiffer or softer, why we place material in this location and not in another location, how much battery energy storage is needed, whether the design can deliver the power needed, how to dimension and tolerance parts to ensure functional capability, etc. We build these tools early in the process and use them to drive the design – they are the basis for our design decisions.