Materials information matters for all medical devices because materials technology is fundamental to their performance and viability. For example, nitinol stents rely on the shape-memory capabilities of an advanced alloy to deploy to the vessel wall and maintain their shape over hundreds of millions of heartbeats. Other products such as filters, connectors and instruments rely on a combination of plastics, ceramics and alloys. Some device applications such as pump tubing and stent coatings are possible due to the properties of elastomers. Meanwhile, orthopedic implants often employ advanced composite materials.
The effective use of materials information is business critical. As shown in Exhibit 1, materials information is required not only for design decisions that have major consequences for product performance, but also for operational decisions in manufacturing, to support the sales process, in response to customer inquiries, for regulatory compliance and to enable informed purchasing. Yet, many organizations have failed to develop a systematic approach to managing materials information, which is resulting in increased exposure to risk, higher development costs and missed opportunities. For example, product engineers often lack access to the most accurate and up-to-date materials information. Having this sort of information can significantly mitigate the risk of a product recall when making design choices. However, instead of a certified source of materials information, they must rely on the knowledge of colleagues, piecing together data from spreadsheets, handbooks or other ad hoc methods. A designer may also use a material that they have used before, which immediately limits a new product design.
Exhibit 1: Materials information impacts not only design and R&D, but also many other segments of an organization.
A survey of our customers revealed that at least 75% of reworks or redesigns of medical devices that occur during product development are related to materials. Typical problems that were encountered include:
- Material type is identified, but no suitable grade is available
- Execution of the design identifies a problem that requires an alternative grade or a completely different material
- Supplier changes or other catastrophic events (pandemic) cause supply chain issues
- An alternative material is required to achieve environmental compliance
- Materials costs are too high
Solving these problems can be expensive, as materials are typically selected very early in the product development process. The decisions made regarding materials and processes can greatly affect how simulation, manufacturing and compliance are executed.
What does a materials intelligence approach that addresses these fundamental issues look like? It starts by considering the use, processing and compliance of a material at the earliest possible stage of product development and continuing this process through each subsequent stage. This article will review some of the more common issues that can occur in each stage of the device design process and discuss how materials intelligence can reduce and eliminate some of these problems.
A crucial place to make intelligent material decisions is in the earliest phases of device design. Indeed, studies find that 50% of the recalls are a result of design decisions from this phase of the device development process . In further research on product issues, it was found that 36% of recall actions for 510(k) approvals are found to be mechanical in nature.1 While we don’t know exactly how many of these problems are due to materials choices, it is our experience that poor materials selection is a major factor in mechanical issues of devices.
One common challenge is that designers don’t usually understand the full breadth of materials that may be acceptable for a given application. As a result, they use materials they are familiar with or ones that are contained within the tribal knowledge of their organization. But even familiar materials can rely upon materials information that is pieced together from online sources, handbooks and research papers, which lacks traceability. Additionally, this approach does not comply with FDA Guidance on Design Controls , which refers to the need to incorporate materials considerations into the design. Common problems that can occur when materials are not considered early in the design cycle include:
- Material costs are too high for a part or device
- Manufacturing issues require an alternative grade or different type of material
- The potential exists for a supply chain disruption
- Medical grade versions of a material are not available
The materials intelligence approach involves designing with compliance and manufacturing in mind. It starts with a general understanding of materials that relies on a cross-functional group of scientists and engineers. This knowledge must not only influence material choices but also filter down into the designer’s tools to aid in selecting materials that optimally fit their use cases (see Exhibit 2 for an example). It also involves assigning and running early compliance tests to foresee what sorts of issues may arise as a device moves into simulation and production. This approach reduces product release delays and enables preparedness when issues do arise.
Exhibit 2: A systematic optimization of materials data based on vetted sources stored in a materials selection tool (Ansys GRANTA Selector). In this example, the data has been filtered to include polymer-based material types and grades suitable for use in certain orthopedic devices. The axes are based on performance indices that provide a visual summary of the trade-off between volume and cost as they correspond to unit stiffness.
Simulation is critical for improving device performance and reducing the number of physical prototypes required to bring a product to market. However, justifying the materials used in the simulations is difficult as it requires verification of the material data and model parameters. It is also critical to have accurate knowledge and traceability of material properties for biological tissues, since a lack of knowledge here can result in problems with in vivo performance. Material data is often used without full knowledge of its origin or even traceability from the original source of testing. This is important since improperly calibrated material data can lead to poor (or misleading) simulation results regarding device performance. In the worst case, a recall can occur if the data is outside of the realm of validated material behavior.
One goal of taking a materials intelligence approach is to be able to automatically integrate material test data into a design model. This type of automation enables multiple material property models to be assessed on the same set of data, giving more robustness and insight into device performance. This knowledge grows as more testing is performed, acting to either verify materials that are already in use or to qualify materials for future projects. The test data can also be tied to existing resources for human biological materials such as the ASM Medical Materials Database, giving a level of understanding to designers and analysts that is critical for device reliability.
A standardized workflow can exist where simulations are conducted with updated material data as new materials are assigned and qualified. Using this approach, it is even possible to run “what if” analyses for material issues that could appear in the supply chain. This extends to using materials as a replacement for physical prototyping, as keeping the traceability of materials is critical when justifying simulation to regulatory bodies. At a minimum, a materials intelligence approach means providing access to verified reference data for your simulation community. Data gathering activities are expensive, but a supply of vetted reference data improves the quality of simulation and allows experts to focus on testing.
Materials and process problems during manufacturing are often difficult to overcome and can result in significant delays in product launch dates. Manufacturing can be prolonged, and redesigns may be needed if materials and processes are not assessed thoroughly during design. For example, tolerance issues with a device can be traced back to a lack of understanding regarding how and why the requirements for each material were established. One reason is that the modifications required to effectively manufacture parts and devices can require expertise in both the manufacturing processes themselves as well as material tolerances. Maintaining a materials intelligence approach in manufacturing allows for the knowledge gained in all other aspects of the product development process to influence manufacturing decisions, including:
- Assessing measurements of as-received materials for use in processing compared to the values used in simulation
- Modifying processing variables to adjust necessary tolerances
- Observing trends in production materials over time and then modifying simulations accordingly
- Dynamically influencing statistical process controls
By assigning materials during initial device design that are traceable throughout the entire supply chain, it is also possible to assess a bill of materials and identify potential production issues before prototypes are developed. This has the additional benefit of enabling team members with expertise in materials and processing to share their knowledge as early as possible with others in the organization. By taking a materials intelligence approach and managing the traceability of these choices, organizations can reduce the time required to both make prototypes and transfer new products to commercial manufacturing.
The risk of product recalls due to noncompliance with regulatory requirements are by their very nature difficult to manage and mitigate. Frequently, these ideas are fully considered too far along in the design and development process, which can result in substantial additional costs to modify a design. This type of delay is somewhat understandable as the sheer breadth of applicable legislations can be enormous when developing a device that is intended to be sold in multiple geographies. This is because each agency has established its own restrictions regarding the presence of specific substances in the device and what materials the device is exposed to during manufacturing. For instance, as part of the EU Medical Devices Regulation, the use of substances in the Carcinogenic, Mutagenic and Toxic for Reproduction (CMR) list is restricted, particularly in certain use conditions like contact with the human body. Although essential, identification and quantification of these substances can be a significant challenge, especially in a fast-changing regulatory environment. Moreover, directives like ISO 109933 or USP Class VI , which specifically apply to the testing and certification of materials, need to be considered, and internal initiatives like Six Sigma  or ISO 13485 can add compliance hurdles that are difficult to anticipate.
A materials intelligence approach for compliance, therefore, tries to mitigate risk in the earliest stages of design. One way to accomplish this is by limiting materials choices to a vetted database of materials. Another approach is to have a managed materials database that is linked to the material choices that product engineers make. Their initial designs can then be analyzed from a materials perspective and potential compliance issues identified. These two approaches can also be combined to allow for compliance to influence all aspects of the device design process.
HOW TO STREAMLINE PRODUCT DEVELOPMENT
The essence of a materials intelligence approach is to consider how choices made early in the design process will affect later development stages, e.g., manufacturing and regulatory compliance. When correctly implemented, materials intelligence involves maintaining traceability to tested and vetted data that is visible to all stakeholders. Ansys GRANTA MI was designed to enable this approach and works by creating a unified database that is accessible to everyone who needs and uses materials information. It enables members of the design team to access relevant data either within their tools or through a dedicated web portal. Well-developed solutions for importing test data, conducting and approving analysis, and understanding compliance on materials and product flow easily to CAD, CAE, CAM and PLM software to build traceability and knowledge across organizations pursuing digital transformation (see Exhibit 3).
Exhibit 3: Computer-aided design (CAD), computer-aided engineering (CAE) and computer-aided manufacturing (CAM) have led the way as medical device manufacturers proceed along their digital transformation journey. Companies now realize that they must also consider materials intelligence as critical due to its impact on all aspects of their product for a true digital transformation to occur.
By working to understand how materials move through each aspect of the product development process, device developers can significantly shorten time-to-market, reduce the risk of compliance issues, fine-tune manufacturing processes and improve overall device quality. Device companies have seen processes that normally take 18 months reduced to weeks or days, resulting in significant savings per annum. The materials intelligence approach is also necessary to facilitate digital transformation and to let design teams build quality products.
1. Villarraga, M.L., Guerrin, H.L., Wood, J.M. Five Year Review of Class I Medical Device Recalls: 2004-2008. 2009, Vol. 64, 4, pp. 663-676.
2. U.S. Food and Drugs Administration. Design Control Guidance for Medical Device Manufacturers. FDA website. [Online] 1997.
3. U.S. Pharmacopeia Standards. USP Class VI Standard. [Online]
4. Motorola, Inc. What is Six Sigma? [Online]
Ben Conlon, M.S., is an Application Engineer at Ansys specializing in materials. Before joining Granta Design in 2019 (acq. Ansys 2019), Mr. Conlon had experience in materials engineering, with a focus on semiconductor processing. He led teams on developing sensors and memory devices using vapor deposition techniques as well as processing 2D materials. He works with customers in adopting a materials intelligence approach to their business.
A special contribution is due to Kristen Roenigk (Ansys - Granta) for editing and formulating this article.