How infrared cameras are increasing accuracy and insight in product testing
Infrared (IR) cameras may be best known for their use in military and security applications. Yet the same attributes that enable IR cameras to thrive in these environments – namely their ability to detect infrared radiation and form an image with it – make them ideal tools for product testing and product development in the medical devices field.
Once thought too expensive for common use in the product testing process, today’s IR cameras are more affordable than ever. They serve as a strategic testing tool that speeds product development by providing major data-gathering advantages over legacy approaches to temperature measurement. These advantages can lead to insights critical to preventing costly product failures and safety hazards, providing operations that use them with an immediate and ongoing return on investment.
As medical device manufacturers develop new products, the key temperature measures they must understand, track, and manage include: understanding the ways in which heat impacts medical device performance, identifying hot spots and temperature spikes, and knowing how, and the rate at which, materials in a device heat up and cool down. Yet even as the rate of innovation in the medical device field has increased, temperature measurement approaches have remained relatively unchanged.
Two Types of Temperature Measurement, One Common Problem
Two types of temperature measurement tools are prevalent today in medical device testing: probes and pyrometers. First, let’s consider probes, which include thermocouples, resistance temperature detectors (RTDs), and thermistors. The problem displaying a fast thermal transient. Measuring this rapid heating accurately in the design phase requires rapid temperature sampling and measurement. If you are using a single probe to make this measurement, you are missing something. You might miss the true maximum temperature to which the device has heated, or a temperature spike during the heating process. What’s worse, you won’t even know what you have missed, which effectively means you are basing product design and performance decisions on bad data. When you don’t sample fast enough, you don’t know what you don’t know.