Machine Design

IR vision: more than meets the eye

Machine-vision experts say infrared can spot defects better than ordinary cameras or the naked eye.

Samuel Sadoulet
Manager, Tucson Design Center
Edmund Optics Inc.
Tucson, Ariz.

Infrared detectors complement the visible-light spectrum of machine-vision equipment with images of heat sources located on or just below (about 0.001 in.) the surface of an object. Although the systems don't measure temperature directly, IR sensors primarily detect surface emissions such as absorbed energy that is reemitted. This lets IR systems discriminate features from these surface emissions based on material properties or subsurface structures.

One advantage of using the IR spectrum is that systems may operate without traditional light sources as do conventional machine-vision cameras. Moreover, IR imaging systems detect heat from both faulty and good parts on a powered-up, functioning unit. For example, an IC package sitting just below the surface of a keyboard emits a certain temperature signature when operating properly, and a different one when the IC is faulty. The resulting change in keyboard surface temperature is easily detectable with an IR system. In other applications, unique thermal patterns may indicate cracks in an object as thermal resistance to heat flow through or around the faults.

Simpler IR systems may replace complex machine-vision systems in some cases. For example, some visible-light systems detect surface-finish blemishes on reflective materials. But stray light and light reflections may wash out images or lower contrast. This can make it difficult for the algorithms to detect the defects. However, such defects produce a specific emission signature that an IR system can pick up without external illumination. The resulting amount of washout is minimal, which increases the signal-to-noise ratio, and the defect can be easily and quickly detected.

IR may be added to an existing system and share some or most of the major components, such as software and some mechanical components. In most cases, however, cameras and lenses will need upgrading to cover the added spectrum.

Key parameters to consider when selecting an IR system include field of view, spatial resolution, thermal resolution, spectral range, and availability. Field of view (FOV) is the viewable area of an object under inspection, or the portion of the object filling the camera sensor. Spatial resolution is a measure of an IR system's ability to reproduce object detail. This is typically specified in terms of number of pixels and pixel interpolation. Commercial sensors range from about 160 x 120 to 640 x 480 pixels. Thermal resolution measures how well a system distinguishes temperature changes within the FOV, analogous to dynamic range in visible systems. The spectral range includes wavelengths the system can detect without saturating. Sensors are categorized as near infrared (NIR), midwave infrared (MWIR), and long-wave infrared (LWIR), based on their spectral position in the range from 0.75 to 13.5 µm. Keep in mind the special IR lenses needed to detect the different wavelengths typically cost more than visible-light lenses and choices are fewer. Also, be sure to consider thermal noise. Some systems are sensitive to room temperature and any heat source in the environment, including the optics, metal housings, and the sensor itself.

Some applications for IR sensors

  • Monitoring the temperature gradient across a web of material before it reaches a spool
  • Thermal expansion and contraction of ceramic materials as they are being heated or cooled
  • Measuring liquid level in a steel tank
  • Imaging effects of temperature on different materials in an assembly
  • Electric motor heating
  • Monitoring the surface temperature of an open-top freezer

An infrared image of a motor housing indicates a uniform temperature while the end bell is cooler and the output shaft is hotter (near-white color).

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