Here we’ll examine challenges that the measurement environment imposes on machine vision and give approaches to mitigate the effects and retain much of the high-accuracy capability.
The first part of this three-part series covered the principles that allow machine vision to make high-accuracy measurements. This second part examines challenges that the measurement environment imposes on machine vision and gives approaches to mitigate the effects and retain much of the high-accuracy capability.
The techniques allowing high-precision measurements are well understood and based on solid principles. Calibration is critical to accurate measurements.
Machine vision can measure with greater precision and accuracy than human vision. This series starts by exploring techniques for high-precision measurements in vision systems. The next installments will examine challenges and solutions for maintaining this precision and accuracy. First, we'll clarify key terms related to measurement accuracy.
Machine vision projects often face challenges such as slow progress, difficulty in getting quotes, cost overruns, and unreliable operation. These issues require recognizing and adapting to the unique nature of machine vision projects compared to other types of projects.
The CL Series, available now for hands-on demos and orders, and other additions to the company’s extensive robotics portfolio give manufacturers flexibility and advanced capabilities to bring automation to a wide range of new applications and markets.
Selecting the correct LED lighting for machine vision can be challenging, even for experienced professionals. While red LEDs are commonly used, blue LEDs can be a superior choice for certain applications.
At Technical University of Applied Sciences Würzburg-Schweinfurt (THWS), Professor Christian Zirkelbach is teaching robotics and machine vision at Faculty of Applied Natural Sciences and Humanities.