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 RHI200-DO Lightgistics series light delivers all the advantages of strobing without the disturbance and disorientation often linked to LED strobe lights.
The Gocator 4000 smart 3D coaxial line confocal sensor is ideal for inspection applications in various industries, including semiconductor, consumer electronics and EV battery.
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.
No robot can replace a person, but they can handle dull, dangerous, or repetitive tasks, freeing up employees for more fulfilling work. Businesses need to embrace robotics to efficiently adapt to production cycles and address supply chain backlogs and labor shortages.
In battery cell production, maintaining high quality and reducing material waste is crucial. Digital image processing and machine vision solutions enable reliable defect detection, ensuring the production of safe, high-quality battery cells for electric mobility.