Manufacturing industries have been striving for years for a robust quality control solution which can reliably sort out bad parts of the dispensing process without causing much production downtime. What the industry craves is a robust 3D solution that provides a 360° view of the bead regardless of the dispensing direction.
It is impossible to overestimate the critical nature of keen and constant visual inspection in any production process. That’s why an advanced vision system is vital for any of today’s advanced production lines – as well as for “yesterday’s” older and more issue-prone lines.
Vision guided robotics (VGR) is an automation technology well-recognized for enabling greater flexibility and higher productivity in a diverse set of manufacturing tasks over a wide range of industries.
Part 1 of this three-part series examined how to identify characteristics of the object and the background you can use to create contrast with the illumination source for your machine vision application. This second part looks at how you go about choosing a light source to take advantage of the characteristics that create contrast.
The use of machine vision in industrial automation applications continues to increase as companies look for gains in productivity, efficiency and safety. Market forecasters estimate that the total market for machine vision will reach more than $18 billion by 2025, up from about $10 billion today.
Several critical components need to come together to form a machine vision system. This includes the sensor (typically within a camera) that captures a picture for inspection, the processing hardware (a PC or vision appliance) and software algorithms to render and communicate the results. In addition, lighting, staging, and lenses are required to set up a machine vision system.
Much of the latest news surrounding machine vision is about machine learning and the innovations regarding algorithms. But those algorithms need data to perform correctly. The data in this case is the images. It is imperative to capture the best image possible so that the algorithms can perform at their highest level.
Demands in productivity, efficiency, and quality continue to drive today’s industrial systems and processes. Machine vision, in its many forms and widely varying use cases is a key enabling technology in automated systems that can significantly improve manufacturing and services.