Terry Arden

Integrating robots into a manufacturing line challenges process control engineers to rethink part flow and learn how both robot and 3D sensors can work together to achieve faster, more efficient production.

When it comes to building cost-effective 3D vision systems, is it better to use a component-based (i.e., camera, laser, lens, brackets, calibration targets) or all-in-one (i.e., smart) approach? 

The Industrial Internet of Things (IIoT) is a term used to describe interconnectivity in relation to factory automation. The term captures the complex interrelationship of smart hardware feeding actionable data over a network, in order to drive deep learning systems toward optimized factory output. 

In industries like consumer electronics, battery, and solar, the race for ever faster scanning, measurement, and control is critical to delivering 100% inspection of small parts moving at production speed.

In the early 2000 era, companies were happy just to have a website. Then the emergence of cloud-based applications driven by web browser technologies brought about SAAS (software as a service), and the business environment underwent a paradigm shift toward digital infrastructure—one that could improve production and increase profit.

The data handling of many sensors that scan a single target, called multi-sensor networking, is an important capability to support large object applications.

Here are two application examples where the combination of 2D and 3D provide results that cannot be achieved by either technology on its own.

3D builds on the proven capabilities of 2D by adding a secondary layer of data describing shape, which is essential to designing highly robust measurement systems.

In the world of automated industrial quality control and inline inspection, both 2D and 3D technology have important roles to play. In this three part blog series we will look at the strengths and limitations of each, and how combining the two creates a more complete inspection system.