The successful application of machine vision technology involves an intricately and carefully balanced mix of a variety of elements.

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.

Where rule-based machine vision has not been attempted or has reached its limits, there is a high potential for deep learning algorithms to support employees and drive forward automation.

At its simplest, automation means to make something automatic. In manufacturing, whether describing a single device or an entire system or process, automation refers to performing one or many tasks autonomously with minimal or even no human interaction in a manufacturing or production environment.

The renewed interest for vision guided robotics (VGR) for the manufacturing of parts and packaging of goods is much in part to the number of advances in sophisticated technologies over the last decade. 

Manufacturing often involves the fabrication of products that are made up of multiple smaller parts or components. Assembling these parts into finished products can be complex and labor intensive. 

As developments in machine learning and the Internet of Things (IoT) impact how manufacturers run their businesses, automation can support these changes and boost productivity.

Manufacturing is becoming automated on a broad scale. The technology enables manufacturers to affordably boost their throughput, improve quality and become nimbler as they respond to customer demands. Ultimately, this helps them become more efficient.

Robots in manufacturing is not a new idea. But today an increasing number of collaborative robots are joining the ranks, working alongside their human coworkers.