How Machine Vision Plays a Role in Industry 4.0

By Emily Lu, Marketing and Communication Manager, Hermary

Every industrial revolution in history has changed the world and the way we manufacture goods. The first industrial revolution introduced water and steam-powered machines that facilitated the mass production of goods. The use of electricity defined the second revolution by powering production lines and extending operating hours.

The development of electronics, computers and programmable logic controllers (PLCs) in the third industrial revolution gave rise to automated production lines and processes.

In the fourth industrial revolution, Industry 4.0, cyber-physical systems are optimizing manufacturing automation with enhanced data and machine learning.

As more businesses work to achieve higher productivity with better quality control, they’re also looking for ways to slash unscheduled downtime. Piecemeal technological investments will no longer be sufficient to thrive in Industry 4.0, also known as smart manufacturing or digital transformation. A network of connected machines with real-time monitoring is the key to sustainable success.

With digital transformation increasingly relying more on machine learning, machine vision technology can play a key role in capturing the physical world and transforming it into networked, digital data. This data can be analyzed and processed in real time to help improve productivity, maintain quality standards and reduce waste so manufacturers can boost operational efficiency in continuous improvement.

The Potential

Industrial firms report as much as 10 to 12% gains in areas such as manufacturing output, factory utilization and labor productivity after they invested in smart factory initiatives. Some industries already are using 3D machine vision to identify opportunities for improvement.

A 3D machine vision scanner uses lasers to map the surface of an object and digitize it into a point cloud — a set of data points in space — to represent the 3D copy of the object. This data stream is continually transmitted downstream for processing by a computer, a PLC or a programmable automation controller (PAC). The output from such a device further determines the actions taken next in the automation process.

Companies that adopt machine vision in robotic guidance often see an exponential increase in its productivity, efficiency and quality. This can both create new revenue streams for the robot OEM and additional value for their customers.

Machine Vision in Digital Transformation

3D machine vision serves several key roles in smart manufacturing operations.

1. Help Increase Competitiveness. In smart manufacturing, machines are connected to form an Industrial Internet of Things (IIoT) ecosystem. Integrating 3D machine vision with automation systems can improve the quality and quantity of data capture, making the smart factory a reality. This additional data is used in applications to verify quality, minimize waste and increase safety — all at a greater production speed.

2. Automate Quality Control. Delivering a consistently flawless product that meets clients’ requirements is crucial for the company’s reputation and financial success. In the age of Industry 4.0, quality control often can be enhanced by integrating machine vision to identify discrepancies in the products in real time.

Instead of waiting to manually test or examine batches of products after completion, the 3D scanner continuously monitors the products during production and prevents entire batches from being wasted as substandard.

Many industries are incorporating 3D machine vision for quality assurance roles. For example, in plants that manufacture goods with specific dimensions, such as paver blocks, lumber or tubing and pipes, 3D scanners can generate accurate real-time measurements of the product dimensions during each stage. Unacceptable variations in dimensions can trigger an immediate warning to the operator and shut down of the production line.

3. Increase Safety and Throughput. Traditionally, a production line has required human participation and judgement at critical steps. This can make consistent output and quality challenging to achieve, and sometimes, throughput can’t be improved without risking safety.