How Machine Vision Is Revolutionizing Manufacturing

Summary

Manufacturers increasingly turn to machine vision systems to automate critical processes, improve product quality and gain insight into operations. Machine vision involves using cameras, sensors and image processing software to provide visual input and analysis for automated machines. As manufacturing processes become more complex, machine vision is emerging as a critical technology to drive the efficiency gains of Industry 4.0 (Figure 1).

Seeing is believing

Traditionally, manufacturing has relied on human operators to visually inspect products for defects and anomalies. But human inspection has limitations in terms of accuracy, repeatability and throughput. Machine vision offers a more objective and consistent “point of view” for automated inspection and measurement tasks.

Cameras and image sensors capture visual information by detecting light intensities and converting them into digital images. The images are then processed by software to identify objects, read text, detect abnormalities and perform dimensional measurements. Unlike humans, machine vision systems can inspect products at incredibly high speeds with perfect objectivity and consistency. This enables manufacturers to catch defects earlier in the process, which avoids expensive rework and increases first-pass yields.
 

Manufacturing applications

Machine vision systems are used across industries for a variety of purposes, which includes quality inspection, guidance and alignment, identification and classification, dimensional measurement and process monitoring.
 

• Quality inspection: Machine vision compares digital images of products against reference images to detect imperfections in areas such as shape, color, finish or labeling. This helps manufacturers eliminate defective products before they reach customers.
• Guidance and alignment: Cameras provide visual input for robot guidance systems and automated guided vehicles (AGVs), which enables them to correctly pick up, assemble and transport parts.
• Identification and classification: Object recognition software can read barcodes, character strings and 2-D codes to identify products for traceability and inventory management.
• Dimensional measurement: Machine vision performs noncontact parts measurement to ensure dimensional accuracy and specifications are met. This allows manufacturers to optimize processes and minimize waste.
• Process monitoring: Monitoring machines and automated lines with cameras can detect operational issues in real time, which reduces unplanned downtime and increases production efficiency.

By leveraging machine vision systems in these ways, manufacturers can achieve higher throughput, implement more flexible production processes and reduce the need for manual labor (Figure 2).

Enabling technologies

Several trends—higher resolution, speed and lower costs; artificial intelligence and deep learning; IoT integration; and software automation—are driving the increasing adoption of machine vision in manufacturing.
 

• Higher resolution, speed and lower costs: Advances in camera and sensor technologies have improved image resolution, frame rates and processing speeds while driving down costs. This has made machine vision feasible for a broader range of applications.
• Artificial intelligence (AI) and deep learning: Technologies such as neural networks and computer vision enable machine vision systems to detect complex defects, read difficult text and accurately identify objects (Figure 3).
• Internet of Things (IoT) integration: As manufacturing equipment and operations connect, machine vision data is increasingly incorporated into Industry 4.0 strategies by linking with IoT platforms, cloud computing and analytics tools.
• Software automation: New software tools provide an intuitive interface that helps machine vision specialists automate setup, configuration and testing with less manual programming. This decreases the time to deployment and makes systems easier to integrate, maintain and upgrade.

The future of machine vision

Machine vision is already experiencing phenomenal growth that will likely continue and increase. Following are some notable statistics from A3 [Association for Advancing Automation]:

• In the second quarter of 2021, the North American machine vision market expanded by 26% over the same period a year before, growing to $764 million.
• From January to June of 2021, the North America market grew by 18% to $1.5 billion.
• By 2022, the global market for machine vision was estimated at $9.01 billion.
• By 2023, that figure increased to $9.68 billion.
• By 2030 the global machine vision market is projected to rise to $16.82 billion.

As technologies like AI, cloud computing and 3-D vision evolve, machine vision will likely play an expanding role in areas such as augmented reality (AR) maintenance, predictive analytics and adaptive quality control. Future possibilities include real-time defect prediction and removal, digital twin integration and 3-D vision for advanced inspection.

• Real-time defect prediction and removal: Using historical defect data, vision systems may be able to predict future defects and automatically reconfigure production parameters to minimize their occurrence.
• Digital twin integration: Monitoring physical systems with machine vision will enable digital replicas of those systems to become more accurate representations for simulation and testing.
• 3-D vision for advanced inspection: 3-D machine vision could provide a more comprehensive inspection of part geometry, which could enable the detection of defects that 2-D vision cannot see.

Machine vision represents a core technology for automation, data collection and optimization in Industry 4.0. As manufacturers seek to increase agility, reduce costs and produce higher-quality products, machine vision systems will continue transforming processes on factory floors worldwide.

_{All figures courtesy of ControlTek.}