The development of industrial automation in China has reached a high level of maturity, and most people have some understanding of it. To deepen your knowledge of industrial automation, this article will explore two key aspects: 1. The application of machine vision in industrial automation, and 2. Why industrial automation utilizes machine vision systems. If you find the upcoming content intriguing, feel free to continue reading.
I. Applications of Machine Vision in Industrial Automation
Machine vision has long been integrated into industrial automation systems to enhance production quality and output by replacing traditional manual inspection. From pick-and-place operations and object tracking to metrology and defect detection, leveraging visual data improves overall system performance by providing straightforward pass/fail feedback or enabling closed-loop control loops.
The use of vision extends far beyond industrial automation; we also see cameras extensively applied in daily life, such as in computers, mobile devices, and especially in automobiles. Cameras were only introduced into vehicles a few years ago, yet today cars are equipped with numerous cameras to provide drivers with a complete 360° view of the vehicle.
However, the most significant technological advancement in machine vision has arguably been processing power. With processor performance doubling every two years and ongoing focus on parallel processing technologies like multi-core CPUs, GPUs, and FPGAs, vision system designers can now apply highly complex algorithms to visual data, creating smarter systems.
Advancements in processing technology open new opportunities beyond merely smarter or more powerful algorithms. Let's examine application cases for adding vision capabilities to manufacturing machines. These systems are traditionally designed as networks of intelligent subsystems forming collaborative distributed systems, enabling modular design.
However, as system performance improves, adopting this hardware-centric approach may encounter difficulties, as these systems typically employ a mix of time-critical and non-time-critical protocols for interconnection. Connecting these disparate systems through various communication protocols can lead to bottlenecks in latency, determinism, and throughput.
For instance, if designers attempt to develop applications using this distributed architecture while maintaining tight integration between vision and motion systems-as required in vision servo applications-they may encounter significant performance challenges due to insufficient processing capacity. Furthermore, the fact that each subsystem has its own controller actually reduces processing efficiency.
Finally, this hardware-centric distributed approach forces designers to use different tools for each subsystem: specialized vision software for the vision system, motion-specific software for the motion system, and so on. This poses particular challenges for smaller design teams, where a single engineer may be responsible for multiple components.
II. Why Machine Vision Systems Are Used in Industrial Automation
There are five primary reasons for employing machine vision systems in industrial automation control:
Precision - Due to the physical limitations of the human eye, machines hold a distinct advantage in precision. Even when humans rely on magnifying glasses or microscopes for product inspection, machines remain more accurate, achieving precision down to one-thousandth of an inch.
Repeatability - Machines can perform inspections repeatedly using identical methods without fatigue. In contrast, human eyes exhibit subtle variations in each inspection, even when examining identical products.
Speed - Machines inspect products faster. This is particularly advantageous when detecting high-speed moving objects, such as on production lines, where they enhance manufacturing efficiency.
Objectivity-Human inspection suffers from a critical flaw: emotional bias. Results fluctuate with an inspector's mood, whereas machines operate without human emotion, yielding consistently reliable outcomes.
Cost-Machines work faster than humans, meaning a single automated inspection unit can handle the workload of multiple workers. Additionally, machines require no breaks, never get sick, and can operate continuously, significantly boosting production efficiency.
Machine vision systems rapidly capture vast amounts of data, facilitate automated processing, and integrate seamlessly with design specifications and manufacturing controls. Consequently, they are widely deployed in modern automated production for process monitoring, finished product inspection, and quality control. Machine vision systems enhance production flexibility and automation levels. They commonly replace human vision in hazardous environments unsuitable for manual work or where human visual capabilities fall short. In high-volume industrial production, manual visual inspection proves inefficient and imprecise, whereas machine vision inspection methods substantially boost productivity and automation. Furthermore, machine vision facilitates seamless information integration, serving as a foundational technology for computer-integrated manufacturing.