Inspection systems are solutions that complement a production line. A simple connection of a camera to a computer can bring many benefits, from accelerating the production process and increasing the quality of manufactured products to savings made due to greater system reliability. Machine vision allows for approaching existing problems from a completely different perspective and significantly simplifying many processes.
Industry is one of the biggest beneficiaries of vision technology.
The biggest industrial revolution was the introduction of the first production line in 1917. Since then, the trend of automation and acceleration of the entire production process has significantly intensified. Each year, new centers emerged worldwide, increasingly basing their operations on modern technology. In the last dozen or so years, vision systems have contributed to the further development of automation. Machine vision has made it possible to become even more independent of the human factor and thus optimize many a production line. Initially, a major barrier was the limitations of the vision components used, especially cameras. The development of sensors and digital data acquisition allowed for greater flexibility and opened up new areas for machine vision applications.
The increasing availability of ready-made solutions is particularly helpful when implementing vision technology in industry. Examples include smart cameras, which integrate a camera, an illuminator, and a control computer. Such a solution is extremely compact and flexible, making it easy to quickly integrate into existing production lines as well as newly created setups. For more complex problems, where off-the-shelf solutions cannot provide sufficient results, “PC-based” systems are built. These types of vision systems consist of: a separate camera with a lens, a computer, an illuminator, and, if needed, other additional elements such as a frame grabber, housing, etc.
An undeniable advantage of vision systems, which significantly contributes to their popularity in many industrial applications, is that measurements are taken non-contact. This eliminates concerns about the sensors’ impact on the examined element, which is a factor in contact measurements. Moreover, vision systems can perform the same tasks faster than contact methods.
Machine or Human?
For decades, the goal has been to automate the production process as much as possible and eliminate the human factor, which is significantly more prone to errors and cannot always ensure repeatable results. Another aspect favoring machines is their operating speed. Automatic production lines can not only perform a given operation faster but also maintain that speed 24 hours a day, seven days a week. They don’t experience fatigue from repetitive actions. Vision systems naturally play a large role in this state of affairs, forming a complementary element of many production lines. Machine vision serves both a control function and forms the basis of many maintenance systems. Thanks to it, for example, when inspecting surfaces, there’s no need to rely on subjective human assessment, and the entire process can take place automatically in just a fraction of a second. Humans, on the other hand, can serve a supervisory role over automated systems and ensure that production runs smoothly. Another important role that machines cannot perform is all kinds of service work. All elements of the production line must be maintained in the best possible condition to guarantee long, uninterrupted operation.
Industry 4.0
Machine vision fits perfectly into the idea of Industry 4.0. This is the next evolutionary step after the automation of the manufacturing process. Industry 4.0 assumes full integration of cooperating machines using internet standards, the application of advanced self-learning algorithms, and fully automatic visual inspection. Although the term itself is still relatively new, there’s growing interest in new solutions aimed at production and industrial centers. Industry 4.0 aims to create an Internet of Things within the factory, where advanced systems control processes and make decisions in real-time.
Deep Learning
Deep learning technology is the latest trend in computer vision systems, which can bring particular benefits in, among other areas, industrial applications. Deep learning is a general term for self-learning algorithms where the computer performs tasks natural to the human brain. An example application could be classifying good and bad objects. Traditionally, creating a system for such a task would require writing an algorithm containing instructions on how to recognize defects. With deep learning, you only need to provide the program with images of good and bad objects, and the system will autonomously recognize and learn the features by which to classify. This approach not only saves time but also opens the door to new applications previously impossible to implement.
Applications
In industry, the rule is: there are no places where production automation wouldn’t pay off. If it doesn’t, it’s only due to poorly planned integration. The same applies to machine vision. The places where vision systems can prove effective are countless. From control applications to maintenance systems, and finally to autonomous devices primarily based on machine vision.
Machine vision is indispensable for the rapid analysis of highly precise electronic components. Smooth inspection wouldn’t be possible if done by a human. An additional difficulty is that these components are usually very small, which practically makes the inspection of such objects dependent on vision systems.
A camera system works great, for example, when checking the completeness of an object. This operation is particularly useful for all types of loading, whether it’s packaging products into cartons or stacking them on a pallet. If an incomplete package is detected, the computer is automatically notified and decides what actions to take. Another task ideal for machine vision is all types of measurement inspections and position control of objects. “Bin picking” systems, for instance, rely on the latter, allowing a robot arm to pick randomly arranged items from a container without prior organization.
Other interesting uses of machine vision technology include all types of OCR (Optical Character Recognition) systems for recognizing characters from raster images. OCR’s task can involve reading labels and automatically extracting data from packaging. Code readers, both for regular 1D barcodes and 2D QR codes, are also machine vision systems. Optical sorters, operating both in-line and off-line, are also part of vision systems. These devices combine a camera system with a rejection mechanism. Optical sorters can achieve throughputs of several tons of sorted material with almost 100% accuracy. These machines are widely used for sorting food such as grains, fruits, and vegetables, as well as inorganic products like ceramics, minerals, glass, and waste.
The examples presented are only typical, repeatedly used solutions utilizing vision systems. However, each issue constitutes a separate, interesting topic that opens new areas of possibilities for machine vision. The continuous development of both vision components and software contributes to the creation of increasingly accessible and cost-effective solutions.
