The global machine vision industry is expected to reach a market value of $18.4 billion by 2028. As a robotics integrator, we’re particularly interested in the use of computer vision systems in industrial environments and the multitude of ways manufacturers can benefit from them. Machine vision uses digital imaging technology to guide manufacturing and production operations automatically. It extracts information from digital images to perform tasks such as material handling and assembly, part inspection, and quality control. The applications for machine vision are numerous, and as we will see, the next generation promises even more possibilities.
How Machine Vision Systems Work
Machine vision systems need several key components to work—sensors, cameras, light sources, frame-grabbers, and software. Sensors detect the presence of a product. If a product is detected, a camera acts as the robot’s “eye” and captures an image, while a light source highlights key features. A frame-grabber translates the camera’s image into digital output stored in computer memory. Software processes the image, analyzing it to identify defects or proper components based on predetermined criteria. Robots then perform programmed actions according to the results of the vision analysis.
3 Common Applications of Machine Vision
Robotic Guidance
An increasing number of manufacturers are adopting machine vision in vision-guided robotics, which enables robots to use visual information for precise and adaptable movements, such as moving its arm, gripper, or tool to a desired position. Vision-based robot control can enable flexible and adaptive manufacturing, as robots can adjust to changes in the environment, product, or task without requiring extensive programming or calibration.
Machine vision can guide robots in tasks like pick-and-place, sorting, and assembly by providing accurate and reliable information about the location, orientation, shape, and size of the objects they need to manipulate. Vision-based robot control can also improve the quality and efficiency of production, as robots can perform tasks with higher accuracy, speed, and consistency.
In the automotive industry, vision-guided robotics may align parts for assembly or adjust the orientation of parts for accurate placement. In other industries like consumer goods, robotic guidance may use vision to find products (“bin picking”) and get those products onto a conveyor for further sorting or the next step in the manufacturing process. Manufacturers may also use robotic guidance to aid the assembly of fine parts, like circuit boards, chips, and wires.
Automated Inspection
Machine vision automated inspection can improve product quality, reduce production costs, and boost the overall performance of manufacturing processes. Machine vision surpasses human vision because of its speed, accuracy, repeatability, and ability to find object details too small to be easily detected by humans and inspect them with greater reliability.
Inspection tasks include:
- Detecting contaminants, irregularities, and functional flaws.
- Measuring object dimensions, shapes, and positions.
- Verifying product completeness, package integrity, and label accuracy.
- Counting items.
- Checking assembly operations.
So, in the automotive industry, machine vision may inspect for part defects or satisfactory completion of operations such as welding, riveting, and gluing. In manufacturing consumer or durable goods, machine vision may inspect package seals, labels, imprinted codes, color, shape, and size, as well as determine whether products and packages match. Similar inspections may take place in medical and pharmaceutical manufacturing, as well as checking for contaminants and dosages.
Quality Control
Quality control is closely related to inspection. Because machine vision detects errors in real-time, the data that can be collected by supervisory control and data acquisition (SCADA) systems and integrated into manufacturing quality improvement processes is invaluable.
Further, since machine vision can optimize the production process by measuring and adjusting elements, such as temperature, pressure, flow, or speed, it ensures optimal performance while eliminating human errors that result from manual regulation of the same elements.
Machine Vision Challenges and Solutions
The complexity and variability of real-world environments are the biggest challenges that vision systems must overcome; shadows, reflections, and other obstructions can hinder vision systems. These challenges are overcome with active lighting, which creates consistent illumination that aids the machine in “seeing” better with full light. Another way is to use the predictive power of machine learning to handle a variety of scenarios. We’ll discuss machine learning more in the context of machine vision trends.
The position and structure of objects themselves can pose challenges. For example, malleable or moveable objects may make it harder for the vision system to recognize the object; objects that have been rotated or appear at strange angles can also increase difficulty. In this case, 3D, rather than 2D vision systems, which can detect depth and shape, may be more appropriate. The growth of 3D versus 2D is another trend in machine vision worth exploring.
History and Emerging Trends in Machine Vision
According to Control Automation, machine vision began decades ago and has been evolving ever since. Machine vision technology is thought to have started in the 1950s when images began to be identified with the use of neural networks. The 1970s saw the first commercial use of machine vision to detect the difference between handwriting and type. However, machine vision in industrial automation became more widespread and advanced in the 1980s, when it started to be used for identifying symbols and labels. In the 1990s, when digital imaging and computing technologies improved.
Numerous advancements in technology since then have given us some exciting trends to watch as machine vision in industrial automation continues to grow. As mentioned briefly above, machine learning will likely continue to impact the progress of machine vision, especially in the form of deep learning. Deep learning is a type of machine learning, both of which are forms of artificial intelligence (AI). Machine learning is a broad term that covers various techniques and algorithms that can learn from data and make predictions. Deep learning uses large amounts of data and computer power, enabling it to automatically learn features from unstructured data, such as images, text, or audio.
The use of 3D machine vision is a relatively recent development in the field of industrial automation; its adoption and innovation have accelerated in the past few years as technology continues to advance and find new uses. More conventional 2D technology only captures the length and width of the objects, while 3D systems, because of 360-degree cameras, also capture the height and orientation of the objects. 2D systems work well for barcode reading, pattern matching, color sorting, and edge detection. When depth information is required, such as for random bin picking or assembly or even meticulous product inspections, 3D machine vision systems are preferred.
If you are looking for a reliable and efficient vision system for your industrial automation project, Wes-Tech has the expertise and experience to help you choose and integrate the best vision technology for your application. We can help you with inspection, sorting, picking, manipulation, measurement, and more. Contact us today and let us show you how vision systems can help improve your operational productivity and product quality.