The Future of Machine Vision in Manufacturing

Machine vision is a combination of seeing and understanding. It mimics the human eyes and brain. It makes machine vision applications more sophisticated. Such systems provide sophisticated analysis like image recognition. It works using various data science methods like deep learning and advanced big data analytics. Machine vision in manufacturing increases efficiency and reduces costs. It’s observable in the long run. Let us check out some of the current applications of machine vision in manufacturing.

Current Applications of Machine Vision in Manufacturing

  • Assistance and inspection during manual assembly

Despite the effort to automate assembly completely, some parts are still assembled by hand. In such cases, using an assistant camera can prove useful. The human operator does his thing to connect a specific part. After that, the camera scans the result and compares it to the referent image to see if it looks okay.

The machine vision inspection can check each step for mistakes using its image recognition algorithms. Furthermore, the camera can log the resultant images and data analytics results. Manufactures can use it for proof that everything has been assembled according to regulations.

  • Industrial robots

The advancements in computer vision led to the rapid development of robots that can see and perceive their environment. It uses a set of cameras and image recognition to manage its surroundings. These robots need external computing resources to power their intelligence. An example might be a moving track with plenty of objects that need to be sorted or filtered. For humans, it is a dull job prone to mistakes. But, computer vision makes it simple for vision-guided industrial robots! Such a robot would have a gripper and a camera that analyzes objects passing by in real-time.

Sorting Robot with Machine Vision

Credits: Singulation and sorting of parcels can benefit from AI-powered robots - The Robot Report

  • Worker safety

Despite all efforts to ensure worker safety, reports show that a worker is injured on the job every 7 seconds. Computer vision systems can use many cameras around the plant. After performing real-time image recognition, these systems can detect if a worker is in danger. After detection, it can alert everyone that can help or even shut down certain parts of the plant. Automatic reactions can save lives by reacting faster than humans.

How Machine Vision Drives Innovation in Manufacturing

Three key concepts can make manufacturing better:

  • Faster production

Being able to produce more in the same timeframe is a major plus for any manufacturer. Machine vision powers precise robots and automates small and repetitive tasks. Robots don’t need breaks, they don’t get tired, and they work in sync. The cost of hiring a data science company and installing the required technology might sound expensive. Still, any calculation will assure you the investment will pay off soon enough!

  • Precision

Machines are still not smarter, but they are more precise with delicate tasks. Many items are ruined because of human errors, which increases rejected products and write-offs. Machines can achieve high precision, especially when they use image recognition. They work in combination with many sensors, forming a complex structure. It makes smart robots perfect for small, repetitive assignments, where humans are prone to errors.

Another benefit of machine precision is worker safety. For example, if a robot needs to drill a hole in a specific spot, it will not do it unless the camera does not see the location. Also, if the worker’s hand happens to wander near the drill, deep learning algorithms can be trained to stop if anything is covering the target point. The image below displays an example of a collaborative robot that can integrate with humans without causing harm.

Collaborative Robot

Credits: ABB's Collaborative Robot -YuMi - Industrial Robots from ABB Robotics -

  • AI and Automation

Automation of any kind leads to increased efficiency, lower costs, and better profits at the end of the day. Primitive automation can use simple controllers to automate the plant and shut it down in case if something goes wrong. Smart automation with image recognition can recognize various scenarios and react in an appropriate manner. Before, plants were partly automated with workers present for monitoring and some delicate tasks. Manufacturing with machine vision can now monitor and make quick decisions as reactions to extraordinary events.

Should Manufacturers Outsource Data Science Projects or Hire an In-house Team?

Everything mentioned above shows how important crucial vision can be for manufacturers. The benefits are too many to number, and the only downside seems to be going through the development process. Still, should they hire full-time data scientists or outsource data science projects?

The answer we stand behind is no. Data science is a field where quality work is done within a specific context. The process is that manufacturers have a problem they want to solve, and data scientists work until they find a solution. A manufacturing company might look for several solutions and optimizations. Anyway, it is better to outsource data science projects to companies. The best part about outsourcing is that you worry less. Data scientists in these companies are skilled, and they will focus on the task at hand. Some data science companies specialize in computer vision for manufacturing. You won’t have problems finding the right one for the job!

Data Science

The Future of Machine Vision in Manufacturing

Computer vision is such a famous deep learning application that they started shipping chips with embedded machine vision capabilities. This allows you to use this processing power exclusively for image recognition. It means you can use other computers for monitoring, data visualization, and other purposes. As computer vision progresses, new industrial robots will be able to take to roll out in manufacturing plants. Machine vision also helps integrate robots into the workplace. Developers make sure humans can’t be harmed by powerful robotic manipulators.

The future of manufacturing might look like a large plant with virtually no humans inside. In the plant, robots will be able to use image recognition not to bump into each other, and they can perform their everyday tasks efficiently. Humans might be needed for monitoring and to jump in if something goes wrong.

Final Words

Image recognition is a vital part of machine vision. It uses deep learning algorithms to extract important information from images. There are many useful applications, from medicine to law enforcement. However, it seems like AI and manufacturing go hand in hand. Machine vision in manufacturing powers things like automatic inspection, smart robots that perceive their environment, and smart monitoring systems in case something goes wrong. The list goes on, and each manufacturer wants something new to bring to the market. Three main reasons for using computer vision include faster production, enhanced precision, and a whole new level of automation.

If you are a manufacturer and you don’t know anything about computer vision, don’t worry! There are many data scientists for hire. The best choice you can make is to outsource data science projects to a data science company.

The future of manufacturing will be shaped by machine vision and artificial intelligence. Soon, humans might become obsolete in manufacturing plants because machines will beat humans in every category. Currently, the stepping stone is expensive to many, but it will decrease over time and give everyone the change of getting to know AI in its true power!


Michael Yurushkin

Founder of BroutonLab, PhD