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Computer Vision in Manufacturing - Top 5 Use Cases

computer vision in manufacturing

From the use of machine vision in warehouse to advanced robotics in R&D labs, technology is creating a significant impact at every stage of manufacturing process. These technological advancements in the manufacturing industry has helped in reducing product defects, increasing flexibility, and enhancing product quality.

In this blog, we will start with an introduction to machine vision and its potential applications in the manufacturing industry.

Table of Contents

An introduction to computer vision in manufacturing

Computer vision applications in manufacturing industry

Machine vision is a systems engineering discipline that uses multiple cameras to automatically inspect objects in a production environment. The data extracted from analyzing the image is further used for controlling a manufacturing process. An example can be, a camera used to capture any operation performed on an assembly line. In such a case, the camera is programmed to assess the size, shape, or color of any object.

Similarly, it can also be used for deciphering a 2D matrix barcode or read any printed characters. The main applications of machine vision are - a) imaging-based automatic inspection and sorting, b) image-based robot guidance.

How is machine vision used in manufacturing?

Machine vision applications are most efficient when strategically integrated into the smart factory as it enhances both human and digital performance. Manufacturers should focus on these 2 aspects to utilize machine vision benefits to the fullest: How the machine vision technology can enable operators to be more efficient and accurate and how other technologies can seamlessly integrate with machine vision to unlock the full potential.

Related: Download the checklist that lays out everything you should consider  before implementing a machine learning project or workflow →

5 Computer vision use cases in the manufacturing industry

Predictive Maintenance

In the worst-case scenario of equipment breakdown or a malfunction in components, work comes to a standstill. Any business dependent on physical components has to consider the maintenance of necessary machinery or equipment. Predictive Maintenance is defined as the process of using IoT and Machine Learning to predict when assets require maintenance. This enables the manufacturer to diminish downtime and optimize equipment lifetime.

For example, a software program was developed by an industrial supplier manufacturer that collected images from cameras attached to 7000 robots across 38 factories. This image collection together with metadata could detect and prevent 72 instances of component failures.

Package Inspection

Pharmaceutical companies face the trouble of counting the number of capsules before packing them in containers. An England-based company discovered a solution to this problem. A machine vision-based inspection system was implemented to count the number of tablets that goes in a bottle at the end of production. If any tablet is deemed defective, the system sends a signal to reject it at the final stage of packaging. The system uses machine vision to probe whether the tablets are of the right length, width, and color. Pictures are clicked when tablets move to the production line and these images are processed by the PC for further analysis.

The main aim is to eliminate any probability of shipping defective medicines.

Reading text and barcodes

Reading and identifying text and barcodes every day is not an easy job. To tackle this problem, future factories will witness the rise of industrial automation and modernized machine vision systems.

Industries are incorporating OCR (optical character recognition) technology to make the information in an image machine-readable and usable.  Software and hardware vendors are increasingly implementing sophisticated text recognition technologies, like barcode recognition (OBR), intelligent character recognition (ICR), and optical mark recognition (OMR) to expand the functionalities of existing machine vision systems. These functionalities are explained in detail below:

  • OCR is used to recognize text from scanned documents or screenshots.
  • ICR is used to read text from hand-written forms, eg. questionnaires
  • OMR is used to recognize check boxes in surveys or forms
  • OBR is used to recognize traditional 1D and 2D barcodes for automatically routing parts through the production line

Product and Components Assembly

Machine vision has enabled manufacturing companies to ensure that assembly of product and components are strictly adhering to standards. For example, pharmaceutical manufacturers are able to inspect bottles in 360 degrees to ensure correct packaging. They can also examine other critical features of packaged bottles like cap seal, position, label, and much more.

These stringent assessment criteria reduce instances of product recalls as well as improves productivity. In the end, consumers remain happy and satisfied with what they get.

3D Vision Inspection

3D machine vision systems usually comprise of several cameras or laser displacement sensors. Multi-camera 3D vision systems have multiple cameras installed at different locations and offer orientation information to the robot.

A machine vision inspection system is placed on an assembly line to identify too short connector pins. Spotting these short pins is a difficult task for manual inspectors. If a defective connector pin passes through the production cycle, it could spell disaster for the vehicle owner and the manufacturer.

What will the future factory look like?

Till now, we got a fair idea how machine vision can transform the manufacturing industry by offering image recognition.  The changing face of manufacturing and distribution has led to the emergence of ‘smart’ products and innovative manufacturing models. Automation in the form of image and voice recognition is set to increase levels of productivity and accuracy. We are all set to welcome a bright future of increased efficiency and healthier economies. The future factory will have improved operational efficiency, reduced water and energy expenses, enhanced safety, and lower inventory.

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