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5 Examples of Using Machine Vision in Manufacturing

5 Examples of Using Machine Vision in Manufacturing

There are many applications for machine learning, whether it be processing social media traffic and trying to surface actionable insights or targeting consumers based on past purchases.  In this article aimed at those interested in artificial intelligence, we look at 10 examples of machine vision in manufacturing which include the following:

  • Predictive Maintenance
  • Package Inspection
  • Reading Barcodes
  • Product and Components Assembly
  • Defect Reduction
  • 3d Vision Inspection?
  • Improving Safety
  • Track and Trace
  • Plain Text Reading and Handwriting Analysis
  • AI and Deep Learning – Landing.AI





A business that depends on physical components to manufacture products or help provide services often need to undertake maintenance on machinery or equipment or in the worst-case scenario, machinery can break or components can become faulty bringing product to a standstill.


Predictive Maintenance is the process of using machine learning and IoT devices to monitor data on machinery and components, often using sensors, to collect data points and identify signals or take corrective actions before assets or components break down.


Consider that just one minute of downtime in an automotive factory can cost as much as $20,000 on high-profit vehicles.  Its challenges like machine vision can help business keep on top of, for example, a software program called ZDT (Zero Down Time), developed by FANUC, collects images from cameras attached to robots, these images and accompanying metadata are then sent to the cloud for processing and helps to identify potential problems before they arise.


During an 18-month pilot, the solution was deployed to 7,000 robots in 38 automotive factories across six contents and detect and prevented 72 component failures!





It is critical for pharmaceutical companies to count tablets or capsules before placing them into containers.  To solve this problem, Pharma Packaging Systems, who are based in England, has developed a solution that can be deployed to existing production lines or even ran as a standalone unit.


A key feature of the solution involves using computer vision to check for broken or partially formed tablets.  As tablets make their way through the production line, pictures are taken and transferred to a dedicated PC that then processes the images using software which then runs further analysis to check if the tablets are the right color, length, width, and whole.


The PC based Vision Inspection system is also implemented to a PC that performs the counting function and if a tablet is deemed as defective, this information is logged which then sends a signal to the counting functioning, and by the time the bottle of containers reaches the end of production line, containers that have defective tablets are then rejected, thereby removing the possibility of shipping defective medical tablets.





Reading, identifying and processing hundreds and thousands of barcodes per day is no easy task and something that humans simply cannot do at scale.


For example, cell phones and mobile devices require smaller and smaller printed circuit boards (or PCBs).  As manufacturers are pressured to produce higher volumes of PCBs for the ever-growing tech market, they are looking towards a process known as “panelization”.  In this process, a number of identical circuit boards are printed onto a large panel, each circuit is then separated by the machine for final testing, in order to inspect these boards, however, a machine vision based solution called PanelScan was developed to read the barcodes – which are the unique identifiers of each circuit that is present on the PCN panel.


Historically a human applied this task by using a handheld barcode scanner, naturally, this was time-consuming and open to human error.  By implementing a machine vision based solution, PCB manufacturers can drive business savings.




High performant manufacturing plants need to ensure products and components that fall off the production line adhere to quality, safety and production guidelines. It‘s with this in mind that Acquire Automation has developed a suite of solutions that help businesses ensure their product and component assembly standards are being enforced.


For example, one of their solutions implements machine vision that allows manufacturers to inspect bottles in a full 360-degree view to ensure that products are placed in the correct packaging and is also able to inspect other critical attributes of packaged products such as:


  • Cap closure/seal
  • Position
  • Label
  • Print quality and much more!

All of this helps increase the throughput of the production line whilst at the same time reducing the number of product recalls and increasing productivity and ultimately, keeps consumers happy!





Understandably, if you run a manufacturing line, you want produce components or products that are free of defects!  Machine vision is a technology that can help businesses achieve this.


That said, machine vision inspection systems can vary widely in terms of their implementation, some require an operator whereas more complex vision based solutions do not need an operator.


A firm named Shelton has a surface inspection system called WebSPECTOR that identifies defects and stores images and accompanying metadata related to the image.  As items fall through the production line, defects get classified according to their type and are assigned an accompanying grade.


Doing this allows manufacturers to differentiate between different types of defect who may then wish to only halt the production line when X number of Y types of defect has occurred.


Another one of Shelton’s machine vision based technologies called WebSpector which leverages imaging software and state of the art cameras could improve the productivity of a fabric producer by 50%!



In this blog post, we‘ve looked at 10 examples of machine vision in manufacturing, we‘ve covered everything from textiles to pharmaceuticals and touched on how artificial intelligence and deep learning are also making an impact into the machine vision space in the form of image recognition.


We hope that by reading this you‘ve got some more insights as to how machine vision can be applied in manufacturing.


Source: https://www.devteam.space/blog/10-examples-of-using-machine-vision-in-manufacturing/