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A business that depends on physical components to manufacture products or help provide services often needs 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.

Product and Components Assembly

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!

Packaging Inspection

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.

Defect Reduction

Understandably, if you run a manufacturing line, you want to 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%!  You can read more about this story here. is a firm based in Silicon Valley that was founded by AI guru Dr. Andrew Ng.  Part of Dr. Ng‘s work at involves developing machine vision tools to find microscopic level defects in products that simply cannot be identified using human vision.  A machine learning algorithm can be trained on a relatively small number of images and yields fantastic results.

3d Vision Inspection

Machine vision can play a massive role in the motoring sector.  One report suggests that the overall machine vision market could be worth up to $14.43 billion by 2022!

A machine vision inspection system that contains a Dalsa Genie Nano camera is being used in a production line to undertake tasks that humans can sometimes struggle with.  In this use case, the system uses high-resolution images to build up a full 3d model of components and their connector pins.

As components pass through the manufacturing plant, the machine vision system takes multiple scans of images from different angles to produce a 3d model, these images, when combined, allow the system to identify if connector pins on circuitry are faulty which could have disastrous effects later down the production line.

3d vision inspection has many applications but one of the most common use cases for the technology is in the production of automobiles.

With electrical faults accounting for a lot of automobile faults these days, being able to perform 3d scans of connector pins can help car manufacturers drive cost savings, reduce the chance of shipping faulty electrical components and help improve driver safety.

Improving Safety

The applications of machine vision aren‘t just restricted to productions lines in manufacturing plants.  For example, Komatsu Ltd, who is a leading manufacturer of mining and construction equipment based in the UK, recently announced plans to partner with NVIDIA to integrate NVIDIA‘s set of “cloud to edge” technologies.  The main driver for this was to improve site management services, safety, and efficiency.

The partnership integrates the NVIDIA Jetson AI platform into machinery often used with drilling, excavation, and mining.  A combination of real-time cameras and video analytics allows the equipment to run with greater efficiency and improved safety.

The idea is also to also apply deep learning-based artificial intelligence to track people and predict the movement of equipment to help avoid dangerous interactions thereby improving safety.

With as much as ten thousand injuries occurring in the US each year on construction sites that are associated with vehicles and machinery, solutions like this will be welcomed by firms.

Track and Trace

Pharmaceutical firms are naturally under stringent rules and regulations to ensure their products can be tracked and traced from the production line to the end patient.

To help achieve this, cartons can be printed with details that include but are not limited to, serial numbers, expiration dates, manufacturing dates.  A globally unique identifier, sometimes known as a GTIN (Global Trade Item Number) is often used to allow packages to be tracked worldwide.

Manufacturing systems can autogenerate these identifiers in a master database which are then used later in the production process and sprayed onto containers and the next step of the production process can be performed, which often is the verification of the information that was just sprayed onto the carton on the packaging.