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Non-Logistics Use Cases: Manufacturing

Computer Vision in Manufacturing

Computer vision can help maintain quality standards in manufacturing, improve equipment monitoring and assessment, boost process efficiency, and support facility surveillance and security. Companies are using this technology to automate tasks that were once done manually so that items come off the production line at the optimal rate and with consistent quality. Computer vision enables manufacturers to inspect even the smallest product details, checking for damages and faults to reduce the likelihood of error. This AI can contribute to process optimization, maximizing operational efficiency and minimizing unplanned downtime.

Current Trends

The key priorities of manufacturers have not changed much over time and continue to include ensuring that the supply chain is sustainable as well as efficient. Many companies today are assessing the benefits of decentralized manufacturing, which is where production occurs in multiple locations and therefore manufacturing takes place in many separate facilities. They are also exploring digital solutions including AI and computer vision technology since there are many benefits to using AI in manufacturing.

In 2020, safety concerns regarding door-latching mechanisms promoted a global recall of 13 million vehicles from a leading car manufacturer.

Here we explore three important computer vision manufacturing applications.

Quality Inspection with Computer Vision

One of the most important ways that computer vision is already used in manufacturing today is automating quality checks during production. In the past these types of inspection were done manually by quality control experts, but even the most skilled inspector could make mistakes.  

For higher accuracy in quality control and inspection, many manufacturers are choosing to use computer vision. When this is linked with deep learning, the quality inspection system can be trained and retrained to undertake not just one but many different tasks in parallel. These technologies are highly effective and help to make operations more efficient, too.

An example of an AI use case in manufacturing is that of Novacura, a Swedish startup, which has replaced inspectors with specialized cameras on the production line. Computer vision is used to extract data from the captured images which is further used for automating quality inspection on the production line. This system can even identify cracks in metal pieces, for example, which can’t be seen by the human eye. 

Another example of how AI is used in the manufacturing industry is for automated quality inspection and control. Slovakian startup Photoneo offers a 3-dimensional machine vision scanner designed for demanding industrial tasks such as precisely inspecting heat exchangers. This solution promises higher accuracy and faster throughput than humanly possible, along with data that is vital to optimizing manufacturing processes.

Equipment Monitoring and Predictive Maintenance with AI

What if the maintenance team fails to notice fault in the equipment? Often this is the case for the specialized tools used in manufacturing plants – over time, they show signs of wear and can break, risking damages to the goods as well as causing disruptions to the process. Computer vision can be used to find flaws in real time even in tiny machine parts without slowing down production. Machine-learning techniques in manufacturing are able to identify problems and figure out what’s wrong with the equipment – intelligent fault diagnosis – and make predictions, enabling data-driven predictive maintenance so it can be fixed in a cost-optimized way before it fails.

Another example of AI is manufacturing is SparkCognition, a software startup in Austin, Texas. They apply machine-learning algorithms to historical visual data from factory equipment to build a baseline model of what normal operations look like. This machine learning application is used in manufacturing to analyze video data in real time, identifying and flagging any deviating value. This information is essential to increasing throughput, preventing quality issues, ensuring operational efficiency, and cutting maintenance costs.

Process Optimization with AI

Computer vision systems can be highly effective at increasing organizational efficiency through process improvement. And often the best outcomes are achieved by combining human skills – sight, intelligence, and brain power – with this technology. 

IFM, a German electronics startup, offers a solution that integrates a computing unit, software, and a camera to capture videos and 3D images. Images are captured by the camera installed at the workstations and then computer vision is applied to extract information regarding the process sequence from the image so it can be displayed on the monitor directly in the worker’s field of vision. Color coded information such as tasks completed, and upcoming steps are used to guide the worker so they can complete their tasks without making any mistakes.

To achieve its objective of ensuring absolute safety in manufacturing processes, Dow Chemicals employed a computer vision system. This system was designed to detect and prevent early leaks and potential contamination. The system’s effectiveness relied on a model that underwent training using various annotated images depicting instances of leaks and non-leak scenarios. Furthermore, the model was fed with real-time data from surveillance feeds, enabling it to successfully fulfill its safety objectives.

FAQs about Computer Vision in Manufacturing

How is AI, such as computer vision, used in the manufacturing industry?

Computer vision is a field of artificial intelligence. In manufacturing, AI and image processing techniques are used to enable machines to interpret and understand visual information from cameras and sensors in order to make automated decisions and perform tasks within the manufacturing process.

What are the key benefits of using computer vision in manufacturing?

This AI can improve quality control, enhance productivity, optimize processes, reduce errors, increase safety, and enable real-time monitoring and analysis in manufacturing processes. AI can also be used to aid predictive maintenance.

What types of manufacturing tasks can computer vision be applied to?

Computer vision can be applied to a wide range of tasks in manufacturing, including defect detection, object recognition, quality inspection, robot guidance, assembly verification, and process monitoring.

How does AI contribute to predictive maintenance in manufacturing?

Computer vision can monitor equipment and machinery for signs of wear and tear, damage, or anomalies, enabling predictive maintenance by alerting operators to potential issues before they result in costly downtime.