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, tracking for damages and faults to reduce the likelihood of error. And it can contribute to workflow 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 sustainability and optimizing the supply chain. Many companies today are assessing the benefits of decentralized manufacturing. They are also exploring digital solutions including AI and computer vision technology.
Here we explore three important computer vision manufacturing applications.
Quality Inspection
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.
Novacura, a Swedish startup, puts specialized cameras on the production line where an inspector previously stood. Computer vision is applied for extracting data from the captured images which is further used for automating the quality inspection on the production line. This system can, for example, identify cracks in metal pieces, even faults that can’t be seen by the human eye
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
What if equipment faults are invisible to the maintenance team? Often this is the case for the specialized tools used in manufacturing plants – over time, they show signs of wear and can break, risking goods damage and stoppages. Computer vision can be used to find flaws even in tiny machine parts in real time without slowing down production. Machine-learning techniques can be used to identify problems and figure out what's wrong with the equipment – intelligent fault diagnosis – and make predictions, enabling data-driven predictive maintenance for a cost-optimized fix before failure.
SparkCognition, a software startup in Austin, Texas, applies machine-learning algorithms to historical visual data from factory equipment to build a baseline model of what normal operations look like. This is used to analyze video data in real time, identifying and flagging any deviating value – information essential to increasing throughput, preventing quality issues, ensuring operational efficiency, and cutting maintenance costs.
Process Optimization
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 using computer vision information regarding the process sequence is extracted from the image and displayed on the monitor placed directly in the field of vision of the worker. Color coded information like task completed and upcoming steps are used to guide the worker to perform tasks without error.
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.
