Glossary

AI in Manufacturing

What is AI in Manufacturing?

AI in manufacturing covers applications in production and industry, such as predictive maintenance based on sensor data, automated camera-based quality inspection, optimisation of process parameters and production planning, and robots that work alongside people. The aims are fewer breakdowns, less scrap and more stable operations. This requires reliable machine data and the involvement of experts on the shop floor. Example: a system recognises from vibration patterns that a bearing may soon fail.

Topics

Industry
Production
Quality

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Related terms

Collaborative Robots (Cobots)

Collaborative robots, or cobots, are industrial robots designed to work directly alongside people without a separating safety fence. Sensors, limited forces and speeds, and safety functions ensure that they stop or move away on contact; requirements are set out in standards such as the ISO 10218 series. Cobots handle tasks such as loading machines, screwing or packing, while employees take care of inspection and special cases.

Robotics
Manufacturing
+1

Anomaly detection

Anomaly detection finds data points, events or patterns that differ markedly from the norm. These might be unusually large transactions, sudden jumps in sensor readings, duplicate records or suspicious network activity. It is used in finance, IT security, manufacturing and quality control. Methods range from simple statistical thresholds to learning models; hits are indications that a person should assess.

Data analysis
Quality control
+1

Template

Computer vision

Computer vision is a field of AI in which computers interpret images and video. Typical tasks include detecting and locating objects, reading text in images and measuring components. The technology is used, for example, in quality control in manufacturing, in driver assistance systems, in logistics and in medical imaging.

image analysis
deep learning
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Predictive modelling

Predictive modelling is the technical core of predictive analytics: example data with known outcomes is used to build a model that predicts a value or class for new cases. Common methods include regression, decision trees, random forests and neural networks. A model might, for instance, estimate a property price from floor area, location and year of construction. Before deployment it is tested on held-back data to detect overfitting.

modelling
prediction
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