AI in Manufacturing: 5 Use Cases

Industrial AI: How Artificial Intelligence is Revolutionizing the Manufacturing Industry?

ai in factories

Cobots (collaborative robots) equipped with AI can adapt to human presence, work alongside them on intricate tasks, and even learn from their actions. This collaboration improves overall productivity while maintaining a safe work environment. Aside from the obvious impacts on employee health and well-being, safety issues can also negatively impact workplace morale. AI helps to keep employees out of harm’s way, enabling automation and robotics to take over the most hazardous of the processes in the manufacturing facility. AI and augmented reality can also positively impact safety by creating more effective training processes.

They are fast and accurate and the AI also has the ability to learn over time, so they can get even better. The factory operator relies on experience and intuition to monitor signals across numerous screens and adjust equipment settings manually. As a result of this system, operators are also responsible for troubleshooting, running tests, and other tasks. The result is that operators may take shortcuts, prioritize activities incorrectly, and fail to add economic value as a result.

Autonomous Robots

AI and manufacturing have a natural relationship since industrial manufacturing settings already require people and machines to work closely together. AI in manufacturing covers various production stages to boost efficiency, precision, and comprises algorithms, machine learning, and data analysis to allow robots to perform jobs that previously required human contact.

Any change in the price of inputs can significantly impact a manufacturer’s profit. Raw material cost estimation and vendor selection are two of the most challenging aspects of production. To better plan delivery routes, decrease accidents, and notify authorities in an emergency, connected cars with sensors can track real-time information regarding traffic jams, road conditions, accidents, and more. Vehicles that drive themselves may automate the entire factory floor, from the assembly lines to the conveyor belts. Deliveries may be optimised, run around the clock, and completed more quickly with the help of self-driving trucks and ships.

AI Order Management

Utilizing AI’s potential can result in better product quality, lower prices, and more sustainability as the manufacturing industry develops. Within the manufacturing industry, quality control is the most important use case for artificial intelligence. Although these are much more infrequent than humans, it can be costly to allow defective products to roll off the assembly line and ship to consumers. Humans can manually watch assembly lines and catch defective products, but no matter how attentive they are, some defective products will always slip through the cracks.

PCB can have multiple assembly defects like missing screws or solder bridging. Quality Assurance in manufacturing relates to the processes applied to maintain consistent quality levels. QA in manufacturing has great potential for applying AI-based computer vision systems to automate inspection throughout the entire production process. Human vision has its obvious cons such as fatigue and inconsistency, but with computer vision inspection, QA becomes more reliable and precise. Let’s talk about the Dutch railway company and the international player in the sphere of railway maintenance, Strukton Rail.

GE uses AI to reduce product design times.

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ai in factories

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