AI robots in industrial plants utilise machine learning algorithms to automate repetitive and decision-making tasks. These algorithms are self-learning, so as they manage their given processes, they get better over time.
AI robots are also less likely to make mistakes than humans and do not need breaks. Manufacturers are able to simply increase their manufacturing capacity as a result.
Robots can perform heavy lifting on industrial floors while humans tackle the more delicate chores. This increases general productivity as well as workplace safety. In areas where there is a lot of manual labour, collaborative and context-aware robots can increase productivity by up to 20%.
Robots are already being used by a number of automakers to assist with assembly lines. In e-commerce and packing, robots are a quick, less expensive option to humans that also makes fewer mistakes.
Other applications include:
- Welding
- Painting
- Drilling
- Product inspection
- Die casting
- Grinding
Factory workers manually alter equipment settings while monitoring a multitude of signals across several screens using their intuition and experience. Additionally, this system burdens the operators with troubleshooting, testing, and other duties, which hinders their productivity. Operators are hence prone to cutting corners, setting the wrong priorities for tasks, and failing to concentrate on creating economic value.
There are two issues with this strategy:
- Human mistake is common in human-intensive systems, which can result in equipment breakdown and lower overall production productivity.
- It’s more difficult to replace production workers when they rely heavily on experience. Additionally, when an experienced operator departs, context-sensitive knowledge of manufacturing procedures is lost.
AI allows manufacturers to dramatically cut labour costs while increasing overall plant productivity and efficiency. Additional uses include:
- Automate a number of intricate activities in industries.
- Due to ongoing surveillance and monitoring of operations, you can identify any irregularities promptly and notify the specialists straight away.
- Create a central store for context and other operational data to make staff migrations simpler.
- Minimize the number of resources needed to operate a manufacturing
- Easily adjust production to demand swings and manufacturing techniques
Siemens is a well-known illustration of manufacturing automation. The business and Google have partnered to use computer vision, cloud-based data, and AI algorithms to increase shop floor productivity.
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