New Development in Artificial Intelligence as a Service Market: Researchers Created an AI tool that could help Accomplish Quality Management of Digital Production

Posted On April 16, 2022     

One can only survive in any given sector through novel, sophisticated technology. The system may include microchips, pharmaceutical items, or complex machinery. The concept states that the more distinctive the strategy is, the more vital it will be. This is especially true in high-wage countries like Switzerland, where industrial production is booming. It's even more startling that poor quality contributes to 15% of overall operating expenses in industrial manufacturing due to poor quality management.
A study team employed artificial intelligence to improve quality management in digital production processes. In an experiment with semiconductor producer Hitachi Energy, the team reduced the number of defective goods by half. The group highlighted how artificial intelligence (AI) could be used to enhance quality management within sophisticated digital production processes. Their algorithm has already demonstrated its vitality successfully at a semiconductor factory present in Hitachi Energy (previously known as Hitachi ABB Power Grids). The new approach could significantly impact the Artificial Intelligence as a Service Market. This is because it successfully decreased the number of defective products by over 50 percent in one experiment. Thus, making production not only way more efficient but also sustainable.
Researchers created an algorithm that mimics the different phases in the semiconductor manufacturing process. They then fed as much previous production data as possible into the program, such as temperature and pressure readings from machinery. This data trained the software which conditions are required for best semiconductor quality, resulting in high error rates.
AI-based technology has the advantage of analyzing a wide range of elements and relationships in the manufacturing process. Further, it can disclose more complicated interrelationships between parameters. This allows for more systematic identification of causes of mistakes throughout the entire production process. The proposed technique will not eliminate the necessity for well-trained factory engineers.
On the other hand, the algorithm primarily identifies previously unidentified sources of inaccuracy. Fixing them, however, will necessitate a considerable deal of technical know-how and human ingenuity. For the present method to produce good outcomes, it would require a lot of production data, requiring highly digitalized production processes.
However, as manufacturing processes grow more digitized, the researchers' algorithm should appeal to other industries. In the long term, it would help maximize artificial intelligence's economic potential in quality control and make the technology broadly available and feasible.

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