performance and cost, which not only accelerates the deployment of AI across various industries, but helps companies gain insights into data, taking improvements of retail experience, city appearance and smart manufacturing to the next level," said Skaugen. ...
AI can help manufacturing and industrial companies optimize their energy and water consumption. For instance, Metinvest uses Azure Data Factory and Azure Machine Learning to detect the silicon content in the cast iron, which helps minimize the fuel the process requires.6...
Manufacturers are turning to AI to get the most out of the increasing volume of data they collect. However, the technology is at an early stage of deployment, and the business case for manufacturing AI projects runs counter to the industry's culture. As a result, businesses in this sector ...
Furthermore, research opportunities and challenges for broader adoptions of AI in AM applications are thoroughly discussed, particularly for human -centered AM products.doi:10.1016/j.jmsy.2022.04.010Liu, ChenangTian, WenmengKan, ChenJournal of Manufacturing Systems...
To value the potential of AI across health systems, more fundamental issues must be addressed: 1. who owns health data; 2. who is responsible for them; 3. who can use them? The potential of AI is well described in the literature [7]. However, in reality, health systems are faced with...
In contrast, the design and sculpting of the data used to develop AI often rely on bespoke manual work, and they critically affect the trustworthiness of the model. This Perspective discusses key considerations for each stage of the data-for-AI pipeline—starting from data design to data ...
We focus on the applications of AI-enabled algorithms, especially DL in monitoring, diagnosis, and prognosis. More importantly, we emphasize the importance of open source datasets and codes for the benign development of the research community of AI-enabled PHM. Last but not least, this paper ...
believes that China's complete industrial chain can turn flexible delivery into a new advantage for China's manufacturing industry. Zhou said that after visiting more than 90 manufacturing enterprises, he found that some suffer from overcapacity while many non-standard custom...
To value the potential of AI across health systems, more fundamental issues must be addressed: 1. who owns health data; 2. who is responsible for them; 3. who can use them? The potential of AI is well described in the literature [7]. However, in reality, health systems are faced with...
In this context, Artificial Intelligence (AI) has emerged as a potential collaborator for NPD teams. Much like the emergence of rapid prototyping in the 1980s, which is now widely accepted as a standard NPD tool in most engineering firms, AI promises to revolutionize NPD by improving decision-...