Читать книгу Artificial Intelligent Techniques for Wireless Communication and Networking - Группа авторов - Страница 49

Artificial intelligence has reached many different business markets today. Compared to machine learning, it also occurs in factories and takes those experiences to the factory floor. An aluminum die-cast maker for transmission parts could previously identify 60% of the defects by manual checks in the automotive industry. By using computer vision and machine learning, at any given process stage, they are now getting close to 100% defect detection. The fourth industrial revolution, Industry 4.0, introduced a mixture of emerging innovations, such as machine learning and IoT smart devices [11].

By introducing condition monitoring systems and growing their analytics capabilities, many companies are already using IoT technologies to track resources in their warehouses and simplify their control rooms. One analysis found that within their set-ups, 35% of US manufacturers are already using smart sensor data. In processing, many data-intensive devices are also used in close vicinity. That’s why the trick is networking with 5G. In an industry focused on data-intensive computer applications, the higher speeds and reduced power of 5G are needed for the efficient use of automated robots, wearable devices, and virtual reality (VR) headsets, discussing the future of industrial automation. AI, Machine Learning, Robot Process Automation (RPA), improved communication, are developing and adapting each of these technologies to fit various use cases and industrial requirements. To satisfy consumer needs and expectations, their abilities and ability are constantly evolving.

For a long time, the manufacturing sector has been grappling with legacy issues around quality management. Not only is it a costly, time-consuming and challenging operation, but it is one that is vulnerable to error. Before they lose concentration and precision, human beings can only do so much repetitive work. On the other hand, AI-powered quality control systems will keep working 24/7 without getting bored or tired. Their ability to repeat, rinse and repeat is what brings a much-needed advantage to the manufacturing sector to ensure continuous quality management [16].

Intelligent systems will radically change how the industry responds to these changes as quality management standards, enforcement criteria, and regulatory demands become more complicated and demanding. AI never sleeps, it can learn, it can adapt, and it can be managed within extremely precise limits to produce incredibly accurate results. It can be programmed, targeted, and precise. All the variables that play an increasingly important role in a company’s long-term performance.

Although the technology is still in the early stages of its potential, it still provides a surprisingly powerful forum for the industry to develop integrated and effective solutions for quality control. Ultimately, as networking increases and systems are given increased capacity for linked communication and collaboration, process capability and effectiveness will be transformed by AI and automation [1].

The industrial sector is on the verge of a transformative shift with AI and 5G that will not only impact infrastructure, but cost, quality control, and growth. For industrial companies, motion control and tele-robotics will be a specific field of development as various companies will exploit the authority to influence real machineries through virtual objects via master control frameworks. Teleoperation is converted in Mind Commerce by digital twin technology, which corresponds to the mapping of the material realm to the virtual environment in which IoT networks the digital twin of a physical object can provide data about the product, such as its physiological body and disposition.

Various AI technologies and their use within the increasingly increasing enterprise and industrial data arena relative to analytics solutions. This analyzes new market models, leading businesses, and solutions. This discusses how to better use different forms of AI for problem solving. Also measured is the need for AI in IoT networks and systems. It offers unit growth and revenue forecasting for both metrics and IoT from 2019 to 2024 [3].