AI and manufacturing have a natural relationship since industrial manufacturing settings already require people and machines to work closely together. At its core is the intelligent networking of machines and processes in factories with the help of information and communication technology. According to a study by the auditing company PwC, 91 percent of all industrial companies already invest in digital factories. However, only six percent of them can claim that their operations are already fully digitized. We are focusing on researching the use of AI in production processes and developing solutions based on it – for digital factories with artificial intelligence logistics and automation robotics.
It isn’t distracted or tired, doesn’t make mistakes, or get hurt, and can work in environments (such as dark or cold) where humans might be uncomfortable. AI can improve the customer experience at many points in the customer journey. AI automates calculation and code to take the stress out of complex mathematical problems. It also bundles them into easy-to-use, sometimes no-code tools engineers can use to speed up their workflow. However, some computers outperform human professionals and specialists in certain tasks.
AI-powered hands-free control systems in manufacturing plants enable human workers to control machinery and equipment using voice commands or gestures without needing to physically touch them. This is particularly useful in hazardous environments where physical contact must be minimized. Computer vision is a technology that uses high-resolution cameras to observe every step of production, and with the help of AI, it can identify flaws that may be missed by human eyes.
As seen on Google Trends graph below, the panic due to lockdowns may have forced manufacturers to shift their focus to artificial intelligence. Collaborative robots — also called cobots — frequently work alongside human workers, functioning as an extra set of hands. 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. Edge analytics uses data sets gathered from machine sensors to deliver quick, decentralized insights.
Manufacturers typically direct cobots to work on tasks that require heavy lifting or on factory assembly lines. For example, cobots working in automotive factories can lift heavy car parts and hold them in place while human workers secure them. While autonomous robots are programmed to repeatedly perform one specific task, cobots are capable of learning various tasks. They also can detect and avoid obstacles, and this agility and spatial awareness enables them to work alongside — and with workers.
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