What does AI smart manufacturing look like in your imagination? Is it like an advanced factory with state-of-the-art equipment, everything automated, just like the ideal future factory?

（示意圖，來源：https://vmaker.tw/archives/40034）

Yes, as you might imagine, the future of factories or what we call AI smart manufacturing mainly involves automation and intelligent production, as mentioned earlier. However, AI smart manufacturing has become a buzzword in recent years, just like digital transformation – everyone wants to do it, but no one really knows what it means.

So, what exactly is AI smart manufacturing? Let me offer a rough concept for your reference: Smart manufacturing involves using new information technologies to solve the pain points and challenges of traditional manufacturing industries, thereby improving efficiency and quality.

Based on this concept, AI smart manufacturing is easy to understand – it means "using AI to address the pain points of traditional manufacturing industries." So, what are some practical cases of cloud solutions in AI smart manufacturing? There are some. In this article, I will provide an example of how cloud platforms can assist in AI to open up new possibilities for traditional manufacturing industries, using the challenges faced by Taiwan's traditional steel industry as an illustration.

Traditional steel production is mainly concentrated in central and southern Taiwan or in the eastern region, where most companies engage in processes like steel grinding, casting, and shaping. Currently, most Taiwanese steel companies are actively undergoing transformation and introducing new equipment and expanding new plant areas. However, while it's easy to use new solutions for new facilities and equipment to achieve optimal manufacturing efficiency, the old plants and equipment, which are still the mainstay, face bottlenecks in terms of capacity and cost.

In this case, the problem stems from the layout of the plant and the fact that the equipment used for grinding steel dates back to the World War II era. The original equipment suppliers have closed down, making it impossible to update or upgrade the equipment. Consequently, the steel produced must be transported from Point A to Point B for inspection, and if it's found to be non-compliant, it has to be brought back to Point A for regrinding.

Apart from the significant labor costs involved in this process, the most important aspect is the substantial time spent on quality control. So, how can we use AI to reduce labor and time costs in this scenario?

In this case, without affecting other variables, an edge recognition device will be installed at the discharge port of the grinding machine. The images captured will be transmitted through the factory's physical network to the cloud data center, where AI image recognition functions will be used for image interpretation, and the results will be returned in real-time.

Of course, if the factory has strict cybersecurity requirements, a more advanced approach would be to collect a large number of steel images and pre-train a model on the cloud platform using machine learning. Then, the trained model can be deployed on edge devices to achieve edge computing effects, thus upgrading the existing industrial chain.

The process is illustrated in the diagram below:

This article merely briefly discusses the current challenges and solutions in the manufacturing industry using the example of the steel industry. In the next article, the author will delve deeper into exploring cloud-based solutions and extensively discuss the overall architecture as well as the AI training process.