I. Misconceptions about Intelligent Manufacturing
Leading international companies have begun applying flexible manufacturing systems (FMS) to achieve fully automated and unmanned processing of various parts in machining and sheet metal fabrication. A machining FMS includes several machining centers, robotic deburring units, cleaning units, rail-guided transport vehicles, and control software. It is equipped with automated shelving systems for placing workpieces and tooling, enabling fully automated machining from roughing to finishing. A sheet metal FMS system can handle the entire sheet metal manufacturing process, from blanking, punching, bending to welding.
FMS (Fixed Manufacturing System) is not a new concept; some companies began using it thirty years ago. However, due to insufficient reliability and stability of early equipment, as well as the complexity of production scheduling and management across multiple machines, some early adopters of FMS abandoned it and reverted to single-machine CNC machining centers. Nevertheless, with the development of automation, digitalization, and inspection technologies, FMS technology has matured in recent years, becoming an inevitable choice for discrete manufacturing companies to improve production efficiency.
II. Several Forms of Intelligent Manufacturing
1. Machine Learning
Machine learning is a form of artificial intelligence that makes algorithms more accurate and reliable in predicting outcomes. There are four standard machine learning methods: supervised, unsupervised, semi-supervised, and reinforcement learning. Historical data is typically used as input to the process to predict new output values. Researchers choose different data for their algorithms depending on the type of data they want to predict. After selecting the data, the machine learning process begins by observing these datasets and then infers from the provided data. The ultimate goal of machine learning is to enable computers to autonomously learn and recognize patterns and adjust their actions with minimal human intervention or assistance.
Machine learning has proven valuable because it provides insights into specific trends or patterns in customer behavior and business operations, enabling businesses to better understand and adapt to future needs. More importantly, with its massive computing power, machine learning can solve problems at a speed and scale that the human brain alone cannot replicate. Many leading companies today, such as Alibaba, Baidu, and Tencent, have machine learning as a core part of their strategic initiatives.
2. Digital Twin
A digital twin is a digital copy of any process, product, system, person, asset, or real-world scenario. Created using data from real-world components, a digital twin reflects what is happening to an object in real time. Digital twins can be adjusted at any stage of the product lifecycle to understand how the state and functionality of the original system would change in real life, thereby gaining more knowledge, a deeper understanding of the production process, and achieving better business goals.
A viable digital twin can help engineers and data scientists in several ways. First, operations teams can use digital twins to identify discrepancies that indicate the need for preventative repairs or maintenance before serious problems occur. Second, digital twins facilitate cross-disciplinary collaboration and sharing. They provide the most important and direct operational data to people from diverse backgrounds, helping them gain a deeper understanding of the work of other team members. This deeper understanding leads to better communication and collaboration. Finally, digital twins can improve customer experience. Because digital twins can automate data extraction, cleaning, structuring, and transformation, they help operations teams solve problems and identify which input variables have the greatest impact on customer experience, thus providing insights into product performance and driving the company forward.
