I. Application of PLM in Intelligent Manufacturing
Intelligent manufacturing should include intelligent manufacturing technology and intelligent manufacturing systems. Intelligent manufacturing systems can not only continuously enrich their knowledge base in practice, but also have self-learning capabilities, as well as the ability to collect and understand environmental and self-information, and to analyze, judge, and plan their own behavior.
With the continuous development of information technology, the concept of intelligent manufacturing has rapidly penetrated the manufacturing field. The current transformation of the manufacturing industry can be seen as the integration of automation upgrades and information technology upgrades, and the application of PLM, or Product Lifecycle Management, is an indispensable core technology that plays an important role in intelligent manufacturing.
1. Intelligent manufacturing
Intelligent products in intelligent manufacturing processes are developed and designed based on PLM, and the foundation of all design software is PLM. In intelligent manufacturing factories, intelligent products are essentially CPS (Cyber-Physical Systems). Their virtual components are designed by PLM, and then linked to production plans and specific parts by ERP (Enterprise Resource Planning) so that they can be intelligently produced by a new generation of MES (Manufacturing Execution System) in intelligent manufacturing factories.
2. Personalized services
After personalized products are delivered to users, the challenge of insufficiently precise service arises. This is where Product Management (PLM) comes in – for a specific product, PLM is the design blueprint at the initial design stage; during production, it's the manufacturing process; during use, it's the instruction manual; and during repair and maintenance, it's the medical record. This allows for more precise and effective service.
3. Personalized customization
Supporting personalized customization is one of the characteristics of intelligent manufacturing in industry. In the era of intelligent manufacturing, personalized customization involves key components, such as engines. Design is a highly specialized task that requires the support of Product Management (PLM). PLM provides suitable components, ensuring that the user-selected components can function properly with other components and guaranteeing product quality.
II. Core of Intelligent Manufacturing
Intelligent manufacturing is transitioning from modularization to integration, and then to networking and intelligence. Modeling and simulation technologies are showing new technical characteristics and applications.
In general, with the development of intelligent manufacturing, modeling and simulation technologies will be more closely integrated with 5G, cloud computing, big data, and artificial intelligence.
Modeling and simulation technologies are showing trends such as real-time simulation, distributed embedded simulation, cloud-based modeling and simulation, multi-terminal modeling and simulation and model resource sharing, virtual-real combined modeling and simulation, human-process interaction in modeling and simulation, big data-driven hybrid modeling, and modeling and simulation combining artificial intelligence and swarm intelligence optimization technologies.
With the development of the manufacturing industry, modeling and simulation technology will play an increasingly important role. At the same time, due to the new characteristics of intelligent manufacturing systems, higher demands are being placed on simulation technology.
The establishment and management of digital models are a crucial foundation for manufacturing enterprises to achieve the digitalization of their manufacturing systems. Due to the complexity of the manufacturing process, digital models of the manufacturing lifecycle possess some new characteristics.
(1) The model is more complex. The number of constituent elements in the model is increasing, and the relationships between the elements are becoming more complex.
(2) The model has a longer lifespan. The model in the intelligent manufacturing system will participate in the entire product lifecycle. Due to the complexity of the relationships between model elements, the evolution process of the model will be very complex and highly uncertain.
(3) The models are highly heterogeneous. A large number of models are built by different organizations using different platforms, structures, development languages and databases.
(4) The credibility of the model is extremely difficult to assess. As the dependence on the model increases, the issue of model credibility becomes increasingly important. As the complexity of the model increases, assessing the credibility of the model becomes more difficult.
(5) Model reusability. In order to improve the efficiency and quality of model development, the role and value of model reuse have become more important.
