Today, companies are using digital twins in a variety of ways. In the automotive and aircraft manufacturing fields, digital twin technology is gradually becoming an important tool for optimizing the entire product manufacturing value chain and innovating products; in the energy field, oilfield service operators are obtaining and analyzing a large amount of data from wells to create digital twin models to guide drilling and oil and gas transportation operations in real time; in the healthcare field, cardiovascular researchers are creating highly simulated digital twins of the human heart; as a typical case of smart city management, Singapore uses detailed virtual city models for projects such as urban planning, maintenance, and disaster emergency warning. Since the concept of digital twins was introduced, digital twin technology has been evolving rapidly, giving a huge boost to both product design, manufacturing and service quality. The popularization and application of digital twin technology will greatly promote the development of enterprises in the digital, networked two levels, help enterprises to accelerate the process of digital intelligent manufacturing transformation, for enterprises to achieve digital intelligent manufacturing transformation and upgrading of empowerment.
The rapid development and application of digital twin technology in the manufacturing industry has not only changed the traditional production methods of manufacturing enterprises, but also promoted the reconstruction of enterprise management mode and organizational form.
The development and application of digital twin technology in the manufacturing industry mainly reflects the following characteristics:
First, the simulation of the mirror
In the product or even the production line and the factory formally manufactured, built before, can be digitized through the design, simulation, and simulation of the entity, the output of 3D, holographic images and other forms of design drawings and manufacturing manuals. The simulation and analysis process forms a complete digital virtual file of the product, which includes not only the structure and function of the product, but also the material, process and flow, and is a digital information integration of the trinity of manufacturing material, manufacturing process and manufacturing result, which is not only a description and record of the current status of the manufacturing, but also realizes the tracing back of the source of the quality problem, for example, which circuit board a certain capacitance is used in, and so on. The new technology also allows the user to easily visualize and record the process of manufacturing a micro turbojet engine, such as whether the production process specification is scientific or not, and whether the operation effect is up to the standard or not.
The second is dynamic mapping
The traditional digital manufacturing technology mainly stays in the simulation mirror stage, and does not realize the manufacturing process as well as put into use after the data interaction between the entity and the virtual body. With the depth of IoT technology, through real-time intelligent sensing and monitoring, the dynamic information of the manufacturing entity changes can be captured, and timely feedback to the twin virtual body for recording and correction, through the calculation and validation, to reduce the risk to reduce the error, and through the dynamic adjustment, virtual and real synchronization, to achieve the mutual twinning of the whole life cycle of the product, and the **** the same growth.
Third, quality prediction
Through the accumulation of big data of the whole life cycle of the product and the application of technologies such as deep autonomous learning of machines, the errors and pain points of the manufacturing process and the use of the link are found in a timely manner, and adjustments are made and verified in the twinned virtual body, which effectively assesses and improves the quality of the product's product management level, and reduces the cost of research and development of the enterprise and the
These are the first time that we've seen this.
The same digital twin technology in the digital transformation and upgrading process to help manufacturing enterprises, the quality management of the enterprise business field has also produced great changes and impact:
IoT technology based on the realization of a fully interconnected product development and manufacturing ecosystem, which includes people, products, machines and related systems to achieve the quality of the information without barriers to the flow;
IoT devices, chips, information flow, artificial intelligence implanted in traditional product hardware, greatly improving the functionality of the product, but also poses a challenge to product quality, reliability and risk management;
In addition to the traditional methods of quality management, many high-tech methods, such as cloud computing, in-depth learning, artificial intelligence, VR/AR into the practical, although powerful, but the misuse, the risk of abuse is high;
The increase in high-performance equipment, instruments, and components in the manufacturing process creates new capabilities and new problems. For example, accurate, fast 3D scanner, can quickly generate accurate computer image model of the processing parts, can replace the traditional point measurement for quality management, but at the same time need to get out of the "image-based quality management process" ...
Based on the above changes in the field of quality management business and the impact of quality management, but also at the same time, to the quality of the enterprise management work to bring about a different kind of change:
1, the future is the era of personalized customization and flexible production of services, the focus of the enterprise quality management work is no longer the control of quality defects and failures, but the customer value of the continuous perception, satisfaction and enhancement;
2, IoT, big data, cloud computing, artificial intelligence, and other digital twins to enable the application of technology, to achieve the product lifecycle of all phases of the information of efficient interconnectivity, but also prompted the team of enterprise quality management personnel will be extended to the IT department, data analysts, industrial Internet platform managers, etc.;
3, in the personalized customized production services, product quality is more embodied in the creation of value for the end-consumers, the digital twin factory to allow customers to participate in the whole process of product research and development, design, production and other processes, product design and innovation from the enterprise as the
4, the enterprise quality management personnel's thinking, ability and methods will also change:
Fast and high-quality ability to deal with massive customer information;
The ability to improve and meet the perceived value of the customer;
The ability to calibrate data quality and data;
The ability to manage the quality of embedded systems;
The ability to predictive quality control and equipment maintenance;
The ability to manage and control the risk control system;
...
As the next generation of information technology and digital technology advances rapidly, making the Digital twins become possible, but also make the enterprise product quality traceability, quality monitoring, quality warning, quality repair become more timely, accurate and effective, to achieve from physical tracking to the virtual mirror of the historic leap, will be the enterprise product quality evaluation or "customer value perception" to bring subversive breakthroughs. I personally understand that in the digital twin manufacturing new business, the future direction of the focus of the work of enterprise quality management will be as follows:
First, quality management strategy and planning
The future of quality management will no longer be to reduce the error, but how to create value for the customer. In the face of the future development trend of personalized demand, the core of the enterprise quality strategy is the creation of digital twin quality platform to achieve enterprise quality management platform. In addition to cost reduction, the application of digital twin technology will also lead to increasingly fierce market competition, product homogenization further aggravated, and the Matthew effect further amplified. In this case, the enterprise quality strategy and planning work should be a step ahead of others, through the high-quality word of mouth to seize the first opportunity, and gradually build the industrial quality management ecological chain, set the ecological chain of wisdom, to create the enterprise quality competitive advantage.
The construction of product technology and quality standard system
The product technology and quality standard system is the reference basis for the selection of quality management tools and methods, and is also the target direction for the implementation of all quality management work. In particular, the application of digital twin technology has brought disruptive changes to the management of the entire life cycle of the product, so enterprises need to be based on the characteristics of the application of digital twin manufacturing technology, to build a standard system that is suitable for and meets the needs of the enterprise's quality management work.
Control of data modeling and quality of interaction
In the chain of product development, procurement, manufacturing, quality management and delivery, the interaction of data in each chain will bring a lot of challenges. It directly affects the quality of the final product, as well as the efficiency of quality management throughout the product lifecycle.
With the addition of new technologies such as digital twins and additive manufacturing, data types are richer, data capacity is growing geometrically, the timeliness of data interactions is required to be higher, and the tools for data value and application analysis are more advanced, which at the same time brings the requirement for quality control of updating the data standards and updating the tools. Take additive manufacturing as an example, the product data in the R&D and manufacturing process can be calculated in G at any time; due to the application of sensors, machine vision, artificial intelligence and digital twins, the data transmission process is also transformed from the traditional unidirectional low-speed control commands to bi-directional high-speed transmission of control commands and process inspection data; and the large amount of data accumulated in the production process, in addition to the product quality assurance, in the areas of reliability management in its lifecycle and In addition to product quality assurance, the large amount of data accumulated in the production process will play an important value in the areas of reliability management and predictive maintenance during its life cycle.
The development of standards for digital twins and additive manufacturing related to data modeling and interactive collaborative control has become a core research topic for international standardization organizations such as ISO and China's 14th Five-Year Plan for digital manufacturing.
Fourth, quality fluctuation simulation modeling and analysis of the key process chain
In the context of economic globalization and accelerated digital transformation and upgrading of enterprises, the market competition has changed from inter-enterprise competition to quality competition based on the products of the key process chain, and the focus of the enterprise's attention to the key process chain has been extended from the traditional product optimization, cost optimization. The focus of enterprises on the key process chain has been extended to quality assurance, forward design process and risk control, etc. Any quality fluctuation in the key process chain may bring significant economic losses and market risks for enterprises. Therefore, modeling the quality fluctuations of key process chains to predict and control the possible quality fluctuations and their impact range is a key element of product quality management in the new digital twin manufacturing industry.
V. Precise control of the quality of enterprise product innovation
Based on the application of data from the digital twin of the entire product lifecycle, it can assist enterprises in accurately locking the user and expressing the user's needs, and then innovate a new product in accordance with the user's needs, and simulate the test and produce it through the digital twin platform for the consumer experience and value The digital twin platform simulates and tests a new product and produces it for consumers to experience and perceive its value, significantly reducing the product development cycle and realizing the accurate market launch of the product. This is also a new business model: simulate and optimize the production process in the digital world to reduce costs and improve efficiency.
Sixth, predictive maintenance quality management of the product use process
Based on the use of the product in the process of big data AI model, mechanism model, the failure of the depth of the fusion of knowledge base, the construction of the product use of the process of the twin platform, so that the enterprise after-sales personnel can accurately identify the product after the sale of the faulty parts, failure mode, failure causes, failure level and improvement measures, and automatically generate the product use of the twin platform. The platform enables the enterprise after-sales personnel to accurately identify the product failure parts, failure modes, failure causes, failure levels, and improvement measures, and automatically generates detailed product after-sales diagnosis reports.
The above is a simple reflection based on personal knowledge, welcome to exchange and correction!
The above is a brief thought based on personal knowledge.