This article discusses the use of digital twins for products made of polymer composite materials. The design of new products from polymer composite materials, both within the framework of the traditional and new direction of cloud production, requires the need to calculate the physical and mechanical characteristics of the product at the design stage. Carrying out full-scale tests increases greatly the cost and slows down the production. It requires the manufacture of a prototype of the product. The use of existing development tools does not always provide the required characteristics. To solve this problem, it is proposed to use a digital twin, which will not only solve the problem, but will also help to move to cloud production and the development of the Industry 4.0 direction. Thus, a new problem arises - how to create digital twins of products from polymer composite materials. The analysis makes it possible to conclude that the traditional methods of mathematical physics are not suitable for the solution of this problem, since the twins obtained with their help do not have any properties of adaptability. To solve the problem, it is proposed to use deep neural networks one of the most powerful methods of machine learning. This will make it possible to obtain digital twins of products made of polymer composite materials that can adapt to changes in the model, environmental conditions and adapt to changes in the indicators of sensors and transducers installed on the product.
翻译:文章讨论了使用数字双胞胎生产聚合复合材料的产品的问题。设计聚合复合材料的新产品,在传统和新云生产方向的框架内,都需要在设计阶段计算产品的物理和机械特性。全面测试极大地提高了成本,减缓了生产速度。全面测试需要制造产品原型。利用现有开发工具并不总是提供必要的特点。为了解决这个问题,建议使用数字双胞胎,这不仅将解决问题,而且将有助于向云生产和发展工业4.0方向发展。因此,出现了一个新问题:如何从聚合复合材料中产生数字双胞胎产品。分析使得可以得出结论,传统的数学物理学方法不适合解决这一问题,因为用双胞胎帮助获得的双胞胎并不具有任何适应性。为了解决问题,建议使用深层神经网络,这是最强大的机器学习方法之一。这将使得有可能获得数字双胞胎产品,从聚合复合材料中产生数字双胞胎,使已安装的复合材料的感应变感应到环境变化。