Quality assured flow production of light UHPC bar elements using artificial neural networks

使用人工神经网络对轻型 UHPC 棒元件进行有质量保证的流程生产

• 批准号: 423958617
• 负责人: Professor Dr.-Ing. Ludger Lohaus
• 金额: --
• 依托单位: Institut für Baustoffe
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/423958617?language=en
• 关键词: Quality; assured; flow; production; light

Construction is still strongly influenced by workmanship. The value of a construction project is mainly taking place at the construction. Due to external conditions such as weather, the construction process is prone to error. Furthermore, the dependence on the manual skill of construction workers leads in many cases to unsatisfactory results in terms of quality of the structures. Long construction times are yet another consequence of in-situ manufacturing. Precast concrete elements can solve a part of the production problems on site. However, similar complex production steps as with in-situ concrete construction (preparing reinforcement and formwork, stripping, cleaning formwork) lead to similar problems.A modular design should lead to faster and cheaper construction. By reducing the possible components to a catalog of elements, higher similarities can be possible, allowing an economic flow production process.Trusses with their nodes and bars should allow the erection of supporting structures with high precision in a short time. The focus of the proposed research is the production of bar elements by extrusion. Hollow cylinders made of ultra-high-strength concrete (UHPC) with carbon reinforcement, developed at the Institute for Building Materials (IfB), are extruded. This element is characterized by a particularly low weight, a high buckling stability and a ductile behavior. Extruding UHPC requires a high level of quality assurance. In the case of the proposed research project, a continuous, self-learning quality assurance in the form of an artificial neural network (ANN), which is to be developed by the Institute of Statics and Dynamics (ISD).The aim is to develop an extrusion process for UHPC that incorporates a sensor concept "from day one", i.e. from the first steps of manufacturing. The measured data is used by the ANN to monitor the production process. For this purpose, the ANN is trained by measured data obtained from “healthy” extrusion processes. Furthermore, errors are deliberately introduced in some processes to “train" the ANN to recognize such and recommend countermeasures. Finally, in a statistical analysis, the safety gain is quantified by the proposed quality assurance framework. The work program provides for the following work packages:-Development of a method of extruding UHPC-Development of a sensor concept- Experiments to UHFB extrusion- Training of the ANN- Simulation of erroneous processes.-Quantitative assessment of the safety gain

建筑业仍然受到工艺的强烈影响。建筑工程的价值主要发生在建筑工程中。由于天气等外部条件的影响，施工过程中容易出错。此外，对建筑工人手工技能的依赖在许多情况下导致结构质量方面的不令人满意的结果。建造时间长是原位制造的另一个后果。预制混凝土构件可以解决现场的部分生产问题。然而，与现场混凝土施工类似的复杂生产步骤(准备钢筋和模板、剥离、清理模板)会导致类似的问题。模块化设计应该会导致更快、更便宜的施工。通过将可能的构件减少到一个元素目录，可以实现更高的相似性，从而实现经济的流程生产过程。桁架及其节点和杆件应允许在短时间内以高精度安装支撑结构。建议研究的重点是挤压生产杆件。由建筑材料研究所(IFB)开发的由超高强混凝土(UHPC)和碳加固制成的空心圆筒被挤压。这种构件的特点是特别轻的重量、高的屈曲稳定性和延性。挤出超高性能混凝土需要高水平的质量保证。在拟议的研究项目中，将以人工神经网络(ANN)的形式提供持续的、自学习的质量保证，该网络将由静力学和动力学研究所(ISD)开发。目的是为超高性能混凝土开发一种“从第一天起”，即从制造的第一步开始，就纳入传感器概念的挤压工艺。神经网络利用测量数据对生产过程进行监控。为此，神经网络通过从“健康的”挤压过程中获得的测量数据进行训练。此外，在某些过程中故意引入错误，以便对ANN进行识别并建议对策。最后，在统计分析中，提出的质量保证框架量化了安全收益。工作计划规定了以下工作包：-开发挤出UHPC的方法-开发传感器概念-UHFB挤出的实验-ANN的培训-错误过程的模拟。-安全收益的定量评估