Advanced Engineering Research (Mar 2020)
On solving problems of operational forecasting of main pipeline weld joint quality
Abstract
Introduction. Since welding is the only means to connect pipe lengths into a continuous line when constructing main pipelines, modern quality management systems for the welding industry products are based on minimizing the occurrence of specific defects. This is achieved through monitoring and documenting welding procedures.Materials and Methods. The analysis of monitoring systems customized for manual, mechanized and automatic orbital welding has shown that the industry urgently needs systems that not only control and document the welding process, but also predict the quality of weld joints. This actualizes the need to develop an intelligent module that could, basing on real-time monitoring results, predict the quality of welded joints on the fly.Results. Since the theoretical connection between the forecasting results and weld quality attributes is characterized by the interaction of a significant number of physical phenomena continuous in time, the results of welding can be described only by a sufficiently complete nonstationary physicomathematical model of the welding process. However, in order to be able to predict the results of welding directly during the monitoring of the process, a simplified forecasting model is proposed whose key feature is the ability to perform calculations synchronously with the real process, which is implemented in a real-time mode with a given interval.Discussion and Conclusions. The major obstacle to the successful functioning of the operational forecasting module, apart from the length of the numerical solution of equations, is an estimation error. To ensure the minimum error of virtual display during simplification, it is necessary to conduct comprehensive studies of the significance and influence of individual factors and phenomena on quality attributes. These observations determined the content and sequence of work on the creation and implementation of an intelligent module for the operational forecasting of welding quality. Undoubtedly, the information on the forecasting of the weld joint quality should enter a higher-level pipeline quality management system, as well as be analyzed by construction organizations in order to develop preventive measures to improve the organization and performance of welding work.
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