Ship port machinery forging is an important part of ship manufacturing process, its quality and performance are directly related to the safety and stability of ship operation. With the development of industrial big data technology, analyzing and optimizing the data of ship port machinery forgings has become a research direction with practical application value. This paper will introduce the application of industrial big data in the mechanical forgings of ship ports, and put forward an optimization scheme for the deficiency of current forgings.
In the manufacturing process of ship port machinery forgings, a large amount of data is generated in each link, such as design, materials, production, testing and so on. These data include both structured data, such as production records, product parameters, etc., and unstructured data, such as drawings, pictures, videos, etc. Using industrial big data technology, these data can be deeply analyzed and valuable information can be extracted.
In the process of industrial big data analysis, cluster analysis, association rule mining, time series analysis and other methods can be used to analyze the historical data of ship port mechanical forgings and find its inherent laws and characteristics. For example, through the analysis of the temperature, pressure, deformation and other data of the forging in the production process, the influence law of different process parameters on the performance of the forging can be obtained, which provides a basis for optimizing the manufacturing process.
Based on the results of industrial big data analysis, an optimization scheme can be proposed for the deficiencies of current forgings. Here are some possible optimizations:
Optimization of process parameters: According to the data analysis results, the forging, heat treatment and other process parameters are optimized to improve the quality and performance of the forging.
Improve the design scheme: Through the analysis of the design data, the deficiencies in the design scheme are found, and the improvement measures are put forward to improve the design efficiency and quality of the forging.
Improve inspection efficiency: Use big data technology to conduct in-depth analysis of inspection data, improve the accuracy and efficiency of inspection, and reduce the rejection rate.
Intelligent early warning: Through real-time monitoring and analysis of data in the production process, intelligent early warning can be realized, and potential problems can be discovered and solved in time to avoid the occurrence of production accidents.
This paper introduces the application and optimization scheme of industrial big data in ship port machinery forging. Through data analysis, the inherent laws and characteristics of forgings can be obtained, and then targeted optimization measures are proposed. The implementation of these optimization measures can improve the quality and performance of forgings, reduce manufacturing costs, and provide strong support for the sustainable development of the shipbuilding industry.
With the continuous development of industrial big data technology, its application in ship port mechanical forgings will be more and more extensive. Through the in-depth analysis and mining of the data in the production process, the manufacturing process can be further optimized, production efficiency can be improved, energy consumption can be reduced, and green manufacturing can be realized. At the same time, the use of big data technology can also strengthen quality management and monitoring, improve the reliability and stability of products, and provide help for the quality improvement and brand building of enterprises. Therefore, the application of industrial big data in ship port machinery forging has great significance and broad prospects.
