With the rapid development of manufacturing industry, warm forging technology, as an important forming process, has been widely used in the manufacture of various high-quality forgings. However, the traditional method of temperature forging process monitoring and quality management has been unable to meet the modern manufacturing industry’s demand for high efficiency, high quality and low cost. Therefore, this paper discusses the application of big data-driven warm forging process monitoring and quality optimization management in order to improve production efficiency and product quality and reduce production costs.
The process of warm forging involves many links and many parameters, such as heating temperature, holding time, forging pressure and so on. These parameters interact with each other, and a mistake in any one link can lead to the failure of the entire production batch. The traditional temperature forging production process monitoring mainly relies on manual experience and regular inspection, can not grasp all kinds of information in the production process in real time, can not optimize the management of the production process. Therefore, it is necessary to introduce big data technology to solve these problems.
Big data driven warm forging production process monitoring
Data acquisition and storage: Through sensors, PLC and other equipment real-time acquisition of various data in the production process of warm forging, such as temperature, pressure, displacement, etc., and store these data in the database. These data can provide a data base for the subsequent production process analysis and optimization.
Data preprocessing: Preprocessing the collected data, including data cleaning, data conversion and data compression, to improve the quality and availability of data. With data preprocessing, outliers, missing values, and redundant data can be removed, data can be converted into a format suitable for analysis, and data can be compressed to save storage space.
Real-time monitoring and early warning: Through real-time monitoring of various parameters and states in the production process, abnormal conditions in the production process can be found in time and early warning. For example, when the data of a sensor is abnormal, an alarm can be triggered and processed accordingly. Through real-time monitoring and early warning, problems can be found and solved in time to avoid losses in the production process.
Data visualization: Through data visualization technology, various data in the production process can be displayed in the form of charts, curves, etc., which is convenient for managers and operators to view and analyze. Through data visualization, you can intuitively understand the various situations in the production process, find problems in time and deal with them.
Hot forging quality optimization management driven by big data
Quality data analysis: Through in-depth analysis of the quality data collected during the production process, key factors and potential problems affecting product quality can be found. For example, by analyzing the qualified rate and defective rate of the product, the main reasons affecting the quality of the product can be found out and improved.
Quality prediction and prevention: Through the analysis of historical quality data and the application of machine learning algorithms, the quality of products can be predicted and prevented. For example, by establishing a quality prediction model, the quality of products in the future can be predicted and adjusted and optimized accordingly. Through quality prediction and prevention, problems can be found and solved in advance to avoid the occurrence of quality problems.
Quality optimization decision: Through the comprehensive analysis and mining of various data in the production process, it can provide data support for quality optimization decision. For example, by analyzing the relationship between various parameters and states in the production process and product quality, the best process parameters and production conditions can be found and optimized. Through quality optimization decision-making, product quality and production efficiency can be improved, and production costs can be reduced.
Intelligent scheduling and collaboration: By combining big data with intelligent scheduling algorithms, intelligent scheduling and collaboration in the production process can be realized. For example, through the optimal allocation and scheduling of production planning and production resources, efficient collaboration and resource sharing of production processes can be achieved. Through intelligent scheduling and collaboration, production efficiency and quality stability can be improved.
Big data-driven process monitoring and quality optimization management of warm forging is one of the important trends in the development of modern manufacturing industry. By combining big data technology with warm forging production process, real-time monitoring and early warning, quality data analysis, quality prediction and prevention, intelligent scheduling and collaboration can be realized in the production process, so as to improve production efficiency and quality stability, reduce production costs and provide strong support for the sustainable development of enterprises.
