I. Introduction

In recent years, big data technology has been widely used and developed in various fields, especially in the manufacturing industry. The application of big data has become an important means to improve production efficiency and reduce production costs. In the die-casting industry, the application of big data is also increasingly showing its importance. A die-casting machine is a mechanical device that uses high pressure to inject molten metal or alloy liquid into a mold at high speed. After certain process conditions, it is cooled and solidified to obtain parts of various shapes and properties. Traditional die-casting machines often rely on the experience and intuition of operators for production and management. This method is not only inefficient, but also prone to quality problems. The introduction of big data technology can realize real-time monitoring and intelligent decision support for die-casting machines, thereby improving production efficiency, reducing production costs, optimizing the production process, and improving product quality.

2. Application of big data in die casting machines

2.1 Real-time monitoring

Through sensors and controllers installed on the die-casting machine, various parameter data, such as temperature, pressure, speed, current, etc., can be collected in real time. These data can be transmitted to the cloud database in real time through the Internet of Things technology to realize real-time monitoring of the die-casting machine. Through the analysis and processing of these data, abnormal conditions of the equipment can be discovered in time, and early warning and maintenance can be carried out in advance to avoid the impact of equipment failure on production.

2.2 Intelligent prediction

Based on big data and machine learning methods, various parameters in the die-casting process can be predicted and analyzed, such as predicting the quality, performance and defects of castings. These prediction results can help engineers optimize the die-casting process, improve product quality, and reduce production costs.

2.3 Optimize production process

By analyzing a large amount of production data, bottlenecks and problems in the production process can be discovered, thereby optimizing the production process and improving production efficiency. For example, data analysis can be used to discover which parameters have the greatest impact on product quality, and then these parameters can be adjusted to optimize the production process.

3. Intelligent decision support system

Intelligent decision support systems based on big data can provide real-time and accurate decision-making information to help engineers make better decisions. This kind of system usually includes four parts: data collection, data processing, data analysis and decision-making suggestions.

3.1 Data collection

Data collection is the basis of intelligent decision support systems. In the die-casting industry, data collection is primarily accomplished through sensors and controllers installed on die-casting machines. These data include equipment operating status, process parameters, product quality, etc.

3.2 Data processing

Data processing mainly involves cleaning, integrating and storing the collected data. Cleaning is mainly to remove noise and outliers in the data; integration is to unify data from different sources; storage is to store the processed data in the database to facilitate subsequent analysis and use.

3.3 Data analysis

Data analysis is to conduct in-depth analysis and mining of processed data based on preset models and algorithms to derive valuable information and knowledge. In the die casting industry, the main goals of data analysis are to predict product quality, optimize production processes and improve equipment efficiency.

3.4 Decision-making suggestions

Decision-making suggestions are provided to engineers based on the results of data analysis. These suggestions can be about the adjustment of equipment parameters, changes in process flow, equipment maintenance, etc. By using intelligent decision support systems, engineers can manage and control the production process more scientifically, improve production efficiency, and reduce production costs.

4. Conclusion

With the advent of the era of Industry 4.0 and big data, the application of big data in die casting machines is becoming more and more extensive. Through real-time monitoring, intelligent prediction and optimization of production processes, big data can improve the production efficiency of die-casting machines, reduce production costs, optimize production processes and improve product quality. At the same time, through intelligent decision support systems, engineers can better manage and control the production process, improve production efficiency, and reduce production costs. Therefore, big data-driven intelligent decision support systems are of great significance to the development of die-casting machines.


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