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上海方驰新材料科技有限公司
聚焦方驰 · 洞见MIM前沿


Fangchi New Materials introduces MES system in the injection molding workshop, enabling real-time production process monitoring.

2025-06-07 21:54:58

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“ To enhance production transparency and quality traceability, Shanghai Fangchi New Material Technology Co., Ltd. recently fully deployed an MES (Manufacturing Execution System) in its injection molding workshop. The system covers 24 injection molding machines, enabling real-time collection of key process parameters such as temperature, pressure, and speed. The abnormal alarm response time has been reduced to within 3 seconds, and the yield rate of the first batch of pilot products has increased b ”

In early June 2025, Shanghai Fangchi New Material Technology Co., Ltd. announced the completion of the deployment and commissioning of an MES system in its injection molding workshop. This marks a significant step in the company's digital transformation, aimed at addressing issues in traditional production such as reliance on manual recording of process parameters, delayed anomaly detection, and difficulties in quality traceability.

The MES project was launched in March 2025 and took approximately three months. The project team networked 24 injection molding machines in the workshop (including brands such as Nissei and Haitian Changfeiya), installing data acquisition terminals on each machine and connecting them to a central server. The system can collect over a dozen key parameters, including injection speed, mold cavity temperature, holding pressure, and clamping force, at a frequency of once per second.

Production Supervisor Zhang Lei explained: 'Previously, parameters had to be manually recorded by operators every two hours. Not only was this inefficient, but by the time a problem was found, hundreds of defective parts had often already been produced. Now, the system automatically records data. Once a parameter exceeds the set range, the control room receives an immediate alarm, and operators can see the prompt on the machine screen.'

In the first month of the system's trial operation, taking the company's main product—the phone Type-C housing—as an example, the defect rate in the injection molding process dropped from 2.8% to approximately 1.3%. Zhang Lei attributed this decline to two main factors: first, the system can promptly detect and alert operators to parameter drift, allowing for quick adjustments; second, the parameter data recorded by the MES system provides a basis for process optimization. After analysis, engineers discovered common fluctuations in the holding pressure curves of several machines, and targeted adjustments resolved the issue.

In addition to quality improvements, the MES system has also enhanced management efficiency. Production planners can view the real-time status of each machine (running, waiting for material, fault, mold change) in the system, enabling more reasonable scheduling. The equipment maintenance module records the maintenance cycle and repair history of each machine and automatically reminds staff when maintenance is due.

The company's deputy general manager stated that the MES system in the injection molding workshop is a pilot project. If it runs smoothly, the debinding, sintering, and post-processing stages will also be integrated into the system in the second half of 2025, ultimately achieving full-process digital traceability to meet the strict batch traceability requirements of automotive and medical clients.

Currently, the MES system has been integrated with the company's existing ERP system, allowing managers to view the entire process from order to production on a single platform.


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Fangchi New Materials introduces MES system in the injection molding workshop, enabling real-time production process monitoring.
To enhance production transparency and quality traceability, Shanghai Fangchi New Material Technology Co., Ltd. recently fully deployed an MES (Manufacturing Execution System) in its injection molding workshop. The system covers 24 injection molding machines, enabling real-time collection of key process parameters such as temperature, pressure, and speed. The abnormal alarm response time has been reduced to within 3 seconds, and the yield rate of the first batch of pilot products has increased b
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