Auto parts manufacturing
Intelligent Factory Data Application Solution — — Beijing Futian Cummins Engine Co., Ltd.

Beijing Foton Cummins is the world's leading diesel engine manufacturer, providing diesel engines to major commercial vehicle OEMs, including Foton and other global customers. Cummins has the following requirements for data mining in machining:

1. Timely control of variations in machine tools and robots during use to avoid quality and equipment abnormalities.

2. In-depth analysis of processes and equipment to enable more effective use and support MES in making higher-value decisions.

3. Support for a comprehensive data management system for the entire lifecycle of engine products to drive new operating models and industry revolution.

To enable data mining in machining, the following requirements are identified:

Real-time control of variations during the use of machine tools and robots to prevent quality and equipment abnormalities.In-depth analysis of processes and equipment to optimize their usage and support MES in making high-value decisions.Support for a comprehensive data management system for the entire lifecycle of engine products to drive new operating models and industry revolution.

Through intelligent monitoring and analysis of machining process data, our customers have achieved tangible benefits on their 33 CNC machines at the production site. The specific benefits are as follows:

1. Tool cost: Maximizing tool life is expected to increase by 10%, reducing tool cost by 2 yuan per unit. This results in an annual savings of 430,000 yuan.

2. Unexpected collision downtime and spare parts cost: Since 2017, there have been 11 incidents of unexpected downtime due to collisions between the robotic arms and the main spindle, totaling approximately 4,500 minutes. On average, there are 4 incidents per year, resulting in a cost of approximately 300,000 yuan for spare parts and downtime.

3. Efficiency improvement: By utilizing temperature and vibration data collected from intelligent terminals to guide the warm-up process, the warm-up time has been reduced from 30 minutes to approximately 2 minutes. This results in an annual saving of 23,000 minutes across the 33 CNC machines, improving efficiency and reducing energy waste.

4. Process quality control: By monitoring spindle power and vibration, we can identify machining quality issues caused by unexpected tool breakage, tool wear, equipment status, and material variations.

5. Equipment state prediction: By continuously monitoring equipment vibration, temperature, power, and CNC system data, we can comprehensively assess the equipment's state and provide guidance for maintenance planning and spare parts preparation. This helps to reduce unexpected downtime and maintenance time.

These objective benefits demonstrate the effectiveness of our intelligent monitoring and analysis system in optimizing production processes and improving overall efficiency.
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