The customer is a private joint-stock enterprise specializing in R&D and manufacturing of planetary transmission assemblies, high-precision synchronizer assemblies, differentials, limited-slip differentials and related products. With more than 2000 employees and an annual output value of 1.5 billion RMB, the company has more than 800 sets of advanced equipment at home and abroad, with an annual production capacity of 5 million planetary transmission assemblies and over 20 million various automotive parts through mechanical processing.
1. Project Overview
This project aims to create a complete intelligent manufacturing system for the customer, achieving digital, networked and intelligent management of the production process. Through the implementation of the WIMI-CPS system, it helps customers improve production efficiency, reduce operating costs, and improve product quality.
2. MDC Data Collection System
Through the MDC data collection system, real-time collection of CNC machine tool operation status, processing parameters, alarm information and other data, analysis of key indicators such as equipment utilization rate, operation efficiency, and failure rate, providing data support for equipment maintenance and management.
3. Tool Monitoring System
The tool monitoring system performs real-time monitoring of tool usage status in machining centers, achieving tool life early warning, tool replacement reminders and other functions, effectively reducing processing accidents caused by tool problems and improving processing quality and efficiency.
4. Production Kanban System
The production Kanban system realizes visual management of the production site, through electronic Kanban real-time display of production progress, quality status, equipment operation status and other information, allowing management personnel to have a clear picture of production situations.
5. Project Results
After project implementation, equipment utilization rate increased by more than 15%, product defect rate decreased by 30%, production efficiency improved by 20%, achieving the expected intelligent transformation goals.