Call and compare the actual inspection images of the same detected point at the SPI, pre-reflow AOI, and after-reflow AOI, analyze the root cause of the problems (printing-mounting-reflow), and help formulate improvement measures.
Monitor and analyze SPI inspection data to improve solder paste printing quality and process parameters.
Through the collection and statistics of mounting data, our machine can position with a high frequency of offsetting and determine the cause of defects. At the same time, big data observation can find unstable equipment, track the effect of mounting machine maintenance and adjustment and provide evidence for maintenance.
DMS (Data Monitoring System) collects testing data and displays the production capacity, production quality, statistical defects, and completion of production tasks of each production line in the Kanban (billboard), as well as calculates the throughput rate and production line utilization rate, allowing for real-time visualization of production data, period-by-period statistics, and output report functions.
Guided by the final result of the general IPC after-reflow inspection standard, the closed-loop feedback mechanism of the whole line can provide feedback to the front-end SPI and the pre-reflow AOI, optimize and adjust the detection threshold, and finally realize the automatic adjustment of parameters, avoiding false alarms from front-end process detection and improving the detection efficiency of the entire line.
On the basis of realizing the linkage between testing equipment, we can increase the interaction between testing equipment and production equipment, and connect all equipment of the whole line in series. Through big data feedback and adjusting process parameters in real-timee, we can achieve feedback control of the whole line closed-loop, and finally reach the ultimate goal of building an unmanned, unstopped, no defects, low-cost, high efficient, high-quality smart factory.