SMT Performance: Optimized in 8 Weeks

Control and optimization
Manufacturing

Challenge

The efficiency of the surface mount technology (SMT) process hinges on the strategic sequencing of electronic components on the printed circuit board (PCB). An inefficient sequence can significantly prolong the assembly time, impacting overall productivity. To overcome this challenge, advanced AI algorithms are essential for analyzing PCB layouts and determining the most effective component placement sequence.

*Surface Mount Technology (SMT) is a method in electronics manufacturing that involves directly mounting components onto the surface of printed PCBs

Approach

To tackle these challenges, MakinaRocks implemented its cutting-edge AI reinforcement learning model to optimize the sequence of mounting electronic components on PCBs. Prioritizing efficiency, the model aims to minimize the overall cycle time in the assembly process. Leveraging a sophisticated simulator, it is trained to derive a sequence that achieves quicker assembly, ultimately enhancing efficiency and productivity in PCB manufacturing.

Value Delivered

The implementation of the AI reinforcement model led to a significant reduction in the time required for the PCB production process by employing an efficient component mounting sequence. Moreover, the model is adaptable to other machines facing similar challenges, providing a universal solution to optimize the cycle time of the entire manufacturing process.

SMT Performance: Optimized in 8 Weeks

Challenge

Approach

Value Delivered

Related Use Cases

Robot Offline Programming Automation: From 6 Weeks to Days

EV HVAC Control Optimization: Energy Use Cut by 10%

Chip Design Automation: 85% Faster Execution, 49% Better Performance