ASMPT Enhances SMT Analytics with AI-Powered Line Optimization

[Cengiz Özemli]

## ASMPT Enhances SMT Analytics with AI-Powered Line Optimization

ASMPT has expanded its SMT Analytics software with new AI-powered analysis functions, aiming to increase production transparency and line efficiency in surface mount technology (SMT) manufacturing.

The latest version introduces the Line Balancing Analysis feature, which comprehensively evaluates time utilization across all SMT lines. Additionally, existing features such as theoretical cycle time comparison and rejected parts analysis have been enhanced to provide deeper operational insights.

### Line-Level Transparency and Bottleneck Detection

SMT Analytics collects production data from multiple lines and compares it with theoretically optimal reference values. The goal is to uncover bottlenecks, unbalanced line structures, and hidden optimization opportunities, even in complex and high-mix production environments.

Line Balancing Analysis instantly reveals performance-limiting points by comparing the actual cycle time at each station with reference values calculated by the WORKS Programming software. This allows manufacturers to clearly analyze line balance and the impact of different programs.

### Enhanced Theoretical Cycle Time Comparison

The new version also identifies deviations in programming parameters such as waiting times and acceleration settings. Since these parameters affect placement cycles thousands of times, even small adjustments can lead to significant efficiency gains.

### Cost-Based Rejected Parts Analysis and Maintenance Integration

The enhanced Rejected Parts Analysis not only considers rejection rates but also component costs. This allows manufacturers to more accurately measure the economic impact of defects.

Working in integration with the Factory Equipment Center, the system provides access to maintenance data such as feeder status, cycle counter, and service intervals from within the analysis environment. This supports decision-making processes by evaluating quality, cost, and maintenance performance together.

### AI-Powered Reporting and Recommendations

One of the most significant advancements is the addition of AI-powered reporting. The integrated assistant automatically analyzes production data and provides prioritized and structured improvement recommendations. This reduces the manual interpretation of data dashboards and accelerates action processes.

SMT Analytics also supports the integration of third-party machines via the IPC-2591 Connected Factory Exchange (CFX) protocol, enabling consistent and line-level optimization even in diverse production environments.

According to ASMPT, this expanded solution supports electronics manufacturers in their transition to smart and AI-powered SMT production by translating deep process expertise into practical and data-driven improvements.