Defect patterns study of pick-and-place machine using automated optical inspection data

Abstract

Purpose This paper aims to study the component pick-and-place (P&P) defect patterns for different root causes based on automated optical inspection data and develop a root cause identification model using machine learning. Design/methodology/approach This study conducts experiments to simulate the P&P machine errors including nozzle size and nozzle pick-up position. The component placement qualities with different errors are inspected. This study uses various machine learning methods to develop a root cause identification model based on the inspection result. Findings The experimental results revealed that the wrong nozzle size could increase the mean and the standard deviation of component placement offset and the probability of component drop during the transfer process. Moreover, nozzle pick-up position can affect the rotated component placement offset. These root causes of defects can be traced back using machine learning methods. Practical implications This study provides operators in surface mount technology assembly lines to understand the P&P machine error symptoms. The developed model can trace back the root causes of defects automatically in real line production. Originality/value The findings are expected to lead the regular preventive maintenance to data-driven predictive and reactive maintenance.

Parameter optimization for surface mounter using a self-alignment prediction model

“…After implementing the two kernel functions, it was found that the SVR-RBF model was more effective for predictions compared to the SVR-Linear model. Cen et al (2022) presented P&P defect patterns for different root causes based on automated optical inspection data and developed a root cause identification model using a machine learning. Carvalho et al (2022) presented a study to determine the minimum force required to pull out a surface mount component from a PCB during the wave soldering process.…”