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.

中文翻译：

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基于自动光学检测数据的贴片机缺陷模式研究

目的

本文旨在基于自动光学检测数据研究针对不同根本原因的组件取放( P&P) 缺陷模式，并使用机器学习开发根本原因识别模型。

设计/方法/方法

本研究通过实验模拟 P&P 机器误差，包括喷嘴尺寸和喷嘴拾取位置。检查具有不同误差的元件放置质量。本研究使用各种机器学习方法来开发基于检查结果的根本原因识别模型。

发现

实验结果表明，错误的吸嘴尺寸会增加元件贴装偏移的均值和标准偏差以及转移过程中元件掉落的概率。此外，吸嘴拾取位置会影响旋转的元件放置偏移。这些缺陷的根本原因可以使用机器学习方法进行追溯。

实际影响

这项研究为表面贴装技术装配线的操作员提供了了解 P&P 机器错误症状的方法。开发的模型可以在实际生产线中自动追溯缺陷的根本原因。

原创性/价值

预计这些发现将引导定期预防性维护到数据驱动的预测性和反应性维护。