Towards a Smart Electronics Production Using Machine Learning Techniques

被引：2

作者：

Seidel, Reinhardt ^{[1
]}

Mayr, Andreas ^{[1
]}

Schaefer, Franziska ^{[1
]}

Kisskalt, Dominik ^{[1
]}

Franke, Joerg ^{[1
]}

机构：

[1] Friedrich Alexander Univ Erlangen Nuremberg FAU, Inst Factory Automat & Prod Syst FAPS, Nurnberg, Germany

来源：

2019 42ND INTERNATIONAL SPRING SEMINAR ON ELECTRONICS TECHNOLOGY (ISSE) | 2019年

关键词：

STENCIL; OPTIMIZATION;

D O I：

10.1109/isse.2019.8810176

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

High quality and low costs are main drivers in electronics production. Regardless of the application, the trend towards batch size 1 heavily challenges current production systems. With higher data availability, the application of machine learning (ML) has great potential for the future of electronics production. Therefore, this paper gives an overview about exemplary investigations of ML techniques in the assembly of surface mount devices (SMD) and shows the need for a systematic proceeding when searching for profitable ML use cases. In doing so, a process-oriented methodology for the identification of ML use cases is derived, paving the way towards a smart electronics production.

引用

页数：6

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