Towards a Smart Electronics Production Using Machine Learning Techniques

Reinhardt Seidel, A. Mayr, Franziska Schäfer, Dominik Kißkalt, J. Franke
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引用次数: 4

Abstract

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.
利用机器学习技术实现智能电子产品生产
高质量和低成本是电子产品生产的主要驱动力。无论应用是什么,批量大小为1的趋势对当前的生产系统构成了严重挑战。随着数据可用性的提高,机器学习(ML)的应用对电子产品的未来具有巨大的潜力。因此,本文概述了表面贴装设备(SMD)组装中ML技术的示例性调查,并表明在搜索有利可图的ML用例时需要进行系统的程序。在此过程中,导出了用于识别ML用例的面向过程的方法,为智能电子产品的生产铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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