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Predictive model-based quality inspection using Machine Learning and Edge Cloud Computing

机译:采用机器学习和边缘云计算的预测模型的质量检验

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The supply of defect-free, high-quality products is an important success factor for the long-term competitiveness of manufacturing companies. Despite the increasing challenges of rising product variety and complexity and the necessity of economic manufacturing, a comprehensive and reliable quality inspection is often indispensable. In consequence, high inspection volumes turn inspection processes into manufacturing bottlenecks. In this contribution, we investigate a new integrated solution of predictive model-based quality inspection in industrial manufacturing by utilizing Machine Learning techniques and Edge Cloud Computing technology. In contrast to state-of-the-art contributions, we propose a holistic approach comprising the target-oriented data acquisition and processing, modelling and model deployment as well as the technological implementation in the existing IT plant infrastructure. A real industrial use case in SMT manufacturing is presented to underline the procedure and benefits of the proposed method. The results show that by employing the proposed method, inspection volumes can be reduced significantly and thus economic advantages can be generated.
机译:提供缺陷的高质量产品是制造公司长期竞争力的重要成功因素。尽管产品种类越来越多的挑战越来越多,但经济制造的复杂性和必要性,但全面可靠的质量检验往往是不可或缺的。结果,高检量将检验过程转化为制造瓶颈。在这一贡献中,利用机器学习技术和边缘云计算技术,研究了工业制造中预测模型的质量检测的新综合解决方案。与最先进的贡献相比,我们提出了一种整体方法,包括面向目标的数据采集和处理,建模和模型部署以及现有IT植物基础设施中的技术实现。提出了SMT制造中的真正工业用例,以强调所提出的方法的程序和益处。结果表明,通过采用所提出的方法,可以显着降低检查量,因此可以产生经济优势。

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