Abstract With the growing interest in smart factories, defect-prediction algorithms using data analysis techniques are being developed and applied to solve problems caused by defects at manufacturing sites. Cost benefit is an important factor to consider, and can be obtained by applying such algorithms. Existing defect-prediction algorithms usually aim to reduce the error rate of the prediction model, rather than focusing on the cost benefit for the practical application of defect-prediction models. Therefore, this study develops a defect-prediction algorithm considering costs and systematization for field application. To this end, a type error-weighted deep neural network (TEW-DNN) is proposed that applies a loss function to set a different weight for each type error, and cost analysis is conducted to search the optimal type error weight. A cost analysis-based defect-prediction system is designed considering the TEW-DNN algorithm and a cyber-physical system environment. The efficacy of the designed system is demonstrated through a case study involving the application of the system in a die-casting factory in South Korea.

中文翻译：

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基于类型误差加权深度神经网络算法的基于成本分析的缺陷预测系统的开发

摘要 随着人们对智能工厂的兴趣日益浓厚，使用数据分析技术的缺陷预测算法正在开发和应用，以解决制造现场由缺陷引起的问题。成本效益是一个需要考虑的重要因素，可以通过应用此类算法来获得。现有的缺陷预测算法通常旨在降低预测模型的错误率，而不是关注缺陷预测模型实际应用的成本效益。因此，本研究开发了一种考虑成本和系统化的现场应用缺陷预测算法。为此，提出了一种类型错误加权深度神经网络（TEW-DNN），它应用损失函数为每种类型错误设置不同的权重，并进行成本分析以寻找最优类型错误权重。考虑到 TEW-DNN 算法和信息物理系统环境，设计了基于成本分析的缺陷预测系统。设计系统的功效通过一个案例研究来证明，该案例涉及该系统在韩国压铸厂的应用。