Rework for defective items is very common in practical shopfloors; however, it generally causes unnecessary energy consumptions and operational costs. In order to address this problem, we propose a novel approach called the intelligent rework process management (i-RPM) system. The proposed system is based on intelligent rework policy, which provides a preventive rework procedure for items with latent defects. Such items can be detected before quality tests by applying conventional classification techniques. Moreover, training sets for the classification algorithms can be collected by using modern information and communications technology (ICT) infrastructures. Items with latent defects are not allowed to proceed to the following processes under intelligent rework policy. Instead, they are returned to the preceding processes for rework in order to avoid unnecessary losses on the shopfloor. Consequently, the proposed system helps to achieve a sustainable manufacturing system. Nevertheless, misclassification by the classification model can degrade the performance of intelligent rework policy. Therefore, the i-RPM system is designed to compare rework policies based on classification accuracy and choose the best one of them. For illustration, we applied the i-RPM system to the rework procedure of a steel manufacturer located in Busan, South Korea, and our experiment results revealed that the cost reduction effect of the intelligent rework policy is affected by several input parameters.

中文翻译：

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智能工厂环境下的智能返修流程管理系统

在实际车间中，对有缺陷的物品进行返工非常普遍。然而，它通常会导致不必要的能源消耗和运营成本。为了解决这个问题，我们提出了一种称为智能返工流程管理 (i-RPM) 系统的新方法。所提出的系统基于智能返工政策，为具有潜在缺陷的项目提供预防性返工程序。此类项目可以在质量测试之前通过应用常规分类技术进行检测。此外，可以使用现代信息和通信技术 (ICT) 基础设施收集分类算法的训练集。根据智能返工政策，具有潜在缺陷的项目不允许进行以下流程。反而，它们返回到前面的流程进行返工，以避免在车间造成不必要的损失。因此，所提出的系统有助于实现可持续的制造系统。然而，分类模型的错误分类会降低智能返工策略的性能。因此，i-RPM 系统旨在根据分类准确性比较返工策略并选择其中最好的一种。例如，我们将 i-RPM 系统应用于位于韩国釜山的一家钢铁制造商的返工程序，我们的实验结果表明，智能返工策略的成本降低效果受几个输入参数的影响。分类模型的错误分类会降低智能返工策略的性能。因此，i-RPM 系统旨在根据分类准确性比较返工策略并选择其中最好的一种。例如，我们将 i-RPM 系统应用于位于韩国釜山的钢铁制造商的返工程序，我们的实验结果表明，智能返工策略的成本降低效果受几个输入参数的影响。分类模型的错误分类会降低智能返工策略的性能。因此，i-RPM 系统旨在根据分类准确性比较返工策略并选择其中最好的一种。例如，我们将 i-RPM 系统应用于位于韩国釜山的钢铁制造商的返工程序，我们的实验结果表明，智能返工策略的成本降低效果受几个输入参数的影响。