Nonconformities are the major sources of waste in manufacturing process. Nonconformities cannot be fully eliminated but their occurrence rate can be predicted. This paper proposes a hybrid approach based on ontological modelling and machine learning for predicting the non-conformance rates of a manufacturing process and minimising its associated costs. Based on the proposed approach, the work orders, that are represented semantically using a formal ontology, are first clustered according to their semantic similarities and then, for each cluster, the appropriate models that predict the probability distribution of non-conformance rates are developed. When a new work order is created, the most similar work order is retrieved from historical records, and the probability distribution of its non-conformance rate is estimated by applying the predictive model of the cluster to which the work order belongs. The probability distribution is used to calculate the expected underproduction and overproduction cost and to determine the amount of production that minimises the expected costs. The proposed method was validated using a dataset obtained from a manufacturer of packaging for cosmetics. Compared to the expert’s opinions and other machine learning algorithms, the proposed method demonstrated better performance with respect to cost reduction.

不合格品是制造过程中浪费的主要来源。不合格不能完全消除，但其发生率可以预测。本文提出了一种基于本体建模和机器学习的混合方法，用于预测制造过程的不合格率并最小化其相关成本。基于所提出的方法，使用形式本体在语义上表示的工作订单首先根据其语义相似性进行聚类，然后对于每个聚类，预测不合格率概率分布的适当模型被开发。创建新工单时，从历史记录中检索最相似的工单，应用工单所属集群的预测模型，估计其不合格率的概率分布。概率分布用于计算预期的生产不足和生产过剩成本，并确定使预期成本最小的生产量。使用从化妆品包装制造商获得的数据集验证了所提出的方法。与专家的意见和其他机器学习算法相比，所提出的方法在降低成本方面表现出更好的性能。