Manufacturing processes usually exhibit mixed operational conditions (OCs) due to changes in process/tool/equipment health status. Undesired OCs are direct causes of out-of-control production and thus need to be identified. Data-driven OC identification has been widely used for recognizing undesired OCs, yet most methods of this kind require labels indicating the OCs in model training. In industrial applications, such labels are rarely available due to delay, incompleteness or physical constraints in data collection. A typical case is the thermal images acquired by in-process infrared camera and pyrometer, which contain rich information about process health status but are unlabeled. To facilitate data-driven OC identification with unlabeled thermal images, this study proposes a feature extraction-clustering framework that characterizes the heat-affected zone by its temperature profile and performs ensemble clustering on the extracted features to label the data. Domain knowledge from plant manufacturing is incorporated in the framework to map cluster labels to OCs. Both offline OC recovery and online OC identification are studied. Thermal images from hot stamping in automotive manufacturing are used to demonstrate and validate the proposed method. The feasibility, effectiveness and generality are well justified by the case study results.

由于过程/工具/设备健康状态的变化，制造过程通常表现出混合操作条件 (OC)。不希望的 OC 是生产失控的直接原因，因此需要确定。数据驱动的 OC 识别已被广泛用于识别不需要的 OC，但大多数此类方法都需要在模型训练中指示 OC 的标签。在工业应用中，由于数据收集的延迟、不完整或物理限制，此类标签很少可用。一个典型的案例是过程中红外摄像机和高温计获取的热图像，其中包含有关过程健康状态的丰富信息，但没有标记。为了使用未标记的热图像促进数据驱动的 OC 识别，本研究提出了一种特征提取-聚类框架，该框架通过其温度剖面表征热影响区，并对提取的特征进行集成聚类以标记数据。来自工厂制造的领域知识被纳入框架以将集群标签映射到 OC。研究了离线 OC 恢复和在线 OC 识别。来自汽车制造中热冲压的热图像用于演示和验证所提出的方法。案例研究结果很好地证明了可行性、有效性和通用性。研究了离线 OC 恢复和在线 OC 识别。来自汽车制造中热冲压的热图像用于演示和验证所提出的方法。案例研究结果很好地证明了可行性、有效性和通用性。研究了离线OC恢复和在线OC识别。来自汽车制造中热冲压的热图像用于演示和验证所提出的方法。案例研究结果很好地证明了可行性、有效性和通用性。