Identifying manufacturing operational conditions by physics-based feature extraction and ensemble clustering

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.