Distortion prediction model considering process types in film manufacturing process and identification of critical process variables

Optical films are used in flat panel displays, touch sensors and other devices. The film is wound and sent to the next process, but defects are generated in the winding, indicating an issue. Defects are often identified by customers after shipment. It is therefore anticipated that the identification of characteristics associated with defects at the end of the process will facilitate the detection of defective products in advance, or alternatively, result in a reduction of the defects themselves. One of the factors that contribute to the occurrence of defects is the distortion of the film on the roll surface. The degree of distortion is determined by calculating the difference between the instantaneous and average distance between the film surface and the sensor, as measured by the displacement meter installed before the winding. The objective of this study was to identify the process conditions that cause the distortion of the film as measured by the displacement meter through the application of machine learning techniques. A model was constructed between the sensor data of the process conditions and the distortion index, the relationship between them was identified, and the process causing the distortion was estimated by analyzing the model. The results of this study successfully narrowed down the process variables that are common causes of distortion among three displacement meters with different measurement positions. • The thickness and optical characteristics of optical films are monitored by sensors. • Defects are generated during the winding process. • The relationship between the process conditions and the distortion index was modeled. • Multiple models were constructed and analyzed based on the types of process variables. • Critical process variables responsible for the distortion were identified.