Abstract: Described herein are systems and methods for improving defect detection in an automated production process. The system comprises a memory that stores executable components and a processor, operatively coupled to the memory, that executes the executable components. The executable components comprise an automation defect component and a machine learning component. The automation defect component retrieves parametric data associated with the production process. The automation defect component provides the parametric data to a machine learning algorithm. The machine learning component generates common attributes between the defective items. The machine learning component identifies a set of common attributes shared between the defective items and a non-defective item. The machine learning component modifies the set of the common attributes shared between the defective items and the non-defective item. The machine learning component generates defect indicators based on the common attributes.

Abstract: Described herein are improvements for identifying defects during automated item production. In one example, a method includes identifying a first defect in a first item. The first defect is associated with a stage of production of the first produced item. The method further includes retrieving first parametric data associated with the stage for the first item and identifying one or more defect indicators based on the first parametric data and second parametric data associated with the stage for one or more second items having defects associated with the stage. The method also includes monitoring subsequent parametric data associated with the stage to recognize the one or more defect indicators in the subsequent parametric data.

Abstract: Described herein are improvements for identifying defects during automated item production. In one example, a method includes identifying a first defect in a first item. The first defect is associated with a stage of production of the first produced item. The method further includes retrieving first parametric data associated with the stage for the first item and identifying one or more defect indicators based on the first parametric data and second parametric data associated with the stage for one or more second items having defects associated with the stage. The method also includes monitoring subsequent parametric data associated with the stage to recognize the one or more defect indicators in the subsequent parametric data.