Intelligent Insights for Manufacturing Inspections from Efficient Image Recognition

Abstract

Many complex electromechanical assemblies that are essential to the vital function of certain products can be time-consuming to inspect to a sufficient level of certainty. Examples include subsystems of machine tools, robots, aircraft, and automobiles. Out-of-tolerance conditions can occur due to either random common-cause variability or undetected nonstandard deviations, such as those posed by debris from foreign objects. New methods need to be implemented to enable the utilization of detection technologies in ways that can significantly reduce inspection efforts. Some of the most informative three-dimensional image recognition methods may not be sufficiently reliable or versatile enough for a wide diversity of assemblies. It can also be an extensive process to train the recognition on all possible anomalies comprehensively enough for inspection certainty. This paper introduces a methodical technique to implement a semiautonomous inspection system and its algorithm, introduced in a prior publication, that can learn manufacturing inspection inference from image recognition capabilities. This fundamental capability accepts data inputs that can be obtained during the image recognition training process followed by machine learning of the likely results. The resulting intelligent insights can inform an inspector of the likelihood that an assembly scanned by image recognition technology will meet the manufacturing specifications. An experimental design is introduced to generate data that can train and test models with a realistic representation of manufacturing cases. A benchmark case study example is presented to enable comparison to models from manufacturing cases. The fundamental method is demonstrated using a realistic assembly manufacturing example. Recommendations are given to guide efforts to deploy this entire methodical technique comprehensively.