Accelerated In Situ Inspection of Release Coating and Tool Surface Condition in Composites Manufacturing Using Global Mapping, Sparse Sensing, and Machine Learning

The interfacial adhesion, friction, and resulting tool-part interaction during composites manufacturing contribute to the formation of residual stresses and process-induced deformations (PIDs). Tool-part interaction and PIDs are highly sensitive to processing variabilities, one of which is the aging of the release coating and the surface condition of production tools. Unfortunately, due to a lack of available tool inspection methods, manufacturers often attempt to mitigate the aging of release coating based on know-how, leading to cost-deficient tool preparation schedules, lower end-part quality, and in some cases, higher levels of PIDs. This paper presents an in-situ inspection method to evaluate the physicochemical properties of release coating and the surface condition of large production tools by utilizing global mapping, sparse sensing, and machine learning (ML). ML methods are used in conjunction with multiple automated measurement techniques to quickly identify the condition of release coating or contamination on production tool surfaces in manufacturing environments. Results in this paper demonstrate that during autoclave processing, aerospace-grade release coatings undergo significant chemical changes, but may remain highly abhesive for more than twenty autoclave processing cycles. Using the proposed novel inspection technology, dry ice blasting (DIB) is also demonstrated to be an effective method for non-abrasively removing cured resin contamination and release coating from a tool surface. Overall, this paper demonstrates how the proposed inspection method can be integrated into a manufacturing process for automatic surface inspection of large tools to improve production efficiency and potentially mitigate PIDs in composites manufacturing.

Automated summary

This paper presents an in-situ inspection method to evaluate the physicochemical properties of release coating and the surface condition of large production tools. The proposed method utilizes global mapping, sparse sensing, and machine learning to quickly identify the condition of release coating or contamination on tool surfaces. The results show significant chemical changes in aerospace-graded release coatings after multiple autoclave processing cycles.