Abstract
The machining of Fiber Reinforced Polymers (FRP) is accompanied by specific defects such as delamination, uncut fibers, and others. Such defects are unique in their shape and size for different FRP types, used tools, and applied cutting conditions. Therefore, defect recognition and quantification remain a central challenge in the quality control of FRP components from an accuracy and time balance perspective. The study presents the implementation of Machine Learning techniques for automated recognition of the hole defects resulting from drilling Flax/PLA biocomposites using HSS drills with various cutting data. The paper discusses the effectiveness and stability of the developed solution.
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