Accurate synthesis of sensor-to-machined-surface image generation in carbon fiber-reinforced plastic drilling

Abstract

Delamination is a prevalent issue in carbon fiber-reinforced plastic (CFRP) drilling, significantly compromising the mechanical properties of the material. Considering that delamination can impact the long-term durability of the final products, it is essential for operators to promptly identify it. This paper proposes a machined surface image generation model, called Sensor2Image, that employs time-series force sensor data as input and generates drilled-hole surface images as output. Sensor2Image first encodes the force sensor data into images using the Gramian angular field (GAF) method. Subsequently, it applies an image-to-image translation technique to generate the final machined surface images. The proposed model was trained and evaluated using experimental data gathered from drilling CFRP specimens under an industrial robot machining system. The results demonstrated the versatility of the proposed model for practical applications, regardless of the delamination factor. The proposed method offers significant advantages over existing methods through its intuitive visual representation approach. It facilitates the visual inspection of delamination while enabling surface quality analysis of the drilled hole and identification of defects or irregularities that may impact the mechanical properties of the material. The proposed approach can enhance the efficiency and reliability of industrial processes, particularly those involving complex delamination factors. It is a valuable tool for optimizing the CFRP drilling process and enhancing drilled-hole quality in a user-friendly manner.