A Force Sensorless Method for CFRP/Ti Stack Interface Detection during Robotic Orbital Drilling Operations

Abstract

Drilling carbon fiber reinforced plastics and titanium (CFRP/Ti) stacks is one of the most important activities in aircraft assembly. It is favorable to use different drilling parameters for each layer due to their dissimilar machining properties. However, large aircraft parts with changing profiles lead to variation of thickness along the profiles, which makes it challenging to adapt the cutting parameters for different materials being drilled. This paper proposes a force sensorless method based on cutting force observer for monitoring the thrust force and identifying the drilling material during the drilling process. The cutting force observer, which is the combination of an adaptive disturbance observer and friction force model, is used to estimate the thrust force. An in-process algorithm is developed to monitor the variation of the thrust force for detecting the stack interface between the CFRP and titanium materials. Robotic orbital drilling experiments have been conducted on CFRP/Ti stacks. The estimate error of the cutting force observer was less than 13%, and the stack interface was detected in 0.25 s (or 0.05 mm) before or after the tool transited it. The results show that the proposed method can successfully detect the CFRP/Ti stack interface for the cutting parameters adaptation.

Adaptive Machining Method for Helical Milling of Carbon Fiber-Reinforced Plastic/Titanium Alloy Stacks Based on Interface Identification

“…The hole diameters and their errors for the helical milling of CFRP/Ti stacks under the two experimental methods are shown in Figures 13 and 14. Analyzing Figure 13, it is found that although the same parameters are used for machining the CFRP layer under In other studies, Pan et al [32] proposed an online monitoring method for the machining position of the helical milling of CFRP/Ti stacks based on a robotic hole-making system. Comparing the interface identification results obtained in this study with the previous relevant study by Pan et al [32], it is found that the accuracy of interface identification for the CFRP entry interface and transition interface obtained in this study is slightly higher.…”

“…Furthermore, the thickness of each material layer at different hole-making positions may not be uniform due to the requirements of the shape of the component, and there is a certain error between the actual thickness and the theoretical value, which makes it difficult to accurately control the machining position and adjust the machining parameters promptly. Pan et al [32] proposed an online monitoring method for the position of the helical milling of CFRP/Ti stacks based on a robotic hole-making system. Neugebauer et al [33] utilized acoustic emission signals to monitor the real-time position of the cutting tool during manufacturing.…”

Material identification method for helical milling of CFRP/Ti stacks based on torque−speed model

“…Neugebauer R et al [28] used relevant sensors to monitor the real-time position of the tool in the material through acoustic emission signals. Relying on the robotic helical milling system, Pan et al [29] evaluated the relationship between the current of servo motor and the cutting force, and proposed a method for online monitoring of the hole-making position of CFRP/Ti stacks by establishing a model. The above researches either require external sensors or rely on large robotic hole-making systems, which are limited for confined machining spaces.…”

Material Identification in Helical Milling for Smart Drilling Applications

“…Precise real time material identification along with instantaneous correction in cutting condition if required is crucial for successful implementation of smart machining strategies. Fang et al [15] developed force sensor less method to detect stack interface in robotic orbital drilling operations which can be helpful to adapt proper cutting conditions. The identification of process incidences during stack drilling by assessing different decision making algorithms is demonstrated by Pardo et al [16].…”