Abstract
Drilling of Carbon Fiber-Reinforced Plastic/Titanium alloy (CFRP/Ti) stacks represents one of the most widely used machining methods for making holes to fasten assemblies in civil aircraft. However, poor machinability of CFRP/Ti stacks in combination with the inhomogeneous behavior of CFRP and Ti alloy face manufacturing and scientific community with a problem of defining significant factors and conditions for ensuring hole quality in the CFRP/Ti alloy stacks. Herein, we investigate the effects of drilling parameters on drilling temperature and hole quality in CFRP/Ti alloy stacks by applying an artificial neuron network (ANN). We varied cutting speed, feed rate, and time delay factors according to the factorial design L9 Taguchi orthogonal array and measured the drilling temperature, hole diameter, and out of roundness by using a thermocouple and coordinate measuring machine methods for ANN analysis. The results show that the drilling temperature was sensitive to the effect of stack material layer, cutting speed, and time delay factors. The hole diameter was mainly affected by feed, stack material layer, and time delay, while out of roundness was influenced by the time delay, stack material layer, and cutting speed. Overall, ANN can be used for the identification of the drilling parameters–hole quality relationship.
Highlights
Carbon fiber reinforced plastics (CFRP) are widely used in different applications, starting with rope threads [1], gears [2], and aerospace components [3]
This provides artificial neuron network (ANN) analysis of process parameters influence on drilling temperature, hole diameters, and out of roundness when drilling Carbon Fiber-Reinforced Plastic/Titanium alloy (CFRP/Ti) alloy stacks
The results demonstrate that ANN can be an effective tool for the identification of the drilling parameters–hole quality and drilling temperature relationship
Summary
Carbon fiber reinforced plastics (CFRP) are widely used in different applications, starting with rope threads [1], gears [2], and aerospace components [3]. The traditional ANOVA method does not provide reliable information on many factors affecting the hole quality parameters when drilling CFRP/Ti alloy stacks From this point of view, ANN analysis can provide a researcher with reliable calculations and predictions of possible input–output connections. Based on the above literature review, it can be concluded that the use of ANN analysis of experimental data will be a suitable opportunity to fill the research gap of research on the subject This provides ANN analysis of process parameters influence on drilling temperature, hole diameters, and out of roundness when drilling CFRP/Ti alloy stacks. Such analysis presented novel results of technological factors’ effect on hole quality parameters in CFRP/Ti alloys stacks considering the influence of time delay factor, which was not reported earlier. The results demonstrate that ANN can be an effective tool for the identification of the drilling parameters–hole quality and drilling temperature relationship
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
