Experimental Study and Artificial Neural Network Simulation of Cutting Forces and Delamination Analysis in GFRP Drilling

Katarzyna Biruk-Urban; Paul Bere; Jerzy Józwik; Michał Leleń

doi:10.3390/ma15238597

Experimental Study and Artificial Neural Network Simulation of Cutting Forces and Delamination Analysis in GFRP Drilling

Materials (Basel). 2022 Dec 2;15(23):8597. doi: 10.3390/ma15238597.

Authors

Katarzyna Biruk-Urban¹, Paul Bere², Jerzy Józwik¹, Michał Leleń¹

Affiliations

¹ Department of Production Engineering, Mechanical Engineering Faculty, Lublin University of Technology, 20-618 Lublin, Poland.
² Department of Manufacturing Engineering, Faculty of Industrial Engineering, Robotics and Production Management, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania.

Abstract

This paper reports the results of measurements of cutting forces and delamination in drilling of Glass-Fiber-Reinforced Polymer (GFRP) composites. Four different types of GFRP composites were tested, made by a different manufacturing method and had a different fiber type, weight fraction (wf) ratio, number of layers, but the same stacking sequence. GFRP samples were made using two technologies: a novel method based on the use of a specially designed pressing device and hand lay-up and vacuum bag technology process. The study was conducted with variable technological parameters: cutting speed v_c and feed per tooth f_z. The two-edge carbide diamond-coated drill produced by Seco Company was used in the experiments. Cutting-force components and delamination factor were measured in the experiments, and photos of the holes were taken to determine the delamination. In addition, modeling of cause-and-effect relationships between the technological drilling parameters v_c and f_z was simulated with the use of artificial neural network modeling. For all tested GFRP materials, an increase in f_z led to an increase in the amplitude of cutting-force component F_z. The lowest values of the amplitude of cutting-force component F_z were obtained with the lowest tested feed per tooth value of 0.04 mm/tooth for all tested materials. It was observed that materials produced with the use of the specially designed pressing device were characterized by lower values of the cutting-force component F_z. It was also found that the delamination factor increased with an increase in f_z for all tested GFRP materials. A comparison of the lowest and the highest values of f_z revealed that the lowest delamination factor increase was archived by the B1 material and amounted to about 12.5%. The error margin of the obtained numerical modeling results does not exceed 15%, so it can be concluded that artificial neural networks are a suitable tool for modeling cutting force amplitudes as a function of v_c and f_z. The study has shown that the use of the special pressing device during the manufacturing of composite materials has a positive effect on delamination.

Keywords: GFRP; cutting force; delamination; drilling.

Grants and funding

030/RID/2018/19/Lublin University of Technology - project Regional Excellence Initiative, funded by the Polish Ministry of Science and Higher Education