This paper intends to present the System Dynamics (SD) as a novel method to simulate the thrust force developed during drilling of GFRP composites. Good quality holes are extremely fundamental so as to accomplish equally good joints amid creation of components prepared from composite for better execution. | Simulation of the drilling process in GFRP composites using system dynamics and validation by ANN and RSM International Journal of Mechanical Engineering and Technology IJMET Volume 10 Issue 03 March 2019 pp. 585-593. Article ID IJMET_10_03_060 Available online at http ijmet JType IJMET amp VType 10 amp IType 3 ISSN Print 0976-6340 and ISSN Online 0976-6359 IAEME Publication Scopus Indexed SIMULATION OF THE DRILLING PROCESS IN GFRP COMPOSITES USING SYSTEM DYNAMICS AND VALIDATION BY ANN AND RSM Murthy B. R. N and Vijay G. S Department of Mechanical and Manufacturing Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Corresponding author ABSTRACT This paper intends to present the System Dynamics SD as a novel method to simulate the thrust force developed during drilling of GFRP composites. Good quality holes are extremely fundamental so as to accomplish equally good joints amid creation of components prepared from composite for better execution. Since the nature of a drilled hole is subject to material properties and machining conditions it is important to think about the impacts of these factors on the nature of hole obtained. In the present work the machining parameters thickness of the material drill point angle drill size drill speed and feed rate are selected to evaluate their effect on the quality of the hole. Past works uncover the fact that the damage caused to the drilled hole is primarily due to the thrust force. Consequently it is fundamental to limit the thrust force so as to accomplish better quality of the drilled hole. The SD simulation model was implemented through a causal loop diagram. A mathematical equation used in the simulation was developed utilizing the Design of Experiments DOE technique. VENSIM programming was utilized to create and run the SD model. The SD simulation results were compared with Artificial Neural Networks ANN results Response Surface Methodoly RSM
