Mechanical modelling and simulation of thrust force in drilling process in GFRP composite laminates: A novel system dynamics approach. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Murthy, B. R. N.; G.S, Vijay; Narayan, S.; Naik, Nithesh; Sooriyaperakasam, Nilakshman; Karthik, Aravind; Borkhade, Revati
• Editors: Bahar, Rubina
• Is Part Of: Cogent engineering. Volume 6:Issue 1(2019)
• Publication Date: 2019-01-01
• Subjects: artificial intelligence -- response surface method -- system dynamics -- drilling -- composites -- thrust force; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2019.1706981
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: Drilling is a salient machining process employed in assembling the components and structures made from polymer composites. Influence of various drilling process parameters on the drilling process has been identified and controlled to maintain the integrity of the composite material and avoid material damage during drilling. The precision of drilling depends upon many process parameters like feed rate, tool material, cutting speed, drill diameter, fiber orientation in composite material and thrust forces developed. The present investigation deals with the study and evaluation of influence of drilling parameters such as material thickness, drill diameter, drill point angle, feed rate and spindle speed on thrust force developed during drilling of glass fiber reinforced plastic composite laminates using Taguchi method. The experimental results prove that drill angle and the spindle speed are the most significant parameters which influence the thrust force. The simulation of the drilling process was developed using a novel system dynamics (SD) modelling approach through a causal loop diagram. Design of experiments (DOE) was utilized to generate a number of experiments required. A full factorial design is used, and 243 holes were drilled to collect the experimental data. The required mathematical equation for modelling was developed by using DOE method. To validate the SD results, the results obtained through SD novel approach were compared with artificial neural network and response surface method which are recognised as the best simulation tools and noticed a good agreement between the values obtained. The novel SD approach of modelling showed an agreement of more than 93% acceptance level with the experimental results.