Experimental investigation of threading performance of GGG44 pipe fittings and modeling with ANN method

Purpose The main objective of the study is to increase energy efficiency and to determine the optimum rotational speeds in production processes. In this direction, the ideal production parameters were determined using experimental measurements and artificial neural network (ANN) analyses. Design/methodology/approach In this study, the threading performances of two different types of pipe fittings made of (Spheroidal graphite cast iron) GGG44 spheroidal graphite cast iron are investigated experimentally by considering energy efficiency. Experimental studies were carried out on elbow and tee-type fittings and energy consumption and torque values were measured. The experimental results were modeled using the ANN method. Afterward, optimum production parameters were determined. Findings As a result, it was found that the best threading parameter in terms of maximum energy efficiency is 34 revolutions per minute (rpm) rotational speed value. When force, torque, cutting tool life and energy efficiency outputs are considered together, the optimum threading condition was obtained at 30 rpm rotational speed. Originality/value The production of pipe fittings has high energy costs in the manufacturing sector due to the large amount of threading operations and the high torque required. These machines increase energy consumption by producing high torque while operating at low rotational speeds. Research on optimising energy consumption is of great importance both in terms of reducing costs and sustainability. For these reasons, the study focuses on optimising energy consumption in the mass production of pipe fittings by mounting an inverter on manual threading machines and obtaining energy consumption and required torque values at different rotational speeds. Highlights