ANN Surface Roughness Optimization of AZ61 Magnesium Alloy Finish Turning: Minimum Machining Times at Prime Machining Costs

作者：Abbas Adel Taha; Pimenov Danil Yurievich; Erdakov Ivan Nikolaevich; Taha Mohamed Adel; Soliman Mahmoud Sayed; El Rayes Magdy Mostafa

来源：Materials, 2018, 11(5): 808.

DOI：10.3390/ma11050808

• artificial neutral network
• cutting parameters
• magnesium alloys
• optimization
• prime machining costs
• surface roughness

Magnesium alloys are widely used in aerospace vehicles and modern cars, due to their rapid machinability at high cutting speeds. A novel Edgeworth-Pareto optimization of an artificial neural network (ANN) is presented in this paper for surface roughness (Ra) prediction of one component in computer numerical control (CNC) turning over minimal machining time (T-m) and at prime machining costs (C). An ANN is built in the Matlab programming environment, based on a 4-12-3 multi-layer perceptron (MLP), to predict Ra, T-m, and C, in relation to cutting speed, v(c), depth of cut, a(p), and feed per revolution, f(r). For the first time, a profile of an AZ61 alloy workpiece after finish turning is constructed using an ANN for the range of experimental values v(c), a(p), and f(r). The global minimum length of a three-dimensional estimation vector was defined with the following coordinates: Ra = 0.087 mu m, T-m = 0.358 min/cm(3), C = $8.2973. Likewise, the corresponding finish-turning parameters were also estim

• 出版日期2018-5