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

Abstract

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 (Tm) 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, Tm, and C, in relation to cutting speed, vc, depth of cut, ap, and feed per revolution, fr. 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 vc, ap, and fr. The global minimum length of a three-dimensional estimation vector was defined with the following coordinates: Ra = 0.087 μm, Tm = 0.358 min/cm3, C = $8.2973. Likewise, the corresponding finish-turning parameters were also estimated: cutting speed vc = 250 m/min, cutting depth ap = 1.0 mm, and feed per revolution fr = 0.08 mm/rev. The ANN model achieved a reliable prediction accuracy of ±1.35% for surface roughness.

Artificial Intelligence Monitoring of Hardening Methods and Cutting Conditions and Their Effects on Surface Roughness, Performance, and Finish Turning Costs of Solid-State Recycled Aluminum Alloy 6061 Сhips

“…Considering the above, if we look at the studies describing the establishment of the optimum turning conditions using multi-objective optimization [47][48][49][50][51][52], Basak et al [47] presented two types of Pareto optimization of a hard turning process for the machining of D2 steel with ceramic tools. The goal was to minimize machining time and cost.…”

“…Abbas et al [50,51] used Pareto optimization to establish the turning conditions of a heat-treated alloy steel material (J-Steel), for the minimization of surface roughness, and the turning conditions of a high-strength steel machining time, for the removal of a unit volume. Abbas et al [52] studied surface roughness, Ra, minimum unit-volume machining time, T m , cost of processing one part, C, in magnesium alloy AZ61 finish turning. Papers on multi-objective optimization in turning are, therefore, not very numerous.…”

“…The Pareto method is the most efficient method for solving these sorts of tasks. However, no multicriterion optimization of Alloy 6061 machining was proposed in the above studies [37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52]. Moreover, these studies took no account of the physical and the mechanical properties of the various conditions of the AA6061 workpiece on surface roughness in turning.…”

Analysis of the Deviation in a Low-Cost System for Stepless Digital Control of Conventional Lathe Spindle Speeds

“…The stepless speed control enables optimum cutting speeds to be selected based on the material type of the edge and the material to be machined for each workpiece diameter. Reported literature and practice allow us to achieve the desired edge durability [30,32,38] and surface condition [27,39,40]. Achieving a constant cutting speed is particularly desirable in turning faces with a continuous change in the turning diameter.…”

Optimization of cutting conditions using artificial neural networks and the Edgeworth-Pareto method for CNC face-milling operations on high-strength grade-H steel

“…ANN milling model based on experimental data [34][35][36] was built to achieve our objective of solving the optimization problem in a multi-objective setting using both Edgeworth and the Pareto frontiers [31][32][33][37][38][39].…”