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 μm, T_m = 0.358 min/cm³, C = $8.2973. Likewise, the corresponding finish-turning parameters were also estimated: cutting speed v_c = 250 m/min, cutting depth a_p = 1.0 mm, and feed per revolution f_r = 0.08 mm/rev. The ANN model achieved a reliable prediction accuracy of ±1.35% for surface roughness.

Aluminum Alloy 6061 components are frequently manufactured for various industries—aeronautics, yachting, and optical instruments—due to their excellent physical and mechanical properties, including corrosion resistance. There is little research on the mechanical tooling of AA6061 and none on its structure and properties and their effects on surface roughness after finish turning. The objective of this comprehensive study is, therefore, to ascertain the effects of both the modern method of hardening AA6061 shafts and the finish turning conditions on surface roughness, Ra, and the minimum machining time for unit-volume removal, T_m, while also establishing the cost price of processing one part, C. The hardening methods improved both the physical and the mechanical material properties processed with 2, 4, and 6 passes of equal channel angular pressing (ECAP) at room temperature, using an ECAP-matrix with a channel angle of 90°. The reference workpiece sample was a hot extruded chip under an extrusion ratio (ER) of 5.2 at an extrusion temperature of 500 °С (ET = 500 °C). The following results were obtained: grain size in ECAP-6 decreased from 15.9 to 2.46 μm, increasing both microhardness from 41 Vickers hardness value (HV) to 110 HV and ultimate tensile strength from 132.4 to 403 MPa. The largest decrease in surface roughness, Ra—70%, was obtained turning a workpiece treated with ECAP-6. The multicriteria optimization was computed in a multilayer perceptron-based artificial neural network that yielded the following optimum values: the minimal length of the three-dimensional estimates vector with the coordinates Ra = 0.800 μm, T_m = 0.341 min/cm³, and С = 6.955 $ corresponded to the optimal finish turning conditions: cutting speed v_c = 200 m/min, depth of cut a_p = 0.2 mm, and feed per revolution f_r = 0.103 mm/rev (ET-500 extrusion without hardening).

In this work, an extensive analysis has been presented and discussed to study the effectiveness of using different cooling and lubrication techniques when turning AISI 1045 steel. Three different approaches have been employed, namely dry, flood, and minimum quantity lubrication based nanofluid (MQL-nanofluid). In addition, three multi-objective optimization models have been employed to select the optimal cutting conditions. These cases include machining performance, sustainability effectiveness, and an integrated model which covers both machining outputs (i.e., surface roughness and power consumption) and sustainability aspects (carbon dioxide emissions and total machining cost). The results provided in this work offer a clear guideline to select the optimal cutting conditions based on different scenarios. It should be stated that MQL-nanofluid offered promising results through the three studied cases compared to dry and flood approaches. When considering both sustainability aspects and machining outputs, it is found that the optimal cutting conditions are cutting speed of 147 m/min, depth of cut of 0.28 mm and feed rate of 0.06 mm/rev using MQL-nanofluid. The three studied multi-objective optimization models obtained in this work provide flexibility to the decision maker(s) to select the appropriate cooling/lubrication strategy based on the desired objectives and targets, whether these targets are focused on machining performance, sustainability effectiveness, or both. Thus, this work offers a promising attempt in the open literature to optimize the machining process from the performance–sustainability point of view.

High-strength steels are used in various civilian and military products. The initial cost of the raw materials for these products is very high. The surface roughness of these products is extremely important during the finishing pass to be accepted during the final inspection. The surface roughness should conform to the required values stated on the design drawing. The paper presents the results of experiments in turning of high-strength steel featuring three factors—cutting speed V, feed rate f, and depth of cut t—on five levels (125 specimens). These were divided into 25 groups. Each of the five groups was subjected to one common machining speed. Each group was machined using five levels of cutting depth. Each depth was processed using five levels of feed rate. Tessa was used for examination of surface roughness. There is little modern research on machining high-strength steel. The high cost of this material compels us to look for the optimum turning conditions to provide for the specified roughness of surface Ra and the minimum machining time of unit volume T _m . As a result of our study, an artificial neural network was designed in Matlab on the basis of the MLP 3-10-1 multilayer perceptron that allows us to predict Ra of the workpiece with ±2.14% accuracy within the range of the experimental cutting speed, depth of cut, and feed rate values. For the first time, a Pareto frontier was obtained for Ra and T _m of the finished workpiece from high-strength steel using the artificial neural network model that was later used to determine the optimum cutting conditions. It is possible to integrate the suggested optimization algorithms into computer-aided manufacturing using Matlab.