Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Abstract

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 highstrength 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.

An Investigation on Surface Roughness and Tool Wear in Turning Operation of Inconel 718

“…xx/xx 03/10 consistent with the experimental data set. Various statistical methods have been applied to machining operations to find the optimum process parameters and surface roughness, machining time, and tool cost were used as outputs for these operations (Abbas et al 2017;Abbas et al 2018a;.…”

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.…”

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.…”

“…The cutting inserts used were coated carbides and the surface quality (in terms of speed and feed) as well as tool wear (in terms of machining time) was discussed. Abbas et al [27] calculated the Pareto frontier for surface roughness, Ra, and the minimal machining time of a unit volume, T m , of a finished high-strength steel workpiece using an artificial neural network model that was subsequently used for determining the optimal cutting speed v c , feed rate, f, and depth of cut, a p . Rogante [28] presented a tool condition monitoring study on the dry cutting process using observations of the tool life supported by the Taylor tool life model.…”