Prediction of surface roughness in low speed turning of AISI316 austenitic stainless steel

Abstract

Surface roughness is an important quality in manufacturing, as it affects the product’s tribological, frictional and assembly characteristics. Turning stainless steel at low cutting speeds may result in a rougher surface due to built up edge formation, where as speed increases the surface roughness improves, due to the low contact time between the chip and the tool to allow bonding to occur.However, this increase in cutting speed produces higher tool wear rates, which increases the machining costs. Previous studies have indicated that savings in cost and manufacturing time are obtained when predicting the surface roughness, prior to the machining process. In this paper, experimental data are used to develop prediction models using Multiple Linear Regression and Artificial Neural Network methodologies. Results show that the neural network outperforms the linear model by a fair margin (1400%). Moreover, the developed Artificial Neural Network model has been integrated with an optimisation algorithm, known as Simulated Annealing (SA),this is done in order to obtain a set of cutting parameters that result in low surface roughness. A low value of surface roughness and the set of parameters resulting on it, are successfully yielded by the SA algorithm

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

“…Abbas [19] analyzed the effect of the feed rate, depth of cut and cutting speed on the surface roughness in turning high-strength steel. Though all the previously mentioned research [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] provided prediction models of surface roughness, they failed to solve the problem of determining the optimal cutting parameters for the minimum surface roughness and maximum production rate.…”

“…In anticipation of the next sixth technology revolution, it is becoming an increasingly important technique for processing large data sets using artificial intelligence and the integration of artificial intelligence algorithms in automated production. Many previous investigations have been devoted towards developing prediction models for rough turning [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Risbood et al [1] researched and produced models for forecasting roughness and dimensional deviation for dry and wet turning of mild steel rods.…”

“…Azam et al [13] investigated the relationship of average surface roughness and processing parameters (feed, speed and depth of cut) for turning highstrength low-alloy steel (AISI 4340). Acayaba and Escalona [14] developed a model for predicting surface roughness in low speed turning of AISI316 austenitic stainless steel using multiple linear regression and artificial neural network techniques. Al Bahkali et al [15] studied the effect of feed, cutting depth, radius of curvature of the tool tip and the cutting speed on surface roughness in turning cast iron.…”

Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network

“…A comparison between linear regression models and ANN approach has been studied in the work of Acayaba and Escalona [8]. A target of saving cost, effort, and machining time leads to the necessity of predicting surface roughness prior to performing machining operations.…”

Machinability Investigations of Stainless Steel (304 L) using different Inserts

“…Stainless steels are stainless as these have minimum of 11.5%chromium in them,which having more affinity for oxygen than iron. Chromium forms a very thin, protective and stable oxide (Cr 2 O 3 ) film on the surface [1][2]. This film is continuous, impervious and passive to stop further reaction between the steel and the surrounding atmosphere.…”