An investigation of optimum cutting conditions in turning nodular cast iron using carbide inserts with different nose radius

Abstract

Ductile iron can be produced to have different properties through proper control of heat treatments and additives that is directly related to the microstructure. The nodular form of the graphite imparts beneficial characteristics for this alloy. The purpose of this research is to investigate the effect of main process parameters, namely, feed rate, depth of cut, cutting speed and tool node radius on the surface roughness in nodular cast iron during turning operation. The concerned cutting tools used are turning tools with carbide inserts with tool nose radius of 0.4 and 0.8 mm. Three levels of cutting speed, feed rate and depth of cut are investigated. Surface roughness Ra was measured for each combination of machining conditions. Design of experiment tools were implemented to develop a model that relates the process variables to the resulting surface roughness. The model revealed the individual contribution of each parameter as well as the interaction among parameters to impart a change on the surface quality. The results showed that the feed rate and tool nose radius had the major contribution and to a lesser extent comes the role of cutting speed and depth of cut for controlling surface roughness. Minimum roughness was achieved at higher cutting speed, lower feed rate and lower depth of cut for the higher nose radius. Metal removal rate, as a measure for productivity, was also calculated and multi-objective optimization was conducted to minimize Ra and maximize metal removal rate simultaneously. Optical microscopy, on the effect of nose radius for the optimum process parameters for minimum Ra, revealed that for lower nose radius there are more occasions of graphite pullouts that affected the surface quality adversely.

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

“…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.…”

“…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. Mia and Dhar [16] developed an artificial neural network (ANN) model to predict the average surface roughness in turning hardened steel EN 24 T. Jurkovic et al [17] compared three machine learning methods for predicting the high-speed turning observed parameters (surface roughness (Ra), cutting force (Fc), and the tool life (T)).…”

“…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.…”

Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network

“…The model was fitted in the form given by [2] where is the constant term, represents the linear effects, represents the pure quadratic effects, represents the second level interaction effects, represents the third level interaction effects, represents the effect of interaction between linear and quadratic terms, and represents the error in predicting experimental surface roughness.…”

“…It is worth mentioning that surface roughness also influenced the mechanical properties such as corrosion resistance, creep life, and fatigue behavior. Extensive previous work [1][2][3][4] has been done on investigating the effect of the following parameters: cutting depth, feed rate, cutting speed, tool nose radius, lubrication condition, and cutting tool material, on the following response variables: tool wear, surface roughness, cutting forces, production time, and cost. These studies were conducted using different routes; analysis of variance, neural networking coupled with genetic algorithm, and neural networking coupled with electromagnetism optimization [5][6][7][8][9][10].…”