An overview on economic machining of hardened steels by hard turning and its process variables

Producers of machined components and manufactured goods are continually challenged to reduce cost, improve quality, and minimize setup times in order to remain competitive. Frequently the answer is found with new technology solutions. In the recent years, there has been increasing interest in hard turning over grinding for machining of hardened steels in automotive, bearing, mold-die making industries. Hard turning is greatly affected by factors like machine tool, cutting tool geometry and materials, cutting parameters, and cooling methods. There are some issues in the process which should be understood and dealt with such as friction and heat generation at the cutting area that can affect the tool life and surface finish apart from other machining results to achieve successful performance. Researchers have worked upon several aspects related to hard turning and came up with their own recommendations to overcome these problems. This article presents an overview on all the factors that influences hard turning operations performance and is an attempt to give a proper understanding of the process. A summary of the hard turning techniques is outlined and further a comparison of hard turning and grinding is discussed with regard to certain evaluation criteria based on process economical efficiency

Data driven surrogate model-based optimization of the process parameters in electric discharge machining of D2 steel using Cu-SiC composite tool for the machined surface roughness and the tool wear

Electrical discharge machining (EDM) is mainly utilized for the die manufacturing and also used to machine the hard materials. Pure Copper, Copper based alloys, brass, graphite, steel are the conventional electrode materials for EDM process. While machining with the conventional electrode materials, tool wear becomes the main bottleneck which led to increased machining cost. In the present work, the composite tool tip comprises 80% Copper and 20% silicon carbide was used for the machining of hardened D2 steel. The powder metallurgy route was used to fabricate the composite tool tip. Electrode wear rate and surface roughness were assessed with respect to the different process parameters like input current, gap voltage, pulse on time, pulse off time and dielectric flushing pressure. During the analysis it was found that Input current (I p ), Pulse on time (T on ) and Pulse off time (T off ) were the significant parameters which were affecting the tool wear rate (TWR) while the I p , T on and flushing pressure affected more the surface roughness (SR). SEM micrograph reveals that increase in I p leads to increase in the wear rate of the tool. The data obtained from experiments were used to develop machine learning based surrogate models. Three machine learning (ML) models are random forest, polynomial regression and gradient boosted tree. The predictive capability of ML based surrogate models was assessed by contrasting the R 2 and mean square error (MSE) of prediction of responses. The best surrogate model was used to develop a complex objective function for use in firefly algorithm-based optimization of input machining parameters for minimization of the output responses

Development and Mechanical Characterization of Ni-Cr Alloy Foam Using Ultrasonic-Assisted Electroplating Coating Technique

Metal foams and alloy foams are a novel class of engineering materials and have numerous applications because of their properties such as high energy absorption, light weight and high compressive strength. In the present study, the methodology adopted to develop a Ni-Cr alloy foam is discussed. Polyurethane (PU) foam of 40PPI (parts per inch) pore density was used as the precursor and coating techniques such as electroless nickel plating (ELN), ultrasonic-assisted electroplating of nickel (UAEPN), and pack cementation or chromizing were used to develop the Ni-Cr alloy foam. The surface morphology, strut thickness and minimum weight gain after each coating stage were evaluated. It was observed from the results that the adopted coating techniques did not damage the original ligament cross-section of the PU precursor. The minimum weight gain and the coating thickness after the UAEPN process were observed to be 42 g and 40–60 μm, respectively. The properties such as porosity percentage, permeability and compressive strength were evaluated. Finally, the pressure drop through the developed foam was estimated and verified to determine whether the developed foam can be used for filtering applications.Filipe Fernandes acknowledges the CEMMPRE (UIDB/00285/2020) and ARISE (LA/P/0112/2020) projects, sponsored by national funds through the FCT—Fundação para a Ciência e a Tecnologia

Parametric optimization of Nd:YAG laser microgrooving on aluminum oxide using integrated RSM-ANN-GA approach

Abstract Nowadays in highly competitive precision industries, the micromachining of advanced engineering materials is extremely demand as it has extensive application in the fields of automobile, electronic, biomedical and aerospace engineering. The present work addresses the modeling and optimization study on dimensional deviations of square-shaped microgroove in laser micromachining of aluminum oxide (Al2O3) ceramic material with pulsed Nd:YAG laser by considering the air pressure, lamp current, pulse frequency, pulse width and cutting speed as process parameters. Thirty-two sets of laser microgrooving trials based on central composite design (CCD) design of experiments (DOEs) are performed, and response surface method (RSM), artificial neural network (ANN) and genetic algorithm (GA) are subsequently applied for mathematical modeling and multi-response optimization. The performance of the predictive ANN model based on 5-8-8-3 architecture gave the minimum error (MSE = 0.000099) and presented highly promising to confidence with percentage error less than 3% in comparison with experimental result data set. The ANN model combined with GA leads to minimum deviation of upper width, lower width and depth value of − 0.0278 mm, 0.0102 mm and − 0.0308 mm, respectively, corresponding to optimum laser microgrooving process parameters such as 1.2 kgf/cm2 of air pressure, 19.5 Amp of lamp current, 4 kHz of pulse frequency, 6% of pulse width and 24 mm/s of cutting speed. Finally, the results have been verified by performing a confirmatory test

Modelling and optimization of technological parameters in hot abrasive jet machining of alumina ceramic

The present work focuses on the experimental investigation of hot abrasive jet machining (HAJM) and precision drilling operation on flat surfaces of K-60 alumina ceramic material using different grades of silicon carbide abrasives. The machining AJM setup is designed based on fluidized bed mixing chamber along with pressurized powder feed chamber. The experiments are performed as per Box-Behnken design of experiments (BBDOEs) with four process parameters (pressure, stand of distance, abrasive temperature and grain size) for parametric optimization in order to control the two technological response characteristics (material removal rate, flaring diameter) of the precision holes on K-60 alumina. Analysis of variance (ANOVA), response surface methodology (RSM) and genetic algorithm (GA) are subsequently proposed for predictive modelling and process optimization. Result shows that application of hot abrasives in AJM process has excellent performance in terms of improved material removal rate, and minimum dimensional deviation of drilled hole. Multi-response optimization GA technique presented the optimal setting of machining variables in HAJM process at air pressure of 6.682 kgf/cm2, abrasive temperature of 60.6 °C, stand-off-distance of 7.1124 mm, abrasive grain size of 275.755 µm, with estimated maximal material removal rate of 0.005 gm/s and minimal flaring diameter of 6.382 mm. The methodology described here is expected to be highly beneficial to manufacturing industries

Performance comparison of vegetable oil based nanofluids towards machinability improvement in hard turning of HSLA steel using minimum quantity lubrication

The search of finding best vegetable oil based nanofluid from a set of three nanoparticle enriched cutting fluids for machining is core objective of the work. Extensive research has been done to replace conventional cutting fluids by nanofluids, but abundant analysis for vegetable oil based nanofluids is accomplished in this work which was not seen earlier. Also, the study investigated the cutting performance and comparative assessment towards machinability improvement during hard turning of high-strength-low-alloy (HSLA) AISI 4340 steel using four different compositions of nanofluids by minimum quantity lubrication (MQL) technique. Cutting are investigated and analyzed through this article during hard turning using minimum quantity lubrication (MQL). Cutting force, tool wear (flank and crater), surface integrity (surface roughness, residual stress, microhardness, and surface morphology), and chip morphology are considered as technological performance characteristics to evaluate the machinability of hardened AISI 4340 steel. Additionally, the effect of various fluid properties like thermal conductivity, viscosity, surface tension and contact angle were examined for all nanofluids. Three set of nanofluid samples were prepared using Al2O3, CuO and Fe2O3 with rice bran oil and their various properties are analysed at 0.1% concentration. On comparison among these three nanofluids used, CuO nanofluid exhibited superior behavior followed by Fe2O3 nanofluids while Al2O3 nanofluid was last in the row

Analysis, predictive modelling and multi-response optimization in electrical discharge machining of Al-22%SiC metal matrix composite for minimization of surface roughness and hole overcut

Due to the widespread engineering applications of metal matrix composites especially in automotive, aerospace, military, and electricity industries; the achievement of desired shape and contour of the machined end product with intricate geometry and dimensions that are very challenging task. This experimental investigation deals with electrical discharge machining of newly engineered metal matrix composite of aluminum reinforced with 22 wt.% of silicon carbide particles (Al-22%SiC MMC) using a brass electrode to analyze the machined part quality concerning surface roughness and overcut. Forty-six sets of experimental trials are conducted by considering five machining parameters (discharge current, gap voltage, pulse-on-time, pulse-off-time and flushing pressure) based on Box-Behnken's design of experiments (BBDOEs). This article demonstrates the methodology for predictive modeling and multi-response optimization of machining accuracy and surface quality to enhance the hole quality in Al-SiC based MMC, employing response surface methodology (RSM) and desirability function approach (DFA). Finally, a novel approach has been proposed for economic analysis which estimated the total machining cost per part of rupees 211.08 during EDM of Al-SiC MMC under optimum machining conditions. Thereafter, under the influence of discharge current several observations are performed on machined surface morphology and hole characteristics by scanning electron microscope to establish the process. The result shows that discharge current has the significant contribution (38.16% for Ra, 37.12% in case of OC) in degradation of surface finish as well as the dimensional deviation of hole diameter, especially overcut. The machining data generated for the Al-SiC MMC will be useful for the industry

Experimental investigation, modelling and optimization in hard turning of high strength low alloy steel (AISI 4340)

The present study addresses the machinability investigation of high strength low alloy grade AISI 4340 steel with coated ceramic tools on surface roughness, tool wear, and economic analysis by considering the hard turning process parameters such as cutting speed, feed and depth of cut. Twenty seven set of trials according to full factorial design of experiments are conducted. Analysis of variance, multiple regression method, Taguchi method, desirability function approach and Gilbert’s technique are employed for parametric influence study, predictive modelling, response optimization, tool life estimation followed by cost analysis. Results indicated that feed and cutting speed are the most significant and crucial factors for hard turning operation in order to achieve minimum surface roughness of machined component as well as flank wear of cutting tool. Abrasions, adhesion followed by plastic deformation are the key wear mechanisms of coated ceramic insert, resulted 47 min of tool life under optimum cutting conditions and ensued lower total machining cost per part ($ 0.29 only) due to higher tool life, and reduced downtime that justifies cost effectiveness of hard turning. Novelty aspects, the current research work demonstrates the substitution of conventional, expensive and slow cylindrical grinding process, and proposes the most expensive CBN tool alternative using coated ceramic tools in hard turning process from techno-economical and ecological point of views in line with the industrial requirements