Optimization of tool geometry parameters for turning operations based on the response surface methodology

This investigation focuses on the influence of tool geometry on the surface finish obtained in turning of AISI 1040 steel. In order to find out the effect of tool geometry parameters on the surface roughness during turning, response surface methodology (RSM) was used and a prediction model was developed related to average surface roughness (Ra) using experimental data. The results indicated that the tool nose radius was the dominant factor on the surface roughness. In addition, a good agreement between the predicted and measured surface roughness was observed. Therefore, the developed model can be effectively used to predict the surface roughness on the machining of AISI 1040 steel with in 95% confidence intervals ranges of parameters studied.     

An integrated study of surface roughness for modelling and optimization of cutting parameters during end milling operation

The aim of this study is to develop an integrated study of surface roughness to model and optimize the cutting parameters when end milling of AISI 1040 steel material with TiAlN solid carbide tools under wet condition. A multiple regression analysis using analysis of variance is conducted to determine the performance of experimental measurements and to show the effect of four cutting parameters on the surface roughness. Artificial neural network (ANN) based on Back-propagation (BP) learning algorithm is used to construct the surface roughness model exploiting a full factorial design of experiments. Genetic algorithm (GA) supported with the tested ANN is utilized to determine the best combinations of cutting parameters providing roughness to the lower surface through optimization process. GA improves the surface roughness value from 0.67 to 0.59 μm with approximately 12% gain. Then, machining time has also decreased from 1.282 to 1.0316 min by about 20% reduction based on the cutting parameters before and after optimization process using the analytical formulas. The final measurement experiment has been performed to verify surface roughness value resulted from GA with that of the material surface by 3.278% error. From these results, it can be easily realized that the developed study is reliable and suitable for solving the other problems encountered in metal cutting operations as the same as surface roughness.     

Machinability investigation in hard turning of AISI D3 cold work steel with ceramic tool using response surface methodology

The hard turning process has been attracting interest in different industrial sectors for finishing operations of hard materials. In this paper, the effects of cutting speed, feed rate, and depth of cut on surface roughness, cutting force, specific cutting force, and power in the hard turning were experimentally investigated. An experimental investigation was carried out using ceramic cutting tools, composed approximately with (70 %) of Al₂O₃ and (30 %) of TiC, in surface finish operations on cold work tool steel AISI D3 heat-treated to a hardness of 60 HRC. Based on 3³ full factorial designs, a total of 27 tests were carried out. The range of each parameter is set at three different levels, namely, low, medium, and high. Analysis of variance is used to check the validity of the model. Experimental observations show that higher cutting forces are required for machining harder work material. This cutting force gets affected mostly by feed rate followed by depth of cut. Feed rate is the most influencing factor on surface roughness. Feed rate followed by depth of cut become the most influencing factors on power; especially in case of harder workpiece. Optimum cutting conditions are determined using response surface methodology (RSM) and the desirability function approach. It was found that, the use of lower depth of cut value, higher cutting speed, and by limiting the feed rate to 0.12 and 0.13 mm/rev, while hard turning of AISI D3 hardened steel, respectively, ensures minimum cutting forces and better surface roughness. Higher values of depth of cut are necessary to minimize the specific cutting force.     

TURNING STUDIES OF DEEP CRYOGENIC TREATED P-40 TUNGSTEN CARBIDE CUTTING TOOL INSERTS – TECHNICAL COMMUNICATION

In the present work, coated tungsten carbide tool inserts of ISO P-40 grade were subjected to deep cryogenic treatment at ?176°C. Turning studies were conducted on AISI 1040 workpieces using both untreated and deep cryogenic treated tungsten carbide cutting tool inserts. The turning performance was evaluated in terms of flank wear of the cutting tool inserts, main cutting force and surface finish of the machined workpieces. The flank wear of deep cryogenic treated carbide tools was observed to be lower than that of untreated carbide tools in machining of AISI 1040 steel. The cutting force during machining of AISI 1040 steel was lower with the deep cryogenic treated carbide tools when compared with the untreated carbide tools. The surface finish produced on machined AISI 1040 steel workpieces was superior with the deep cryogenic treated carbide tools as compared to the untreated carbide tools.     

Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regression

In this study, models for predicting the surface roughness of AISI 1040 steel material using artificial neural networks (ANN) and multiple regression (MRM) are developed. The models are optimized using cutting parameters as input and corresponding surface roughness values as output. Cutting parameters considered in this study include cutting speed, feed rate, depth of cut, and nose radius. Surface roughness is characterized by the mean (R _a) and total (R _t) of the recorded roughness values at different locations on the surface. A total of 81 different experiments were performed, each with a different setting of the cutting parameters, and the corresponding R _a and R _t values for each case are measured. Input–output pairs obtained through these 81 experiments are used to train an ANN is achieved at the 200,00th epoch. Mean squared error of 0.002917120% achieved using the developed ANN outperforms error rates reported in earlier studies and can also be considered admissible for real-time deployment of the developed ANN algorithm for robust prediction of the surface roughness in industrial settings.     

Laser Composite Surfacing of Ni-WC Coating on AA5083 for Enhancing Tribomechanical Properties

Laser composite surfacing (LCS) has emerged as an alternative photon-driven manufacturing technology for the fabrication of composite coatings to enhance the tribomechanical properties of various aluminum alloys. The current research presents an analysis on optimization of laser processing parameters for Ni-WC composite coating deposited on AA5083 aluminum alloy in order to improve its tribomechanical properties. To carry out the investigation, Taguchi's optimization method using a standard L₁₆ (3⁴) orthogonal array was employed. Thereafter, the results were analyzed using signal-to-noise (S/N) ratio response analysis and Pareto analysis of variance (ANOVA). Finally, confirmation tests with the best parameter combinations obtained in the optimization process were made to demonstrate the progress made. Results showed that the surface hardness (953 H_v) and roughness (0.81 μm) of coated AA5083 samples was enhanced by 9.27 and 13.14%, respectively. The tribological behavior of LCS samples was investigated using a ball-on-plate tribometer against a counterbody of 440c steel. It was revealed that the wear of the Ni-WC-coated samples improved by around 2.5 times. For lower applied loads, the coating exhibited an abrasive wear mode and a reduction in plastic deformation.     

Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning

This paper presents an approach for modeling and prediction of both surface roughness and cutting zone temperature in turning of AISI304 austenitic stainless steel using multi-layer coated (TiCN?+?TiC?+?TiCN?+?TiN) tungsten carbide tools. The proposed approach is based on an adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (PSO) learning. AISI304 stainless steel bars are machined at different cutting speeds and feedrates without cutting fluid while depth of cut is kept constant. ANFIS for prediction of surface roughness and cutting zone temperature has been trained using cutting speed, feedrate, and cutting force data obtained during experiments. ANFIS architecture consisting of 12 fuzzy rules has three inputs and two outputs. Gaussian membership function is used during the training process of the ANFIS. The surface roughness and cutting zone temperature values predicted by the PSO-based ANFIS model are compared with the measured values derived from testing data set. Testing results indicate that the predicted surface roughness and cutting zone temperature are in good agreement with measured roughness and temperature.     

Slurry Erosion–Corrosion of Carburized AISI 5117 Steel

The erosion–corrosion of carburized and untreated low alloy steel (AISI 5117) has been investigated using slurry whirling-arm test rig. Erosion–corrosion tests were carried out in slurries composed of sand particles and either tap water or 3 % NaCl solution. The tests were carried out with particles concentration of 1 wt% and slurry stream impact velocity of 15 m/s. Silica sand having a nominal size range of 250–355 μm was used as an erodent. It has been shown that the erosion and erosion–corrosion resistance of AISI 5117 low alloy steel can be effectively improved by carburizing for all impact angles. However, the effectiveness of carburizing was the highest for an impact angle of 45°, where the erosion and erosion–corrosion resistance were increased by 60–40 %, respectively, compared with that of the untreated material. The results showed that the treated and untreated specimens behaved as ductile materials under erosion and erosion–corrosion tests, and the maximum mass loss occurred at an impact angle of 45°. SEM analysis showed that the erosion tracks developed on the untreated specimens were wider and deeper than that formed on the carburized specimens for erosion and erosion–corrosion tests.     

Tribological Properties of Borided AISI 4140 Steel with the Powder Pack-Boriding Method

The present study evaluates the tribological properties of boride layers on the surface of AISI 4140 steel, formed using the pack-boriding method. Commercial EKabor^®2 was used as the boronizing agent and the treatment was carried out at 900, 950, 1000, and 1050 °C for 2, 4, and 6 h, respectively. X-ray diffraction (XRD), scanning electron microscopy (SEM), and microhardness tests were used to characterize the phase composition, microstructure, and local hardness, respectively, of the borided steel samples. Block-on-disc tests were used to investigate tribological properties. Abrasive wear tests were carried out using emery paper at a fixed sliding speed and three different loads. Adhesive wear tests were executed against AISI 52100 steel at a fixed load and distance. The coefficient of friction values (COF) of the samples were determined simultaneously during the tests. The weight loss and COF of the borided samples were compared with untreated samples and the results suggest that both wear resistance and friction properties of the AISI 4140 steel improve with boriding.     

Microstructure,chemical composition,wear, and corrosion resistance of FeB–Fe<Subscript>2</Subscript>B–Fe<Subscript>3</Subscript>B surface layers produced on Vanadis-6 steel using CO<Subscript>2</Subscript> laser

The paper presents the study results of laser modification of FeB–Fe₂B surface layers produced on Vanadis-6 steel using pack cementation method. Microstructure, x-ray phase analysis, chemical composition study using wave dispersive spectrometry method, microhardness, corrosion resistance as well as surface condition, roughness, and wear resistance were investigated. The diffusion boronizing processes were performed at 900 °C for 5 h in the EKabor® powder mixture. The boronized layers had a dual-phase microstructure composed of two types of iron borides, FeB and Fe₂B, and their microhardness ranged from 1800 to 1400 HV. The laser surface modification was carried out on specimens after diffusion boronizing process using CO₂ laser with a nominal power of 2600 W. Laser beam power used in this experiment was equal to 1040 W and was constant. While the three values of scanning speed were used: 19, 48, and 75 mm/s. During laser modification, the multiple tracks were made where distance between of axis tracks was equal to 0.5 mm. As a result of this process, microstructure consisted of remelted zone, heat-affected zone, and substrate was obtained. In remelted zone, the boron-martensite eutectic was observed. Boronized layers after laser modification were characterized by the mild gradient of microhardness from surface to the substrate and their value was dependent on the scanning speed used and was between 1700 and 1100 HV. Corrosion resistance tests revealed reducing the current of corrosion in case of laser modification process. Wear resistance of laser modified specimens was improved in comparison to diffusion boronized layers.     

ANN-based prediction of surface and hole quality in drilling of AISI D2 cold work tool steel

This paper focuses on artificial neural network (ANN)-based modeling of surface and hole quality in drilling of AISI D2 cold work tool steel with uncoated titanium nitride (TiN) and titanium aluminum nitride (TiAlN) monolayer- and TiAlN/TiN multilayer-coated-cemented carbide drills. A number of drilling experiments were conducted at all combinations of different cutting speeds (50, 55, 60, and 65 m/min) and feed rates (0.063 and 0.08 mm/rev) to obtain training and testing data. The experimental results showed that the surface roughness (Ra) and roundness error (Re) values were obtained with the TiN monolayer- and TiAlN/TiN multilayer-coated drills, respectively. Using some of the experimental data in training stage, an ANN model was developed. To evaluate the performance of the developed ANN model, ANN predictions were compared with the experimental results. It was found that the determination coefficient values are more than 0.99 for both training and test data. Root mean square error and mean error percentage values were very low. ANN results showed that ANN can be used as an effective modeling technique in accurate prediction of the Ra and Re.     

Performance assessment of microwave treated WC insert while turning AISI 1040 steel

This study attributed to post treatment of tungsten carbide (WC) inserts using microwave irradiation. Tungsten carbide inserts were subjected to microwave radiation (2.45 GHz) to enhance its performance in terms of reduction in tool wear rate, cutting force surface roughness and improvement in tool life. Performance of tungsten carbide insert is very much affected by machine operating parameters i.e. speed, feed and depth of cut. An attempt has been made to investigate the effects of machining parameters on microwave treated tool inserts. This paper describes the comparative study of machining performance of untreated and microwave treated WC tool inserts used for turning of AISI 1040 steel. Machining performance has been evaluated in terms of flank wear, cutting force, surface roughness, tool wear mechanisms. Critical examinations of tool wear mechanisms and improvements in metallurgical properties such as microstructural change, phase activation of WC grains were identified using scanning electron microscope (SEM). Results obtained from the turning using the microwave treated tool inserts showed a significant reduction tool wear thereby enhancing the surface quality of workpiece.     

A Novel Method for Quantitative Determination of Ultra-low Wear Rates of Materials, Part II: Effects of Surface Roughness and Roughness Orientation on Wear

A novel method of measurement of the very low wear-rates of materials in the ultra-mild wear regime, which involves the use of implanted gold as a marker, was used to understand the effects of surface roughness and roughness orientation on wear under reciprocating sliding conditions. AISI 1095 steel coupons with various Vickers hardness values and different surface roughness and roughness orientation relative to the sliding direction were tested under the same sliding conditions. It was found that parallel sliding causes more wear compared with transverse sliding for the harder samples (Vickers hardness (VH); 450 HV, 650 HV and 1000 HV). Furthermore, the average friction coefficient of parallel sliding is also higher than that of transverse sliding for these samples. Severe wear takes place when the samples are too soft (250 HV), resulting in the complete loss of implanted gold. Surface topographic images were taken before and after the wear tests. It was found that parallel sliding dramatically increases the surface roughness, while transverse sliding does not increase the surface roughness for harder samples (450 HV, 650 HV and 1000 HV). For the soft sample (250 HV), the surface roughness increases significantly under parallel or transverse sliding.     

Wear Characteristics of Traditional Manganese Phosphate and Composite hBN Coatings

In this study, the tribological properties of traditional manganese phosphate coatings and composite hBN coatings

composed of nano-hexagonal boron nitride (hBN) in layered manganese phosphate crystals on AISI 1040 steel were investigated. Wear tests were carried out under controlled temperature and humidity using ball-on-disc tribometers for samples that were either submerged in oil or retaining oil on their surfaces at a sliding speed of 2.5 cm/s with loads of 1, 3, 5, and 10 N and sliding distances of 40, 80, 100, and 120 m. The surface profiles before and after the tests were used to characterize the wear. The surfaces of the coated samples were examined using scanning electron microscopy (SEM). The coefficients of friction and wear rates of the coated samples were also measured. The average wear rates of the composite hBN-coated samples were significantly lower than those of the traditional manganese phosphate–coated samples for each of the loading conditions for the oil submersion and retained oil tests. The coefficient of friction (COF) values for the traditional manganese phosphate–coated samples did not change significantly with increasing load. The COF values for the composite hBN coated–samples decreased with increasing load in the oil submersion test.     

Dissimilar metal laser spot joining of steel to aluminium in conduction mode

Dissimilar joining of thin (～1 mm) 6111-T4 aluminium alloy and DC04 uncoated low carbon steel used in automobile structures was carried out using laser spot joining in conduction mode. Two sets of experiments were carried out, using copper and aluminium backing bars, respectively. The welds were produced in overlap configuration with steel on the top. The steel surface was irradiated by the laser, and the heat was conducted through the steel into the aluminium. Temperature at the interface was controlled using the fundamental laser energy parameters so that aluminium melts and wets the steel surface. Reaction between the two metallic alloys resulted in the formation of intermetallic compounds (IMC). The formation pattern of IMC was dependent on the temperature profile and the distribution across the interface and was thicker in the centre of the weld and thinner near the edges. The stoichiometry of the IMC formed was varied across the layer and was principally composed of two different layers of Fe₂Al₅ and FeAl₃. Micro hardness tests were carried out to characterise the IMC layer. Mechanical shear tensile tests showed a maximum joint shear strength of up to 68 % of the shear strength of the aluminium alloy.     

Surface roughness and geometrical characterization of ultra-thick micro moulds for ceramic micro fabrication using soft lithography

Ceramic micro fabrication using soft lithography is a well-known technique used to pattern high-aspect-ratio microstructures. The quality of the pattern highly relies on the quality of the moulds. This paper describes the surface roughness and geometrical characterization of soft lithography technique for the fabrication of ceramic micro components. Effects of patterning materials and methods on the produced geometry, repeatability and surface roughness were studied and a comparative study was performed for the optimization process. UV lithography of BPR100 and SU-8 and deep reactive ion etching are the methods for fabricating the master mould. In addition, polydimethylsiloxane and Dragon Skin elastomeric have been used in the fabrication of the soft moulds. Mould geometry was inspected using SEM images while surface roughness was measured using stereo imaging. The results show that the maximum obtained thicknesses are 1,000, 250 and 500 μm for SU-8, BPR100 and DRIE moulds. In addition, their surface roughness values are higher than the SU-8. On the other hand, Dragon Skin shows demoulding problems despite it has smoother surface than PDMS mould.     

Investigation of mechanical properties of friction-welded AISI 304 with AISI 1021 dissimilar steels

Austenitic stainless steel and low alloy steels are extensively used in various automotive, aerospace, nuclear, chemical, and other general purpose applications. Joining of dissimilar metals is one of the challenging tasks and most essential need of the present-day industry. It has been observed that a wide range of dissimilar materials can be easily integrated by friction welding. The objectives of the present investigation were obtaining weldments between austenitic stainless steel (AISI 304) with low alloy steel (AISI 1021) and optimizing the friction welding parameters in order to establish the weld quality. In the present study, an experimental setup was designed in order to achieve friction welding of plastically deformed austenitic stainless steel and low alloy steel. AISI 304 and AISI 1021 steels were welded by friction welding using five different axial pressures at 1,430 rpm. The joining performances of friction-welded dissimilar joints were studied, and influences of these process parameters on the mechanical properties of the friction-welded joints were estimated. The joint strength was determined with tensile testing, and the fracture behavior was examined by scanning electron microscopy (SEM) and was supported and backed by energy dispersive spectroscopy (EDS) analysis. Furthermore, the proposed joints were tested for impact strength, and the microhardness across the joint was also evaluated.     

High-performance circular sawing of AISI 1045 steel with cermet and tungsten carbide inserts

This work investigated the influence of cutting speed and feed rate on cutting forces, surface roughness, and slot width circular sawing of AISI 1045 steel. The effects of tool material (cermet and tungsten carbide) and geometry (chip breaker flute and pre-cutting/post-cutting teeth) were also investigated. Thrust and radial forces generally tended to decrease as the cutting speed increased and tended to increase with the feed rate. The lowest values of thrust and radial forces were obtained using a tungsten carbide saw ground with precutting and post-cutting teeth. With regard to the quality of the machined wall, the lowest surface roughness was obtained by applying the highest cutting speed and lowest feed rate and employing a cermet brazed saw. Under this condition, roughness values comparable to face turning and parting off operations were obtained. The cermet brazed saw was responsible for producing the narrowest slot widths.     

Sustainability analyses of cutting edge radius on specific cutting energy and surface finish in side milling processes

The aim of this work is to determine the influence of cutting edge radius on the specific cutting energy and surface finish in a mechanical machining process. This was achieved by assessing the direct electrical energy demand during side milling of aluminium AW6082-T6 alloy and AISI 1018 steel in a dry cutting environment using three different cutting tool inserts. The specific energy coefficient was evaluated as an index of the sustainable milling process. The surface finish of the machined parts was also investigated after machining. It was observed that machining with the 48.50-μm cutting edge radius insert resulted in lower specific cutting energy requirements when compared with the 68.50 and 98.72-μm cutting edge radii inserts, respectively. However, as the ratio of the undeformed chip thickness to cutting edge radius is less than 1, the surface roughness increases. The surface roughness values gradually decrease as the ratio of undeformed chip thickness to cutting edge radius (h/r _e) tends to be 1 and at minimum surface roughness values when the ratio of h/r _e equalled to 1. However, the surface roughness values increased as h/r _e becomes higher than 1. This machining strategy further elucidates the black box and trade-offs of ploughing and rubbing characteristics of micro machining and optimization strategy for minimum energy and sustainable manufacture.     

The influence of surface roughness on material dislocation of microindentation using bonded interface technique

ABSTRACT

This paper presents the investigation of the surface roughness on the material dislocation using bonded interface technique. The influence of the surface roughness on three different materials was focused on this study. By using the bonded interface technique, the half-specimens of stainless steel, aluminium and brass were bonded and microindentations were conducted on the bonded interface surfaces with roughnesses ranged from 0.05 to 0.37?µm. The results revealed that the increase of surface roughness induced an exponential decrease in material dislocation in vertical direction beneath the permanent indent. Using Nix-Gao model, rougher surface produced higher load independent hardness which the materials experience the hardening effect. From the observation on the plastic deformation zone, the shear bands of harder material stainless showed limited material dislocation compared to softer material aluminium and brass.