On the Assessment of Surface Quality and Productivity Aspects in Precision Hard Turning of AISI 4340 Steel Alloy: Relative Performance of Wiper vs. Conventional Inserts

This article reports an experimental assessment of surface quality generated in the precision turning of AISI 4340 steel alloy using conventional round and wiper nose inserts for different cutting conditions. A three-factor (each at 4 levels) full factorial design of experiment was followed for feed rate, cutting speed, and depth of cut, with resulting machined surface quality characterized by resulting average roughness (Ra). The results show that, for the provided range of cutting conditions, lower surface roughness values were obtained using wiper inserts compared with conventional inserts, indicating a superior performance. When including the type of insert as a qualitative factor, ANOVA revealed that the type of insert was most important in determining surface roughness and material removal rate, with feed rate as the second most significant, followed by the interaction of feed rate and type of insert. It was found that using wiper inserts allowed simultaneous increases in feed rate, cutting speed, and depth of cut, while providing better surface quality of lower Ra, compared to the global minimum value that could be achieved using the conventional insert. These findings show that wiper inserts produce better surface quality and a material removal rate up to ten times higher than that obtained with conventional inserts. This clearly indicates the tremendous advantages of high surface quality and productivity that wiper inserts can offer when compared with the conventional round nose type in precision hard turning of AISI 4340 alloy steel

Towards an Adaptive Design of Quality, Productivity and Economic Aspects When Machining AISI 4340 Steel With Wiper Inserts

The continuous pursue of sustainable manufacturing is motivating the utilization of new advanced technology, especially for hard to cut materials. In this study, an adaptive approach for optimization of machining process of AISI 4340 using wiper inserts is proposed. This approach is based on advance yet intuitive modeling and optimization techniques. The approach is based on Artificial Neural Network (ANN), Multi-Objective Genetic Algorithm (MOGA), as well as Linear Programming Techniques for Multidimensional Analysis of Preference (LINMAP), for modeling, optimization and multi-criteria decision making respectively. This integrated approach, to best of the authors’ knowledge, has been deployed for the first time to adaptively serve different designs of manufacturing processes. Such designs have different orientations, namely cost, quality, productivity, and balanced orientation. The capability of the proposed approach to serving such diverse requirements answers one of the most accelerating demands in the manufacturing community due to the dynamics of the uprising smart production lines. Besides, the proposed approach is presented in a straightforward manner that can be extended easily to other design orientations as well as other engineering applications. Based on the proposed design, a balanced general setting of 197.4 m/min, 0.95 mm, and 0.168 mm/rev was recommended along with other settings for more sophisticated requirements. Confirmatory experiments showed a good agreement (i.e., no more than 7% deviation) with the predicted optimum responses. This shows the validity of the proposed approach as a viable tool for designers to promote holistic and sustainable process design

Effect of Wiper Edge Geometry on Machining Performance While Turning AISI 1045 Steel in Dry Conditions Using the VIKOR-ML Approach

AISI 1045 can be machined well in all machining operations, namely drilling, milling, turning, broaching and grinding. It has many applications, such as crankshafts, rollers, spindles, shafts, and gears. Wiper geometry has a great influence on cutting forces (Fr, Ff, Fc and R), temperature, material removal rate (MRR) and surface roughness (Ra). Wiper inserts are used to achieve good surface quality and avoid the need to buy a grinding machine. In this paper, an optimization-based investigation into previously reported results for Taguchi’s based L27 orthogonal array experimentations was conducted to further examine effect of the edge geometry on the turning performance of AISI 1045 steel in dry conditions. Three input parameters used in current research include the cutting speed (Vc), feed (f) and depth of cut (ap), while performance measures in this research were Ra, Fr, Ff, Fc, R, temperature (temp) and MRR. The Vise Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method was used to normalize and convert all the performance measures to a single response known as the VIKOR-based performance index (Vi). The machine learning (ML) approach was used for the prediction and optimization of the input variables. A correlation plot is developed between the input variable and Vi using the ML approach. The optimized setting suggested by Vi-ML is Vc: 160 m/min; ap: 1 mm and f: 0.135 mm/rev, and the corresponding value of Vi was 0.2883, while the predicted values of Ra, Fr, Ff, Fc, R, temp and MRR were 2.111 µm, 43.85 N, 159.33 N, 288.13 N, 332,16 N, 554.4 °C and 21,600 mm3/min, respectively

Investigation and Statistical Analysis for Optimizing Surface Roughness, Cutting Forces, Temperature, and Productivity in Turning Grey Cast Iron

This paper investigated the influence of cutting parameters, including feed rate, cutting speed, tool nose radius, and wet or dry cutting conditions, on the resultant force, cutting edge/workpiece temperature, and surface roughness when turning grey cast iron. Results showed that increasing the feed rate increased the resultant force, cutting temperature, and surface roughness. At the same time, increasing the cutting speed and nose radius increased the cutting temperature, which in turn reduced the resultant force. For practical applications, basic mathematical calculations based on the sole effect of each parameter on the output of the experiments were used to estimate the extent of percentage increase in cutting temperature due to increasing feed rate, cutting speed, and nose radius. Similarly, the same approach was used to estimate the effect of increasing feed rate, cutting speed, and nose radius on average surface roughness. Results showed that increasing the feed rate increases the cutting temperature by 5 to 11% depending on the nose radius and cutting speed. On the other hand, increasing the cutting speed was found to have limited effect on cutting temperature with small nose radius whereas this effect increases with increasing the nose radius reaching about 11%. Increasing the nose radius also increases the cutting temperature, depending mainly on cutting speed, reaching a maximum of 21% at higher cutting speeds. Results also showed that increasing the feed rate increased the average surface roughness considerably to about 120% at high cutting speeds and a large nose radius. On the other hand, increasing the cutting speed and nose radius reduced the surface roughness (i.e., improved surface quality) by a maximum of 29 and 23%, respectively. In order to study the combined effects of the cutting parameters on the three responses, namely, the resultant cutting force, cutting temperature, and surface roughness, full factorial design and ANOVA were used, where it was found to be in good agreement with mathematical calculations. Additionally, the desirability function optimization tool was used to minimize the measured responses whilst maximizing the material removal rate

Comparative Study into Microstructural and Mechanical Characterization of HVOF-WC-Based Coatings

The main objective of this work was to characterize and compare the microstructural and mechanical properties as well as erosion resistance of WC-12Co and WC-10Co-4Cr coatings. The High Velocity Oxy Fuel (HVOF) process was applied to carbon manganese steel API 2H typically used in oil and gas industries. Microstructural characterization of feedstock powder and coatings was conducted using scanning electron microscope (SEM), energy dispersive X-ray spectroscopic (EDS) analysis, X-ray diffraction (XRD) for phase determination, powder particle size distribution, and surface roughness measurement. The average particle size of the former powder was 13.7 µm whereas it was 28.1 µm for the latter. The results showed that the smaller particle size tends to melt easier than the larger one, as deduced from SEM images and surface roughness measurements. EDS and XRD results of both coatings indicated the occurrence of WC decomposition where the powder particle size plays a significant role in these results. Mechanical characterization was discussed through comparing hardness, erosion, and adhesion test results of both coatings. WC-10Co-4Cr coating exhibited higher hardness than WC-12Co as well as higher erosion resistance, due to the extent of decomposition of WC and also to carbide particle size within the coating layer; these are the same reasons for the superior adhesion strength of the former coating compared to the latter one as per ASTM Standard “C633- 13”

WC-Co and WC-Co-Cr Coatings for the Protection of API Pipeline Steel from Corrosion in 4% NaCl Solution

Two inorganic coatings, namely 88%WC-12%Co (PSC1) and 86%WC-10%Co-4%Cr (PSC2), were deposited on the surface of an API-2H pipeline steel using high velocity oxy-fuel deposition. The corrosion of the uncoated and coated API-2H steel after their immersion in a solution of 4.0% NaCl for 1 h, 24 h, and 48 h has been studied. Various electrochemical measurements such cyclic potentiodynamic polarization, electrochemical impedance spectroscopy, and potentiostatic current versus time were employed. The surface morphology and analysis were carried out via the use of scanning electron microscopy and energy dispersive X-ray examinations. All experiments have revealed that the deposited coatings decreased the cathodic current, anodic current, corrosion current density (jCorr), absolute current versus time, and the corrosion rate (RCorr) compared to the uncoated API-2H steel. The value of jCorr decreased from 47 µA/cm2 for uncoated steel to 38 µA/cm2 for the PSC1-coated steel and 29 µA/cm2 for the PSC2-coated steel. Moreover, prolonging the time of exposure decreases the jCorr and RCorr values. The jCorr values obtained after 48 h recorded 32, 26, and 20 µA/cm2 for the uncoated, PSC1, and PSC2 samples, respectively. Moreover, applying these coatings also led to increasing the corrosion resistance (RP) after all the exposure periods of time. In addition, the PSC2 coating was found to be more protective against corrosion for the surface of the steel than the PSC1 coating

Cr3C2-NiCr Coating for the Protection of API Steel Corrosion in Concentrated Sodium Chloride Solution

In the present work, a layer of 75%Cr3C2−25%NiCr with thickness of 260 ± 15 µm was coated onto the API-2H pipeline steel surface using high-velocity oxy-fuel deposition. The effect of 75%Cr3C2−25%NiCr coating on the corrosion of the API steel after 1 h, 24 h, and 48 h exposure in 4.0% sodium chloride solutions is reported. The corrosion tests were performed using potentiodynamic cyclic polarization, electrochemical impedance spectroscopy, and chronoamperometric current–time techniques along with scanning electron microscopy and energy-dispersive X-ray analyses. The curves of polarization indicated that the presence of the coating increases the corrosion resistance of the steel through decreasing its corrosion current and corrosion rate. Impedance data showed that all resistances recorded higher values for the coated API steel. Chronoamperometric current–time measurements confirmed that the coated API steel has lower absolute current values and thus lower corrosion rate. All results proved that the presence of 75%Cr3C2−25%NiCr coating enhances the corrosion resistance of the API steel via the formation of a protective layer of Cr and Ni oxides, which could lead to decreasing the corrosion rate

Prediction Model of Cutting Parameters for Turning High Strength Steel Grade-H: Comparative Study of Regression Model versus ANFIS

The Grade-H high strength steel is used in the manufacturing of many civilian and military products. The procedures of manufacturing these parts have several turning operations. The key factors for the manufacturing of these parts are the accuracy, surface roughness (Ra), and material removal rate (MRR). The production line of these parts contains many CNC turning machines to get good accuracy and repeatability. The manufacturing engineer should fulfill the required surface roughness value according to the design drawing from first trail (otherwise these parts will be rejected) as well as keeping his eye on maximum metal removal rate. The rejection of these parts at any processing stage will represent huge problems to any factory because the processing and raw material of these parts are very expensive. In this paper the artificial neural network was used for predicting the surface roughness for different cutting parameters in CNC turning operations. These parameters were investigated to get the minimum surface roughness. In addition, a mathematical model for surface roughness was obtained from the experimental data using a regression analysis method. The experimental data are then compared with both the regression analysis results and ANFIS (Adaptive Network-based Fuzzy Inference System) estimations