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This study aims to investigate the effect of vibration on ceramic tools under dry cutting conditions and find the optimum cutting condition for the hardened steel machining process in a computer numerical control (CNC) lathe machine.

In this research, an integrated fuzzy TOPSIS-based Taguchi L9 optimization model has been applied for the multi-objective optimization (MOO) of the hard-turning responses. Additionally, the effect of vibration on the ceramic tool wear was investigated using Analysis of Variance (ANOVA) and Fast Fourier Transform (FFT).

The optimum cutting conditions for the multi-objective responses were obtained at 98 m/min cutting speed, 0.1 mm/rev feed rate and 0.2 mm depth of cut. According to the ANOVA of the input cutting parameters with respect to response variables, feed rate has the most significant impact (53.79%) on the control of response variables. From the vibration analysis, the feed rate, with a contribution of 34.74%, was shown to be the most significant process parameter influencing excessive vibration and consequent tool wear.

The MOO of response parameters at the optimum cutting parameter settings can significantly improve productivity in the dry turning of hardened steel and control over the input process parameters during machining.

Most studies on optimizing responses in dry hard-turning performed in CNC lathe machines are based on single-objective optimization. Additionally, the effect of vibration on the ceramic tool during MOO of hard-turning has not been studied yet.

The efficient and economical use of natural resources is a big issue. Machine elements with a rolling contact are highly relevant because of their wide application in technical systems and a large production quantity. Innovative hard machining can reduce the friction and increase the fatigue strength of rolling element bearings. The purpose of this study is to focus on the surface properties of such parts.

A new model to predict bearing fatigue life is presented which takes compressive residual stresses in the bearing subsurface area into consideration. The investigated bearings were machined by the processes of hard turning, hard turning with subsequent deep rolling and a combination of hard turning and deep rolling (turn-rolling) in one process step. Changes in the residual stress state during bearing fatigue tests were investigated and the influence of residual stresses on the bearings fatigue life was researched.

Both combinations including the deep rolling process decrease the surface roughness and induce compressive residual stresses. As a result, the L₁₀ fatigue life of roller bearings was increased by the factor of 2.5. Owing to the developed models, this effect can be considered within the design process.

In the context of the research program “Resource efficient Machine Elements (SPP1551),” machining processes of bearings were investigated regarding the bearing fatigue life. By inducing beneficial residual stresses on the bearings’ subsurface area, the fatigue life could be increased. Thus higher resource efficiency was achieved. To increase the productivity, a combination of hard turning and deep rolling was evaluated.

This paper aims to examine the performance of the machining parameters used in the hard-turning process of DIN 1.2738 mold steel and identify the optimum machining conditions.

Experiments were carried out via the Taguchi L18 orthogonal array. The evaluation of the experimental results was based on the signal/noise ratio. The effect levels of the control factors on the surface roughness and flank wear were specified with analysis of variance performed. Two different multiple regression analyses (linear and quadratic) were conducted for the experimental results. A higher correlation coefficient (R²) was obtained with the quadratic regression model, which showed values of 0.97 and 0.95 for Ra and Vb, respectively.

The experimental results indicated that generally better results were obtained with the TiAlN-coated tools, in respect to both surface roughness and flank wear. The Taguchi analysis found the optimum results for surface roughness to be with the cutting tools of coated carbide using physical vapor deposition (PVD), a cutting speed of 160 m/min and a feed rate of 0.1 mm/rev, and for flank wear, with cutting tools of coated carbide using PVD, a cutting speed of 80 m/min and a feed rate of 0.1 mm/rev. The results of calculations and confirmation tests for Ra were 0.595 and 0.570 µm, respectively, and for the Vb, 0.0244 and 0.0256 mm, respectively. Developed quadratic regression models demonstrated a very good relationship.

Optimal parameters for both Ra and Vb were obtained with the TiAlN-coated tool using PVD. Finally, confirmation tests were performed and showed that the optimization had been successfully implemented.

In this study, the tri-bological behaviour of the un-coated and diamond coated tungsten carbide was evaluated using the pin-on-disc test rig. The same was also tested on a lathe machine tool. This paper aims to compare the tri-bological behaviour of coated tungsten carbide pin with un-coated tungsten carbide pin it also correlates the wear obtained from the two machines used.

Experiments were performed using L8 orthogonal array and results obtained on a pin-on-disc test rig under dry sliding process were optimized through a modern optimization technique i.e. genetic algorithm (GA). The response surface methodology model (L8 orthogonal array) formed the basis for the development of the GA model, which defines the conditions of minimum wear, minimum coefficient of friction and minimum surface roughness for the sliding process of the pin-on-disc test rig.

Implementation of the heuristic approach for optimization of input parameters for the combination of tool material used for the turning process. The initial approach involves tri-bological testing considering the same combination. The set of experiments further performed, inferred that the results were similar and that the diamond coating enhances the life of the tool.

Successfully synthesized the diamond coating on tungsten carbide tool material. Implantation of the heuristic approach, i.e. GA to tri-bological tests to identify the optimized level of input variables. Experimentation involves the tri-bological testing whose results were confirmed through performing experiments on the lathe machine tool.

This paper aims to solve the problems of large hard switching loss and unclear resonant parameter design in the existing inverter power supply topology.

This paper proposes a simple and reliable two-stage isolated inverter composed of series quasi-resonant push-pull and external freewheeling diode full-bridge inverter. The power supply topology is analyzed, the topology mode is analyzed, the mathematical model of the converter is established and the DC gain of the converter is deduced. The relationship between the load and the output gain of the resonant tank is presented, a new resonant parameter design method is proposed, and the parameter design of the resonant element of the converter is clarified.

The resonant components of the converter are designed according to the proposed resonant parameter design method, and the correctness of the method is verified by simulation and the development and testing of a 500 W experimental prototype. After experimental tests, the peak efficiency of the experimental prototype can reach 94%. Because the experimental prototype achieves soft switching, the heat generation of the switch is greatly reduced, so the heavy heat sink is removed, and the volume is reduced by about 30% compared with the traditional power supply, and the total harmonic distortion of the output voltage is about 2%.

The feasibility of the scheme is verified by experiments, which is of great significance for improving the efficiency of the inverter power supply and parameter optimization.

The purpose of this paper is to study the cutting performance of high-speed regime end milling of AISI 4340 by investigating the tool life and wear mechanism of steel using the minimum quantity lubrication (MQL) technique to deliver the cutting fluid.

The experiments were designed using Taguchi L9 orthogonal array with the parameters chosen: cutting speed (between 300 and 400 m/min), feed rate (between 0.15 and 0.3 mm/tooth), axial depth of cut (between 0.5 and 0.7 mm) and radial depth of cut (between 0.3 and 0.7 mm). Toolmaker microscope, optical microscope and Hitachi SU3500 Variable Pressure Scanning Electron Microscope used to measure tool wear progression and wear mechanism.

Cutting speed 65.36%, radial depth of cut 24.06% and feed rate 6.28% are the cutting parameters that contribute the most to the rate of tool life. The study of the tool wear mechanism revealed that the oxide layer was observed during lower and high cutting speeds. The former provides a cushion of the protective layer while later reduce the surface hardness of the coated tool

A high-speed regime is usually carried out in dry conditions which can shorten the tool life and accelerate the tool wear. Thus, this research is important as it investigates how the use of MQL and cutting parameters can prolong the usage of tool life and at the same time to achieve a sustainable manufacturing process.

The present study spotlights the single and multicriteria decision-making (MCDM) methods to determine the optimal machining conditions and the predictive modeling for surface roughness (Ra) and cutting tool flank wear (VB) while hard turning of AISI 4340 steel (35 HRC) under dry environment.

In this study, Taguchi L16 design of experiments methodology was chosen. The experiments were performed under dry machining conditions using TiSiN-TiAlN nanolaminate PVD-coated cutting tool on which Taguchi and responses surface methodology (RSM) for single objective optimization and MCDM methods like the multi-objective optimization by ratio analysis (MOORA) were applied to attain optimal set of machining parameters. The predictive models for each response and multiresponse were developed using RSM-based regression analysis. S/N ratios, analysis of variance (ANOVA), Pareto diagram, Tukey's HSD test were carried out on experimental data for profound analysis.

Optimal set of machining parameters were obtained as cutting speed: at 180 m/min., feed rate: 0.05 mm/rev., and depth of cut: 0.15 mm; cutting speed: 145 m/min., feed rate: 0.20 mm/rev. and depth of cut: 0.1 mm for Ra and VB, respectively. ANOVA showed feed rate (96.97%) and cutting speed (58.9%) are dominant factors for Ra and VB, respectively. A remarkable improvement observed in Ra (64.05%) and VB (69.94%) after conducting confirmation tests. The results obtained through the MOORA method showed the optimal set of machining parameters (cutting speed = 180 m/min, feed rate = 0.15 mm/rev and depth of cut = 0.25 mm) for minimizing the Ra and VB.

This work contributes to realistic application for manufacturing industries those dealing with AISI 4340 steel of 35 HRC. The research contribution of present work including the predictive models will provide some useful guidelines in the field of manufacturing, in particular, manufacturing of gear shafts for power transmission, turbine shafts, fasteners, etc.

The article chronicles the use of a Civil War soldier's diary to teach Civil War history. The project was carried out over a two-semester period in a social studies pedagogy class. The professor and students in cooperative learning groups and whole group discussions interpreted the diary. In this unique process, they also discussed how primary sources might be used to teach middle school and high school students United States History as a dynamic process of real people and not simply facts.

The purpose of this paper is to investigate the influence of turning parameters such as cutting speed, feed rate and depth of cut on tool flank wear and machined surface quality of AISI 304 stainless steel during environment friendly turning under nanofluid minimum quantity lubrication (NMQL) conditions using PVD-coated carbide cutting inserts.

Turning experiments are conducted as per the central composite rotatable design under the response surface methodology. ANOVA and regression analysis are employed to examine significant cutting parameters and develop mathematical models for VB (tool flank wear) and R_a (surface roughness). Multi-response desirability optimization approach is used to investigate optimum turning parameters for simultaneously minimizing VB and R_a.

Optimal input turning parameters are observed as follows: cutting speed: 168.06 m/min., feed rate: 0.06 mm/rev. and depth of cut: 0.25 mm with predicted optimal output response factors: VB: 106.864 µm and R_a: 0.571 µm at the 0.753 desirability level. ANOVA test reveals depth of cut and cutting speed-feed rate interaction as statistically significant factors influencing tool flank wear, whereas cutting speed is a dominating factor affecting surface roughness. Confirmation tests show 5.70 and 3.71 percent error between predicted and experimental examined values of VB and R_a, respectively.

AISI 304 is a highly consumed grade of stainless steel in aerospace components, chemical equipment, nuclear industry, pressure vessels, food processing equipment, paper industry, etc. However, AISI 304 stainless steel is considered as a difficult-to-cut material because of its high strength, rapid work hardening and low heat conductivity. This leads to lesser tool life and poor surface finish. Consequently, the optimization of machining parameters is necessary to minimize tool wear and surface roughness. The results obtained in this research can be used as turning database for the above-mentioned industries for attaining a better machined surface quality and tool performance under environment friendly machining conditions.

Turning of AISI 304 stainless steel under NMQL conditions results in environment friendly machining process by maintaining a dry, healthy, clean and pollution free working area.

Machining of AISI 304 stainless steel under vegetable oil-based NMQL conditions has not been investigated previously.

In the current scenario, new materials are gaining popularity due to higher specific properties of strength and stiffness, increase in wear resistance, dimensional stability at higher temperature, etc. Subsequently, the need for precise machining has also been increased enormously. The purpose of this paper is to study the surface roughness during the turning of Al-10%SiC and Al-5%SiC-5%Gr composites under different cutting conditions.

Artificial neural network (ANN) has been effectively employed in solving problems with effortless computation in the areas such as fault diagnosis, process identification, property estimation, data smoothing and error filtering, product design and development, optimisation and estimation of activity coefficients. Response surface method is also used to analyse the problems involving a number of input parameters and their corresponding relationship between one or more measured dependent responses. Using Design Expert.8 evaluation software package, a simpler and more efficient statistical RSM model has been designed. RSM models are created by using 27 experimental data measurements obtained from different turning conditions of aluminium alloy composites.

In this work, the surface roughness during turning of Al-10%SiC and Al-5%SiC-5%Gr composites under different cutting conditions has been studied. The surface roughness value is proportional with the increase in feed rate and depth of cut while inversely proportional with the cutting speed. In all turning conditions, Al-10%SiC composite has lower surface roughness values than Al-5%SiC-5%Gr hybrid composite. An ANN and response surface models have been developed to predict the surface roughness of machined surface. The experimental results concur well with predicted models.

In the present trend, new materials are gaining popularity due to higher specific properties of strength and stiffness, increase in wear resistance, dimensional stability at higher temperature, etc. Subsequently, the need for precise machining has also been increased enormously. In this work, the surface roughness during turning of Al-10%SiC and Al-5%SiC-5%Gr composites under different cutting conditions has been studied.