Surface Roughness In Hard Research Articles

Study of Effects of MoS2 Nanofluid MQL Parameters on Cutting Forces and Surface Roughness in Hard Turning Using CBN Insert

Lubrication and cooling in hard machining is an urgent and growing concern. The use of a suitable cooling lubrication condition is a crucial factor and has a great influence on the machining efficiency and the machined surface quality in hard machining. Among the proposed technological solutions, minimum quantity lubrication (MQL) using nano-cutting oils is a novel solution and its effectiveness has been proven for hard turning. This work aims to investigate the influence of MQL technological parameters using MoS2 nano-cutting oil including nanoparticle concentration, air pressure, and air flow rate on surface roughness and the resultant cutting force in hard turning using CBN inserts. Box-Behnken optimal experimental design and ANOVA analysis were used to study the influence of the input parameters and determine the optimal values. The results present the influence of the survey parameters and provide technological guides for specific objective functions for further sustainable studies on MQL hard turning using nano-cutting oil.

Investigation and Modelling of Surface Roughness in Hard Turning of Sintered Tungsten Carbide (WC 25wt% Co) Using CBN Tool

Investigation and Modelling of Surface Roughness in Hard Turning of Sintered Tungsten Carbide (WC 25wt% Co) Using CBN Tool

Multi response optimization of surface roughness in hard turning with coated carbide tool based on cutting parameters and tool vibration

In the present work, the effects of cutting parameters on surface roughness parameters (Ra), tool wear parameters (VBmax), tool vibration (Vy) and material removal rate (MRR) during hard turning of AISI 4140 steel using coated carbide tool have been evaluated. The relationships between machining parameters and output variables were modeled using response surface methodology (RSM). Analysis of variance (ANOVA) was performed to quantify the effect of cutting parameters on the studied machining parameters and to check the adequacy of the mathematical model. Additionally, Multi-objective optimization based desirability function was performed to find optimal cutting parameters to minimize surface roughness, and maximize productivity. The experiments were planned as Box Behnken Design (BBD). The results show that feed rate influenced the surface roughness; the cutting speed influenced the tool wear; the feed rate influenced the tool vibration predominantly. According to the microscopic imagery, it was observed that adhesion and abrasion as the major wear mechanism.

Effects of Cutting Speed and Feed Rate on Surface Roughness in Hard Turning of AISI 4140 with Mixed Ceramic Cutting Tool

This work investigated the influences of cutting speed and feed rate on surface roughness in hard turning of AISI 4140 chromium molybdenum steel bar using mixed ceramic inserts Al2O3+TiC under dry condition for automotive industry applications. Turning experiments were conducted by varying cutting speed ranging from 150 to 220 m/min and feed rate ranging from 0.06 to 1 mm/rev. General factorial design was used to analyze the data set of surface roughness and determine statistically significant process factors based on analysis of variance results. The results showed that average surface roughness was significantly affected by feed rate and interaction between cutting speed and feed rate at the level of significance of 0.05. An optimal operating condition for hard turning of AISI 4140 with the ceramic cutting tool that produced a minimum machined surface roughness was obtained at cutting speed of 220 m/min and 0.06 mm/rev.

Statistical Analysis of Surface Roughness in Hard Turning: An Optimization Approach

Statistical Analysis of Surface Roughness in Hard Turning: An Optimization Approach

Research on Vibration and Surface Roughness in Hard Cutting Process of Hardened Steel

Vibration of cutting process is an important factor to influence the surface roughness. By Orthogonal design, take hard cutting surface roughness test for bearing steel GCr15 with 62~64HRC, analyzing the law of the variation of cutting parameters effect the amount of vibration and the surface roughness value. And it establishes the formula about vibration and surface roughness. The amount of vibration and the surface roughness increases first and then decreases while the cutting speed increases in a specific area, meanwhile its vibration will reduce and its surface roughness will enlarge while the feed increases. The cutting depth has a little influence to surface roughness and vibration, and the cutting depth is first decreased and then increased accompanied by the tool nose radius increases with the increase of vibration and the surface roughness. The research can provide reference for improving the surface quality.

Modeling and Predicting Surface Roughness in Hard Turning Using a Bayesian Inference-Based HMM-SVM Model

This study proposes a hybrid model for evaluating surface roughness in hard turning using a Bayesian inference-based hidden Markov model and least squares support vector machine (HMM-SVM). The model inputs are multidirectional fusion features that are extracted from the acquired monitoring signals through independent component analysis and singular spectrum analysis. Based on a detailed analysis of the workpiece surface formation mechanism, the cutting vibration signals are determined as monitoring signals and an experimental scheme based on the multifeed rate is designed. The error rate of HMM-SVM is further reduced by introducing the stratification factor comparison method rather than using the conventional probability comparison method. A five-step iterative algorithm is presented to select and optimize the training set, which effectively solves the problems of precision degradation and training data insufficiency. Experimental studies show that the proposed model can accurately predict the surface roughness in case of missing samples. The advantages of the proposed model over least squares support vector machine (LSSVM) and multiple regression approaches are demonstrated via statistical analysis. Note to Practitioners-As an alternative to traditional grinding, hard turning is an attractive machining method, in which surface quality is a crucial measurement index. However, under the scenario of sample missing, a straightforward and relatively accurate model for predicting surface roughness is challenging to establish using conventional strategies. This paper reports on a new HMM-SVM model based on Bayesian inference for modeling and predicting surface roughness in hard turning. The samples are classified based on the accuracy grade of surface roughness according to the GB/T1031-2009 standard using the expectation maximization algorithm and HMM, which is superior in small-sample classification problem. LSSVM is employed to estimate surface roughness. The effectiveness of the proposed model is demonstrated through experimental investigations. The reported methodology can also be extended to other related fields.

Development of Surface Roughness in Hard Turning of 100Cr6 Using Mixed Ceramic Cutting Tool with Wiper Geometry and Conventional Geometry

Abstract Hard turning has been applied in machining since the early 1980s. There is an effort to substitute finish grinding by hard machining, because of machining by cutting tool with defined geometry. For machining of hardened steels (up to 45 HRC) are used two different cutting materials. PCBN are used the most for discontinuous machining of hardened steel (up to 63 HRC) and mixed ceramic tools, which are used in the experiment. This paper reports a development of surface roughness parameters when using wiper tool geometry of mixed ceramic tool and conventional geometry of mixed ceramic tool in hard turning. Roughness parameters (Ra, Rz, Rsk, Rku, RSm, Rdq) are measured when changing the feed, depth of cut and cutting speed are constant.

Estimation and Optimization Cutting Conditions of Surface Roughness in Hard Turning Using Taguchi Approach and Artificial Neural Network

One of the most important requirements of part manufacturing is the surface quality. This is so because the most important part is meeting the specific requirements of customers. The surface roughness is a leading indicator of the quality of the machined surface Parts. In the present work in an experimental study to achieve by application of Taguchi method to investigate the effect of three parameters, which known as cutting speeds of (45, 90, and 135 m/min), feed rate of (0.1, 0.2, and 0.3 mm/rev), and cut depth of (0.05, 0.1, and 0.15 mm) on performance measure of surface roughness (Ra). Thus to determine the optimal levels and to analyze the cutting parameter’s effect on the surface finish values by employing different method of Orthogonal array, S/N ratio, analysis of variance (ANOVA). During our work two models for prediction have been used. The first one is known as the method of regression analysis, and the second is the method of Adaptive - Neural Network (ANN) relying on practical results. The achieved results show that the estimation and prediction ability of neural networks is better than the regression analysis. Experimental results confirmed with optimal levels of the machining parameters which are clarified by using Taguchi optimization method. Also, the indicated results of the Taguchi’s method show its ability to improve the process.