Influence of minimum quantity lubrication parameters on tool wear and surface roughness in milling of forged steel

The minimum quantity of lubrication (MQL) technique is becoming increasingly more popular due to the safety of environment.Moreover,MQL technique not only leads to economical benefits by way of saving ...     

Experimental Investigation on the Compatibility of Nanoparticles with Vegetable Oils for Nanofluid Minimum Quantity Lubrication Machining

Several health and environmental related issues caused by the application of traditional cutting fluids in machining can be solved by implementing eco-friendly technologies such as minimum quantity lubrication (MQL). Moreover, nanofluid MQL has been proposed to enhance the cooling/lubricating properties of pure MQL and displays significantly good results for machinability. However, the mechanism on compatibility of nanoparticles with cutting fluids has not been explored. In this study, nanoparticles with different hardness and vegetable oils with different viscosity were selected for nanofluids preparation. The end milling experiments were carried out on 7050 material by applying MQL with particularly prepared nanofluids. The cutting force and surface roughness were measured corresponding to the machining performance. The compatibility of hardness of nanoparticles with viscosity of base fluids has been evaluated, and the mechanism has been analyzed by new-designed tribology tests. Results show that canola oil-based diamond nanofluids MQL exhibit the lowest cutting force and natural77 oil-based diamond nanofluids perform the lowest surface roughness with reduction of 10.71 and 14.92%, respectively, compared to dry machining condition. The research is novel and contributes to the machining of such materials at the industry level.     

Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling

Influence of tool geometry on the quality of surface produced is well known and hence any attempt to assess the performance of end milling should include the tool geometry. In the present work, experimental studies have been conducted to see the effect of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on the machining performance during end milling of medium carbon steel. The first and second order mathematical models, in terms of machining parameters, were developed for surface roughness prediction using response surface methodology (RSM) on the basis of experimental results. The model selected for optimization has been validated with the Chi square test. The significance of these parameters on surface roughness has been established with analysis of variance. An attempt has also been made to optimize the surface roughness prediction model using genetic algorithms (GA). The GA program gives minimum values of surface roughness and their respective optimal conditions.     

Effect of magneto rheological minimum quantity lubrication on machinability,wettability and tribological behavior in turning of Monel K500 alloy

Abstract

The proposed work deals with the investigation of magnetorheological based minimum quantity lubrication of graphene oxide (GO) based jojoba oil as bio-lubricant on machinability and tool wear mechanism of turning Monel K500 alloy. Experiments were carried out for dry, flooded, minimum quantity lubrication (MQL) and magnetorheological (MR–MQL) conditions using medium duty lathe. The process parameters include the cutting speed 95, 110, 125?m/min, feed rate 0.050, 0.075, 0.1?mm/rev and depth of cut 0.25, 0.50, 0.75?mm for the output responses such as surface roughness, cutting temperature and tool flank wear. The results indicated that GO-based bio-lubricant MR–MQL reduced coefficient of friction (COF) of 0.051 and wetting angle of 6°, as well as improved machining performance such as cutting temperature of 145?°C, the surface roughness of 0.614?µm, flank wear of 0.18?mm with enhanced lubrication regime under extreme wear conditions.     

Study on minimum quantity lubrication in micro-grinding

This paper discusses the performance of the minimum quantity lubrication (MQL) in micro-grinding based on ground surface roughness and tool life. The effects of grinding and lubricating parameters on machining performance are studied. Experiments for dry grinding and grinding with pure air are also conducted for comparison. It is observed that surface roughness and tool life are improved with the application of MQL in micro-grinding. Experimental results show that efficient chip removal from the cutting zone in micro-grinding is important for achieving good surface finish and adequate tool life. The application of a small amount of cutting oil in MQL can significantly extend the tool life. In this study, the tool life in MQL is seven times longer than that in dry grinding and five times longer than that in grinding with air cooling. If the oil flow is surplus to requirements or the air flow is inadequate, excess oil will stay on the grinding tool after the grinding test. As a result, poor surface roughness is observed. The optimal lubrication conditions in this experimental exploration are the combination of an oil flow of 1.88?ml/h and an air flow of 25?L/min.     

Theoretical and experimental study of the chatter vibration in wet and MQL machining conditions in turning process

Turning is one of the most commonly used cutting processes for manufacturing components in production engineering. The turning process, in some cases, is accompanied by intense relative movements between tool and workpiece, which is called chatter vibrations. Chatter has been identified as a detrimental problem that adversely impacts surface finish, tool life, process productivity, and dimensional accuracy of the machined part. Cooling/Lubrication in the turning process is normally done for some reasons, including friction and force reduction, temperature decrement, and surface finish improvement. Wet cooling is a traditional cooling/lubrication process that has been used in machining since the past. Besides, a variety of new cooling and lubricating approaches have been developed in recent years, such as the minimum quantity lubrication (MQL), cryogenic cooling, nanolubrication, etc., due to ecological issues. Despite the importance of cooling/lubrication in machining, there is a lack of research on chatter stability in the presence of cutting fluid in cutting processes. In this study, the chatter vibration in turning process for two cooling/lubrication conditions of conventional wet and MQL is investigated. An integrated theoretical model is used to predict both the metal cutting force and the chatter stability lobe diagram (SLD) in turning process. This model involves deriving a math equation for predicting metal cutting force for both wet and MQL conditions using experimental training force data and a Genetic Expression Programming (GEP)-based regression model. Also, the traditional single degree of freedom chatter model is used here for predicting the SLDs. The chatter model is discussed and verified with experimental tests. Then, the experimental results of the tool's acceleration signal, work surface texture, surface roughness, chip shape, and tool wear are presented and compared for wet and MQL conditions. The results of this study show that the cooling/lubrication systems such as wet or MQL have a considerable effect on the SLDs. Also, the predicted results of metal cutting force and SLD for both wet and MQL techniques are in good agreement with the experimental data. Therefore, it is recommended that for each lubrication condition including wet, or MQL, the SLD be determined to achieve higher machinability.     

Comparative assessment of cooling conditions,including MQL technology on machining factors in an environmentally friendly approach

Manufacturers need to continuously improve productivity and reduce the most disadvantages. In the current work, an experimental study has been carried out in order to evaluate the influence of different cutting parameters on the various machining factors such as surface roughness, cutting force, cutting power, metal removal rate, and tool wear during turning of X210Cr12 steel using a multilayer-coated tungsten carbide insert with various nose radii (r). Tests are designed according to Taguchi’s L₁₈ (2¹ × 3⁴) orthogonal array. ANOVA has been performed to determine the effect of the cutting conditions, and mathematical models have been developed through response surface methodology (RSM). The results indicate that the feed rate and the tool nose radius are the main affecting factors on surface roughness while both tangential force and cutting power are affected mainly by the depth of cut followed by the feed rate and the nose radius. Other special tests of long term have been established in order to study the wear evolution and consequently to determine the tool life. The results indicate also that minimum quantity lubrication (MQL) leads to an important improvement in terms of the cutting tool life by a gain of 23~40% compared to wet and dry machining. It has been found that the MQL is an interesting way to minimize lubrication cost and protect operator health and the environment while keeping better machining quality.     

Surface roughness and cutting force prediction in MQL and wet turning process of AISI 1045 using design of experiments

This paper presents an investigation into the MQL (minimum quantity lubrication) and wet turning processes of AISI 1045 work material with the objective of suggesting the experimental model in order to predict the cutting force and surface roughness, to select the optimal cutting parameters, and to analyze the effects of cutting parameters on machinability. Fractional factorial design and central composite design were used for the experiment plan. Cutting force and surface roughness according to cutting parameters were measured through the external cylindrical turning based on the experiment plan. The measured data were analyzed by regression analysis and verification experiments were conducted to confirm the results. From the experimental results and regression analysis, this research project suggested the experimental equations, proposed the optimal cutting parameters, and analyzed the effects of cutting parameters on surface roughness and cutting force in the MQL and wet turning processes.     

Deformation behaviour of sintered silicon–copper steel preforms of various aspect ratios during cold upsetting

This paper presents the optimization of the face milling process of 7075 aluminum alloy by using the gray relational analysis for both cooling techniques of conventional cooling and minimum quantity lubrication (MQL), considering the performance characteristics such as surface roughness and material removal rate. Experiments were performed under different cutting conditions, such as spindle speed, feed rate, cooling technique, and cutting tool material. The cutting fluid in MQL machining was supplied to the interface of work piece and cutting tool as pulverize. An orthogonal array was used for the experimental design. Optimum machining parameters were determined by the gray relational grade obtained from the gray relational analysis.     

Modeling and Optimization of Machining Process in Discontinuously Reinforced Aluminium Matrix Composites

This article presents development of an Artificial Neural Networks (ANN) based model for the prediction of surface roughness during machining of composite material using Back Propagation algorithm. Statistically designed experiments based on Taguchi method were carried out on machining of Al/SiCp composite material. The experimentation helped generate a knowledge base for the ANN system and understand the relative importance of process, tool and work material dependent parameters on the roughness of surface generated during machining. The ANN model trained using the experimental data was found to predict the surface roughness with fair accuracy. An optimization approach was also proposed to obtain optimal cutting conditions that yield the desired surface roughness while maximizing the metal removal rate.     

Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations

Optimization of cutting parameters is valuable in terms of providing high precision and efficient machining. Optimization of machining parameters for milling is an important step to minimize the machining time and cutting force, increase productivity and tool life and obtain better surface finish. In this work a mathematical model has been developed based on both the material behavior and the machine dynamics to determine cutting force for milling operations. The system used for optimization is based on powerful artificial intelligence called genetic algorithms (GA). The machining time is considered as the objective function and constraints are tool life, limits of feed rate, depth of cut, cutting speed, surface roughness, cutting force and amplitude of vibrations while maintaining a constant material removal rate. The result of the work shows how a complex optimization problem is handled by a genetic algorithm and converges very quickly. Experimental end milling tests have been performed on mild steel to measure surface roughness, cutting force using milling tool dynamometer and vibration using a FFT (fast Fourier transform) analyzer for the optimized cutting parameters in a Universal milling machine using an HSS cutter. From the estimated surface roughness value of 0.71 μm, the optimal cutting parameters that have given a maximum material removal rate of 6.0×10³ mm³/min with less amplitude of vibration at the work piece support 1.66 μm maximum displacement. The good agreement between the GA cutting forces and measured cutting forces clearly demonstrates the accuracy and effectiveness of the model presented and program developed. The obtained results indicate that the optimized parameters are capable of machining the work piece more efficiently with better surface finish.     

An optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool for best surface finish

High-speed machining (HSM) has emerged as a key technology in rapid tooling and manufacturing applications. Compared with traditional machining, the cutting speed, feed rate has been great progress, and the cutting mechanism is not the same. HSM with coated carbide cutting tools used in high-speed, high temperature situations and cutting more efficient and provided a lower surface roughness. However, the demand for high quality focuses extensive attention to the analysis and prediction of surface roughness and cutting force as the level of surface roughness and the cutting force partially determine the quality of the cutting process. This paper presents an optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool to achieve minimum cutting forces and better surface roughness. Taguchi optimization method is the most effective method to optimize the machining parameters, in which a response variable can be identified. The standard orthogonal array of L₉ (3⁴) was employed in this research work and the results were analyzed for the optimization process using signal to noise (S/N) ratio response analysis and Pareto analysis of variance (ANOVA) to identify the most significant parameters affecting the cutting forces and surface roughness. For such application, several machining parameters are considered to be significantly affecting cutting forces and surface roughness. These parameters include the lubrication modes, feed rate, cutting speed, and depth of cut. Finally, conformation tests were carried out to investigate the improvement of the optimization. The result showed a reduction of 25.5% in the cutting forces and 41.3% improvement on the surface roughness performance.     

Effect of MQL flow rate on machinability of AISI 4140 steel

Abstract

Many studies were performed about the influence of minimum quantity lubrication (MQL) technique on cutting performance in the literature, but there is no paper examining the effect of different MQL flow rates and cutting parameters on machinability of AISI 4140 material as a whole. In this study, the effects of different MQL flow rates and cutting parameters on surface roughness, main cutting force and cutting tool flank wear (VB), with great importance among the machinability criteria, and forming as a result of the machining of AISI 4140, were revealed. At the end of the experiments, it was determined that rise of flow rate affected main cutting forces positively to a certain extent; yet, it exhibited no significant effect on surface roughness, but reduced VB. Also, it was observed that both main cutting force and surface roughness increased with the increase of feed, while generally decreased with the increase of cutting speed. It was seen that flank wear was positively affected by the increase in flow rate; and this decreased with the increase in flow rate. R² values obtained as 99.8% and 99.9% for main cutting forces and surface roughness values modeled statistically with the help of quadratic equations, respectively.     

车削奥氏体不锈钢时冷却参数对刀具振动和表面粗糙度的影响

针对最小量润滑(MQL)技术在加工难切削材料时冷却能力不足的问题,分析了最小量冷却润滑(MQCL)条件下冷却参数对刀具振动和表面粗糙度的影响规律。设计了以田口法为基础的正交试验方案,并基于MQCL条件进行了相关切削试验。采用方差分析法、主效应图法、响应面法等方法并结合切削理论,分析了冷风温度、油液流量、风速、喷射面类型等冷却参数对刀具振动和表面粗糙度的影响机制,建立了与冷却参数关联的加工刀具振动和表面粗糙度预测模型,同时采用改进的遗传算法对支持向量回归预测模型进行同步优化,得到冷却参数的最优值。试验结果表明,温度对刀具振动的影响最大且随着温度升高刀具振动呈现出增大的趋势;风速对表面粗糙度的影响最大,当风速小于10 m/s时,随着风速增大表面粗糙度增大,当风速大于10 m/s时,随着风速增大表面粗糙度减小。当喷射面为刀具副后刀面时,刀具振动和表面粗糙度均最小。冷却参数优化结果表明,当冷风温度为-2.36 ℃、风速为7.31 m/s、油液流量为300 mL/h、喷射面为副后刀面时,工件表面质量最好,其表面粗糙度Ra为0.6588 μm。验证实验表明,表面粗糙度和振动均方根的预测误差分别为4.4%和5.9%。     

Modeling and Optimization of Machining Process in Discontinuously Reinforced Aluminium Matrix Composites

Abstract

This article presents development of an Artificial Neural Networks (ANN) based model for the prediction of surface roughness during machining of composite material using Back Propagation algorithm. Statistically designed experiments based on Taguchi method were carried out on machining of Al/SiCp composite material. The experimentation helped generate a knowledge base for the ANN system and understand the relative importance of process, tool and work material dependent parameters on the roughness of surface generated during machining. The ANN model trained using the experimental data was found to predict the surface roughness with fair accuracy. An optimization approach was also proposed to obtain optimal cutting conditions that yield the desired surface roughness while maximizing the metal removal rate.     

最小量润滑在振动钻削中的应用

为了有效地发挥最小量润滑(Minimum quantity lubrication,MQL)在钻削加工中的冷却和润滑性能,把MQL和振动钻削技术结合起来,对MQL在钻削加工(尤其是振动钻削)中的作用效果进行理论分析和试验研究,并对加工中的最大轴向力和表面粗糙度进行测量.研究结果表明,与普通钻削相比,振动钻削能够有效改善MQL的作用效果,通过合理选择参数,能使最大轴向力明显减小,表面粗糙度显著改善;增大振幅是提高MQL作用效果的有效途径,能够降低表面粗糙度,提高加工质量.     

Dynamic surface generation modeling of two-dimensional vibration-assisted micro-end-milling

An integrated model is proposed to simulate the surface generation in two-dimensional vibration-assisted micro-end-milling (2-D VAMEM). The model includes the developed submodels as dynamic cutting force model, machining system response model, and machined surface generation algorithm. The effects of feed rate on cutting force and surface roughness are investigated through simulations. It is found that the cutting force increases while the surface roughness decreases with the increment of the feed rate when the feed per tooth is smaller than the tool edge radius. The trials have been carried out to evaluate and validate the proposed model and the simulation results. The integrated model contributes to the comprehensive understanding of the process of machined surface generation in 2-D VAMEM and will assist the machining operators to select optimal machining parameters.     

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.     

Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling

This paper discusses the use of Taguchi and response surface methodologies for minimizing the surface roughness in machining glass fiber reinforced (GFRP) plastics with a polycrystalline diamond (PCD) tool. The experiments have been conducted using Taguchi’s experimental design technique. The cutting parameters used are cutting speed, feed and depth of cut. The effect of cutting parameters on surface roughness is evaluated and the optimum cutting condition for minimizing the surface roughness is determined. A second-order model has been established between the cutting parameters and surface roughness using response surface methodology. The experimental results reveal that the most significant machining parameter for surface roughness is feed followed by cutting speed. The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the surface roughness in the machining of GFRP composites. The predicted values are confirmed by using validation experiments.     

Evaluation of machinability and economic performance in cryogenic-assisted hard turning of α-β titanium: a step towards sustainable manufacturing

Abstract

Titanium, a difficult-to-cut material, consumes higher time and cost in removing material by machining to produce parts. Machining of Ti alloys has got serious attention owing to its reactive nature with tool materials at elevated temperature that aggravates tool wear. Reportedly, effective and efficient cooling and lubrication at the tool–work interface can ameliorate the machinability of Ti-alloys. In this perspective, this article interrogates the underlying mechanism of critical responses such as surface roughness, temperature, tool life and machining cost under dry, minimum quantity lubrication (MQL) and cryogenic liquid nitrogen (LN2) modes. The effect of cutting speeds and feed rates on such responses have been considered as a function of cooling strategy to standardize the cooling technique as the best alternative for machining. Cryogenic cooling seems to be preponderant regarding machining cost, temperature, surface roughness and tool life in hard turning of a–b titanium alloy. The feasibility of cryogenic cooling was investigated using the iso-response technique in comparison with dry and MQL-assisted hard turning. Experimental results revealed longer tool life and lower machining cost under cryogenic condition followed by MQL and dry machining. Moreover, cryogenic LN2 has been identified as an appropriate alternative to reduce the temperature and surface roughness. On contrary, dry turning evoked a high-temperature and rapid tool wear. In a nutshell, cryogenic assisted hard turning has acceded as a sustainable strategy from an environmental and economic perspective.