Improving surface integrity in finish machining of Inconel 718 alloy using intelligent systems

In machining of hard materials, surface integrity is one of the major customer requirements which comprise the study of the changes induced to the workpiece. Surface roughness and residual stress are often considered as the most significant indications of surface integrity. Inducing tensile residual stress during the machining processes is a critical problem which should be avoided or minimized to obtain better service quality and component life. This problem becomes more evident in the presence of rough machined surface because fatigue life of manufactured components might be decreased significantly. Inconel 718 superalloy is one of the hard materials used extensively in the aerospace industries. It is prone to tensile residual stress in machined surface. Thus, controlling and optimizing residual stress and surface roughness in machining of Inconel 718 are so needed. Intelligent techniques based on the predictive and optimization models can be used efficiently for this purpose. In this study, the optimal machining parameters including cutting speed, depth of cut, and feed rate were accessed by intelligent systems to evaluate the state of residual stress and surface roughness in finish turning of Inconel 718. The results of experiments and analyses indicated that implemented techniques in this work provided a robust framework for improving surface integrity in machining of Inconel 718 alloy. It was shown that cutting speed has more effect on surface integrity than other investigated parameters. Also, depth of cut and feed rate were found in the moderate range to obtain satisfactory state of tensile residual stress and surface roughness.     

An experimental analysis and measurement of process performances in machining of nimonic C-263 super alloy

Nimonic C-263 alloy is extensively used in the fields of aerospace, gas turbine blades, power generators and heat exchangers because of its unique properties. However, the machining of this alloy is difficult due to low thermal conductivity and work hardening characteristics. This paper presents the experimental investigation and analysis of the machining parameters while turning the nimonic C-263 alloy, using whisker reinforced ceramic inserts. The experiments were designed using Taguchi’s experimental design. The parameters considered for the experiments are cutting speed, feed rate and depth of cut. Process performance indicators, viz., the cutting force, tool wear and surface finish were measured. An empirical model has been created for predicting the cutting force, flank wear and surface roughness through response surface methodology (RSM). The desirability function approach has been used for multi response optimization. The influence of the different parameters and their interactions on the cutting force, flank wear and surface roughness are also studied in detail and presented in this study. Based on the cutting force, flank wear and surface roughness, optimized machining conditions were observed in the region of 210 m/min cutting speed and 0.05 mm/rev feed rate and 0.50 mm depth of cut. The results were confirmed by conducting further confirmation tests.     

Investigations of flank wear, cutting force, and surface roughness in the machining of Al-6061�CTiB2 in situ metal matrix composites produced by flux-assisted synthesis

This paper presents the results of an experimental investigation on the machinability of in situ Al-6061?CTiB₂ metal matrix composite (MMC) prepared by flux-assisted synthesis. These composites were characterized by scanning electron microscopy, X-ray diffraction, and micro-hardness analysis. The influence of reinforcement ratio of 0, 3, 6, and 9?wt.% of TiB₂ on machinability was examined. The effect of machinability parameters such as cutting speed, feed rate, and depth of cut on flank wear, cutting force and surface roughness were analyzed during turning operations. From the test results, we observe that higher TiB₂ reinforcement ratio produces higher tool wear, surface roughness and minimizes the cutting forces. When machining the in situ MMC with high speed causes rapid tool wear due to generation of high temperature in the machining interface. The rate of flank wear, cutting force, and surface roughness are high when machining with a higher depth of cut. An increase in feed rate increases the flank wear, cutting force and surface roughness.     

Experimental investigation and performance analysis of cemented carbide inserts of different geometries using Taguchi based grey relational analysis

In this work, effect of machining parameters cutting speed, feed rate and depth of cut, geometrical parameters cutting insert shape, relief angle and nose radius were investigated and optimized using Taguchi based grey relational analysis. 18 ISO designated uncoated cemented carbide inserts of different geometries were used to turn practically used automotive axles to study the influence of variation in carbide inserts geometry. Performance measures viz., flank wear, surface roughness and material removal rate (MRR) were optimized using grey relational grade, based on the experiments designed using Taguchi’s Design of Experiments (DoE). A weighted grey relational grade is calculated to minimize flank wear and surface roughness and to maximize MRR. Analysis of variance shows that cutting insert shape is the prominent parameter followed by feed rate and depth of cut that contributes towards output responses. An experiment conducted with identified optimum condition shows a lower flank wear and surface roughness with higher MRR. The confirmation results obtained are confirmed by calculating confidence interval, which lies within the width of the interval.     

Influence of machining parameters on the machinability of particulate reinforced Al/SiC–MMC

The paper presents the result of an experimental investigation on the machinability of silicon carbide particulate aluminium metal matrix composite during turning using a rhombic uncoated carbide tool. The influence of machining parameters, e.g. cutting speed, feed and depth of cut on the cutting force has been investigated. The influence of the length of machining and cutting time on the tool wear and the influence of various machining parameters, e.g. cutting speed, feed, depth of cut on the surface finish criteria has been analyzed through the various graphical representations. The combined effect of cutting speed and feed on the flank wear has also been investigated. The influence of cutting speed, feed and depth of cut on the tool wears and built-up edge is analyzed graphically. The job surface condition and wear of the cutting tool edge for the different sets of experiments have been examined and compared for searching out the suitable cutting condition for effective machining performance during turning of Al/SiC-MMC. Test results show that no built-up edge is formed during machining of Al/SiC-MMC at high speed and low depth of cut. From the test results and different SEM micrographs, suitable range of cutting speed, feed and depth of cut can be selected for proper machining of Al/SiC-MMC.     

Evaluation of machining performance in cryogenic machining of Inconel 718 and comparison with dry and MQL machining

There has been significant work on establishing relationships between machining performance and the cutting parameters for various work materials. Recent trends in machining research show that major efforts are being made to understand the impact of various cooling/lubrication methods on machining performance and surface integrity characteristics, all aimed at improving process and product performance. This study presents the experimental results of cryogenic machining of Inconel 718, a high-temperature aerospace alloy, and comparison of its performance in dry and minimum quantity lubrication machining. Experimental data on force components, progressive tool wear parameters such as flank wear, notch wear, crater wear, cutting temperature, chip morphology, and surface roughness/topography of machined samples are presented. New findings show that cryogenic machining is a promising research direction for machining of high-temperature aerospace alloy, Inconel 718, as it offers improved machining performance in terms of reduced tool wear, temperature, and improved surface quality. It was also found that the number of nozzles in cryogenic machining plays a vital role in controlling cutting forces and power consumption in cryogenic machining of Inconel 718.     

The performance Of DLC-coated and uncoated ultra-fine carbide tools in micromilling of Inconel 718

Coating is an important factor that affects cutting-tool performance. In particular, it directly affects surface quality and burr formation in the micro milling process. After the micromechanical machining process, surface quality is very hard to increase by a second process (grinding, etc.). In addition, in micromechanical machining, the cutting tool needs to have a good resistance to wear, owing to the fact that the cutting process is carried out at high speed. In this study, the machinability of Inconel 718 superalloy was investigated, using a Diamond Like Carbon (DLC) coated tool. The experimental tests were carried out in dry cutting conditions for different feed rates and depth of cuts. It was found that the dominant wear mechanism for all cutting parameters was identified to be abrasive and diffusive wear. Besides, a significantly Built Up Edge (BUE) formation was observed in uncoated tool. The results clearly show that DLC coating significantly decreased BUE. In addition, a smaller cutting force and better surface roughness were obtained with a DLC-coated tool. In conclusion, DLC coating can be used in micro milling of Inconel 718. It reduces the BUE and burr formation, improves surface roughness.     

镍基合金Inconel718可切削加工性的试验研究

Inconel718是一种高强度耐热镍基合金,具有优良的高温强度、高温硬度和耐蚀性,在高温条件下能长期工作,已被广泛地应用于宇航工业、航空工业的涡轮发动机和相关零件的制造。分析Inconel718的机械性能、微观组织结构及其对切削加工性能的影响并进行了相关的试验验证,在试验数据的基础上,研究Inconel718中含碳量对切削过程中刀具磨损的影响。试验结果表明,Inconel718中含碳量在刀具后刀面磨损中起着非常重要的作用,Inconel718合金中含碳量越高,合金中所含的细微硬质夹杂物也越多,在切削过程中使刀具产生严重的后刀面磨粒磨损,从而降低材料的切削加工性。     

高速车削Inconel 718切削力的试验研究

为了研究高速切削Inconel 718的切削力经验公式和各切削参数对切削力的影响显著程度,应用涂层硬质合金刀具对Inconel 718进行了正交车削试验,得到了硬质合金刀具车削Inconel 718的切削力经验公式。分析结果表明:对切削力影响最大的因素是进给量,切削深度和切削速度对试验结果的影响依次减弱。用涂层硬质合金刀具KC5510精车Inconel 718时,采用小进给量、小切削深度、高切削速度可以得到小的切削力,取得良好的切削效果。     

Multi-objective optimization of surface roughness and cutting forces in high-speed turning of Inconel 718 using Taguchi grey relational analysis (TGRA)

In this paper, a new effective approach, Taguchi grey relational analysis has been applied to experimental results in order to optimize the high-speed turning of Inconel 718 with consideration to multiple performance measures. The approach combines the orthogonal array design of experiments with grey relational analysis. Grey relational theory is adopted to determine the best process parameters that give lower magnitude of cutting forces as well as surface roughness. The response table and the grey relational grade graph for each level of the machining parameters have been established. The parameters: cutting speed, 475?m/min; feed rate, 0.10?mm/rev; depth of cut, 0.50?mm; and CW2 edge geometry have highest grey relational grade and therefore are the optimum parameter values producing better turning performance in terms of cutting forces and surface roughness. Depth of cut shows statistical significance on overall turning performance at 95% confidence interval.     

Modeling and optimization of Al/SiCp MMC machining using Taguchi approach

This paper presents the influence of process parameters like cutting speed, feed and depth of cut on flank wear (VBc) and surface roughness (Ra) in turning Al/SiCp metal matrix composites using uncoated tungsten carbide insert under dry environment. The experiments have been conducted based on Taguchi’s L₉ orthogonal array. Abrasion and adhesion are observed to be the principal wear mechanism from images of tool tip. No premature tool failure by chipping and fracturing was observed and machining was steady using carbide insert. Built-up-edge formation is noticed at low and higher cutting speed and at high feed combination and consequently surface quality affected adversely. The optimal parametric combination for flank wear and surface roughness are found to be v1–f1–d3 and v3–f1–d3 respectively and is greatly improved through Taguchi approach. Mathematical models for flank wear and surface roughness are found to be statistically significant.     

Study of surface quality in machining nickel-based alloy Inconel 718

In machining operation, the surface quality is one of the most important requirements for many workpieces. Because of the special physical and chemical properties, good-machined surface quality becomes a key issue to solve in machining Inconel 718. In this paper, PVD-TiAlN-coated carbide tools were used to turn Inconel 718. Based on observing the tool wear and machined surface morphology, the main factors affecting surface quality at different cutting speeds were analyzed. The optimal cutting temperature was calculated, according to the above analysis and Archard adhesion wear model, and further cutting parameters optimization was conducted, on the basis of the proposed optimal cutting temperature. The optimized cutting parameters based on optimal cutting temperature can be considered to improve surface quality.     

Optimization techniques for machining operations: a retrospective research based on various mathematical models

Simulated annealing, genetic algorithm, and particle swarm optimization techniques have been used for exploring optimal machining parameters for single pass turning operation, multi-pass turning operation, and surface grinding operation. The behavior of optimization techniques are studied based on various mathematical models. The objective functions of the various mathematical models are distinctly different from each other. The most affecting machining parameters are considered as cutting speed, feed, and depth of cut. Physical constraints are speed, feed, depth of cut, power limitation, surface roughness, temperature, and cutting force.     

Some studies on hard turning of AISI 4340 steel using multilayer coated carbide tool

Hard turning with multilayer coated carbide tool has several benefits over grinding process such as, reduction of processing costs, increased productivities and improved material properties. The objective was to establish a correlation between cutting parameters such as cutting speed, feed rate and depth of cut with machining force, power, specific cutting force, tool wear and surface roughness on work piece. In the present study, performance of multilayer hard coatings (TiC/TiCN/Al₂O₃) on cemented carbide substrate using chemical vapor deposition (CVD) for machining of hardened AISI 4340 steel was evaluated. An attempt has been made to analyze the effects of process parameters on machinability aspects using Taguchi technique. Response surface plots are generated for the study of interaction effects of cutting conditions on machinability factors. The correlations were established by multiple linear regression models. The linear regression models were validated using confirmation tests. The analysis of the result revealed that, the optimal combination of low feed rate and low depth of cut with high cutting speed is beneficial for reducing machining force. Higher values of feed rates are necessary to minimize the specific cutting force. The machining power and cutting tool wear increases almost linearly with increase in cutting speed and feed rate. The combination of low feed rate and high cutting speed is necessary for minimizing the surface roughness. Abrasion was the principle wear mechanism observed at all the cutting conditions.     

Suppression of notch wear of a whisker reinforced ceramic tool in air-jet-assisted high-speed machining of Inconel 718

This paper describes the notch and flank wear specific to a SiC whisker reinforced alumina tool in air jet assisted (AJA) turning of nickel-base superalloy Inconel 718 at high cutting speeds. An AJA machining experiment has revealed that the air jet applied to the tool tip in addition to coolant dramatically reduces the depth-of-cut notch wear. As a result, the width of flank wear, but not the size of notch wear, determined the life of a ceramic tool in AJA machining of Inconel 718. This is a reason for the large extension and small variation of the tool life when high speed AJA machining is adopted. The maximum tool life length reached 2160 m at a cutting speed of 660 m/min under the given cutting conditions. Finally, the mechanisms of the notch and flank wear of a SiC whisker reinforced alumina tool in AJA machining are discussed from the viewpoints of tribochemical reactions and tool wear anisotropy.     

Precision machining of an aluminum alloy piston reinforced with a cast iron insert

Bimetallic pistons consisting of aluminum alloy reinforced with a cast iron (CI) insert are used to reduce the weight and improve the wear resistance of pistons. A major problem with machining such bimetallic pistons is producing the desired shape with minimal cutting forces and without damaging the bonding registry. The objective of this paper is to determine the optimal cutting parameters (cutting speed, feed, and depth of cut) for turning bimetallic pistons. When machining, we wish to obtain optimal values of the cutting forces and a better surface integrity while maintaining the required surface finish. Experiments were conducted following Taguchi’s parameter design approach using a cubic boron nitride tool for the machining. The results indicate that the process parameters affected the mean and variance of the cutting force at the Al-CI interface of the piston. The Al-CI interface was examined using an ultrasonic piston bond tester after machining to assure the bond quality. The surface roughness of the components was measured with a surface roughness tester. A mathematical model was developed using the Systat 12.0 software package to establish the relationship between the input quantities (speed, feed, and depth of cut) and the output data (cutting force). The output data of the mathematical model were compared with the experimental results. The results from the Taguchi robust design concept were compared with the results obtained from a nonconventional Genetic Algorithm optimization technique.     

Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts

In this paper, the Taguchi method and regression analysis have been applied to evaluate the machinability of Hadfield steel with PVD TiAlN- and CVD TiCN/Al₂O₃-coated carbide inserts under dry milling conditions. Several experiments were conducted using the L₁₈ (2 × 3 × 3) full-factorial design with a mixed orthogonal array on a CNC vertical machining center. Analysis of variance (ANOVA) was used to determine the effects of the machining parameters on surface roughness and flank wear. The cutting tool, cutting speed and feed rate were selected as machining parameters. The analysis results revealed that the feed rate was the dominant factor affecting surface roughness and cutting speed was the dominant factor affecting flank wear. Linear and quadratic regression analyses were applied to predict the outcomes of the experiment. The predicted values and measured values were very close to each other. Confirmation test results showed that the Taguchi method was very successful in the optimization of machining parameters for minimum surface roughness and flank wear in the milling the Hadfield steel.     

Prediction of tool wear using regression and ANN models in end-milling operation

Tool wear prediction plays an important role in industry for higher productivity and product quality. Flank wear of cutting tools is often selected as the tool life criterion as it determines the diametric accuracy of machining, its stability and reliability. This paper focuses on two different models, namely, regression mathematical and artificial neural network (ANN) models for predicting tool wear. In the present work, flank wear is taken as the response (output) variable measured during milling, while cutting speed, feed and depth of cut are taken as input parameters. The Design of Experiments (DOE) technique is developed for three factors at five levels to conduct experiments. Experiments have been conducted for measuring tool wear based on the DOE technique in a universal milling machine on AISI 1020 steel using a carbide cutter. The experimental values are used in Six Sigma software for finding the coefficients to develop the regression model. The experimentally measured values are also used to train the feed forward back propagation artificial neural network (ANN) for prediction of tool wear. Predicted values of response by both models, i.e. regression and ANN are compared with the experimental values. The predictive neural network model was found to be capable of better predictions of tool flank wear within the trained range.     

Particle swarm optimization technique for determining optimal machining parameters of different work piece materials in turning operation

Empirical models for machining time and surface roughness are described for exploring optimized machining parameters in turning operation. CNC turning machine was employed to conduct experiments on brass, aluminum, copper, and mild steel. Particle swarm optimization (PSO) has been used to find the optimal machining parameters for minimizing machining time subjected to desired surface roughness. Physical constraints for both experiment and theoretical approach are cutting speed, feed, depth of cut, and surface roughness. It is observed that the machining time and surface roughness based on PSO are nearly same as that of the values obtained based on confirmation experiments; hence, it is found that PSO is capable of selecting appropriate machining parameters for turning operation.     

Intelligent parameter identification of machining Ti64 alloy

In this paper, a methodology approach based on analysis of multidimensional Pareto front is proposed. A new optimization approach helps the user to set the optimal parameters of a machining process. Four neural networks are used to model desire output responses, and they are used as objective functions. Particle swarm optimization (PSO) is used to find the best parameters that improve process. As application of approached proposed, an analysis of a multidimensional Pareto front is made considering a minimization of time, temperature, vibration, and surface roughness in a milling process of Ti64 alloy. Physical parameters for experimental approach are tool diameter, number of cutting edge of the tool, cutting speed, feed, and depth of cut. Analysing the 2D and 3D multidimensional Pareto front is generated a user table of machining parameters.