Effect of cooled compressed air on the machining force and surface roughness generated by face milling of CW50/PEEK

This paper explores use of Teaching Learning Based Optimization (TLBO), ‘JAYA’ (Sanskrit word means Victory) and Genetic Algorithm (GA) for the combined minimization of roughness of machined surface and forces generated in cutting in turning of Ti-6Al-4V. Experimentation was carried out with Response Surface Methodology (RSM) and the Central Composite Design (CCD). Speed of cutting (m/min), feed rate (mm/min) and depth of cut (mm) were the design variables for optimization. Two responses (roughness of machined surface and force of cutting) were independently minimized. RSM was useful in finding empirical relations and the effect of each parameter and their interactions on the responses considered. Analysis of variance (ANOVA) was used to find out the effective and non-effective factors and correctness of the models. Later on, a multi-objective optimization function was developed for minimizing both – roughness in machined surface and force generated in cutting using weights method and the correctness of weights were confirmed by Analytical Hierarchy Process (AHP). After formulating the combined objective function, TLBO, ‘JAYA’ and GA methods were used for further parameter optimization of the turning process. Performance of TLBO and ‘JAYA’ algorithm was compared with that of Genetic Algorithm (GA). It is found that TLBO and ‘JAYA’ performed better than GA in the combined minimization of roughness and forces in while turning Ti-6Al-4V. It is also found from the results that higher cutting speed (171.4 m/min) and lower feed rate (55.6 mm/min) can produce better surface roughness and minimum cutting forces in machining of Ti-6Al-4V. Highlights This paper Presents, implementation of Advanced Algorithms for multi objective optimization of cutting force and surface roughness in machining of difficult to cut Ti-6Al-4V. Two newly developed advanced algorithms such as JAYA and Teaching learning based optimization (TLBO) ‘without algorithm control’ parameters are used for machining response optimization. Objective functions for surface roughness and cutting forces are developed after actual face milling operation performed in sequential manner with response surface methodology. Developed models are verified with statistical test (ANOVA, residual plots) as well as confirmation experiments. It is concluded from the results that machining parameters can be optimize using advanced algorithms. This work can help machinists to select cutting parameters based on desired machining response.

The present study reports the effect of different process parameters on machining forces, surface roughness, dimensional deviation and material removal rate during hard turning of EN31, SAE8620 and EN9 tool steels. Feed rate followed by hardness, cutting speed and nose radius-depth of cut significantly affected machining forces whereas feed rate had the largest effect on surface roughness. The four responses were subsequently optimized for both rough and finish machining using genetic algorithm to determine the optimum combination of input parameters. Machined surfaces were subsequently analyzed using XRD followed by analysis of grain size and crystallite size of the machined samples and SEM analysis. Higher chromium content was observed at the machined surface as manganese dissolves in cementite and may replace iron atoms in the cementite lattice after machining. High heat is generated when machining at higher cutting speeds causing severe strain. The depth of the white layer decreases with increasing tool nose radius and increases at larger feeds because of greater heat generation. The SEM observations showed a smooth pattern with very low undulations with almost no crack damage.

Measurement of machining forces and surface roughness in turning of AISI 304 steel using alumina-MWCNT hybrid nanoparticles enriched cutting fluid

This paper presents an approach for the determination of the optimal machining parameters (spindle speed, feed rate and depth of cut) in ball-end milling process of carbon fiber-reinforced plastics (CFRP). In this case, considering the machining force and surface roughness as the constrains, maximum material removal rate is created through coupling response surface method (RSM) and particle swarm optimization (PSO). Many experiments on CFRP were conducted to obtain machining force and surface Roughness values, and then analysis of variance was performed. In order to predict constrains values, RSM was selected to create mathematical relation between machining parameters and constrains. The material removal rate constituted the main function for PSO and machining force, and also surface roughness were applied to the input function of PSO. In this study, the function was optimized by PSO code, and the optimum parameters were obtained. Finally, the results of PSO were tested experimentally and compare...

This study explores how ageing temperature and the volume percentage of Al2O3+B4C nanoparticles influence the machinability and hardness of stir-cast Al-7075 Metal Matrix Composite (MMC). Using liquid metallurgy techniques, hybrid materials were created by reinforcing Al7075 metal matrix with varying weight percentages of nanosized B4C (1.5%, 3%, and 4.5%) and Al2O3 (1%, 1.5%, and 2%). After fabrication, the samples were subjected to five-hour ageing process at temperatures of 100, 120, and 140 degrees Celsius, followed by cooling to ambient temperature (27 degrees Celsius). Hybrid nano composites that had been heat treated were tested for wear, tensile strength, and hardness. Results shows that, the addition of nanoparticles and heat treatment considerably improves the tensile strength, hardness, and wear resistance of hybrid composites by 3%, 17%, and 10%, respectively, for samples reinforced with 4.5% B4C + 2% Al2O3. SEM analysis was used to investigate the type of wear and the tensile fracture mode of nano composite samples by analyzing the wornout surface and the surface where tensile fracture occurred. Machinability was assessed using L27 orthogonal array tests, focusing on three key process parameters: feed rate (0.1 mm/min), depth of cut (0.2 mm/min), and spindle speed (1000 rpm). Outcomes show that, increasing the wt. % of nano-Al2O3/B4C leads to higher machining force and surface roughness (Ra) of MMCs. Conversely, higher ageing temperatures result in decreased machining force and surface roughness. Optimal surface roughness and machining force were achieved with 1% Al2O3 + 1.5% B4C and an ageing temperature of 140°C. These findings offer valuable insights into the ease of machining of composite metal alloys, emphasizing the importance of parameter selection and optimization for desired machining outcomes.

This paper presents the machining characteristics of AA6061/(0–9 wt%) ZrB2+ ZrC aluminium metal matrix composites using polycrystalline Diamond(PCD). In the present investigation, AMCs were fabricated by in situ reactions between K2ZrF6, KBF4 and SiC particles. The electric stir casting furnace was used to fabricate the AMCs under a controlled environment. X-ray diffraction patterns (XRD) and field emission scanning electron microscope (FESEM) were used to ascertain the formation of ZrB2 and ZrC particles in the AMCs. FESEM micrographs confirmed the uniform distribution of ZrB2 and ZrC particulates along with good interfacial bonding with matrix aluminium alloy. The effect of varying wt% of ZrB2+ ZrC along with cutting speed, feed and depth of cut on machining forces and surface roughness were analysed. FESEM was used to study the morphology of cutting tool, machined surface and chip formation. It was observed that cutting forces reduce with increase in cutting speed due to decrease in built-up edge (BUE) formation and deprivation of dislocation density. The tool wear increased with increase in cutting speed, depth of cut and ZrB2 + ZrC content due to the increase in abrasive action of ceramic particles and reduction of stable BUE.

Turning characteristics of in situ formed TiB2 ceramic particulate reinforced AA7075 aluminum matrix composites using polycrystalline diamond cutting tool

Titanium alloys are widely used in various industries such as aerospace, petrochemical, marine, and biomedical due to their high corrosion resistance, high strength, good heat resistance, and lightweight. However, the low thermal conductivity of titanium alloys makes their machinability poor compared to steel alloys. Therefore, it is of great importance to evaluate the machinability of titanium alloys. This experimental study presents the effects of machining parameters on the machinability factors such as surface roughness, power consumption, cutting sound, and machining force in dry turning of Ti-6Al-4V titanium alloy using CBN inserts. Sound emission is a substantial factor for the operator’s safety during the turning process. High cutting sound is generated due to removing a large amount of material from the workpiece using different cutting tools. Since there are not many studies related to the cutting sound in machining processes, the attempt was made to investigate the effect of cutting sound on the machinability factors. The variance analysis results presented the significant effect of cutting speed and cutting depth on power consumption. However, according to the normal plot of the standardized effect, all machining parameters have a high impact on the cutting sound. The highest level of machining parameters, especially cutting depth results in higher cutting sound due to the creation of vibration in the lathe and cutting tool. Besides, the feed rate was found to be the most influential parameter on the surface roughness with a 58.13% contribution. The machining force was affected by both cutting depth and feed rate. The findings revealed that increasing the cutting sound increases the machining force and surface roughness, while the power consumption drops. Based on the desirability function, 0.04 (mm/rev) feed rate, 0.05 (mm) cutting depth, and 60 (m/min) cutting speed were the optimum machining parameters for turning of Ti-6Al-4V titanium alloy.

The use of cutting fluids in machining processes plays an important role in minimizing cutting temperature, cutting forces, and machining cost In this paper, a study has been performed to model and optimize the process parameters involved in the turning operation of alloy steel AISI-304L (4306) with SiO2 nanofluids using the minimum quantity lubrication (MQL) method. The effects of spindle speed, feed rate, and flow rate in three levels have been investigated on the surface roughness and the machining forces using the response surface method (RSM). The results show the feed rate has the most effect on output responses. The minimum level of the feed rate and the maximum level of the flow rate and the spindle speed results in the optimum value for the surface roughness and machining forces. Also, a comparison between lubricating with and without nanoparticles show addition of nanoparticles improves the cutting forces and surface roughness due to the increase of the thermal conductivity coefficient of cutting fluids. The use of SiO2 nanoparticles in cutting fluid decreases the surface roughness by 48.5% and the cutting force by 12.3% compared to pure cutting fluid in the same condition.

Knowing that the machined surface roughness of Al/SiCp composites is linked to its performance, this paper presents an elaborative experimentation using Taguchi methods on four composites to analyze the effects of size (15 μm and 65 μm) and volume fraction (20% and 30%) of reinforcements in the composites on machining forces and machined surface roughness. The independent variables in the experiment were: tool nose radius, cutting edge geometry, feed rate, cutting speed and depth of cut. The results show that, of the three machining force components, only radial force shows significant dependence on composition of composites. The machined surface roughness was found to be more sensitive to a change in size than to volume fraction of reinforcement in composites. However, all the independent variables, except the cutting speed, cause a statistically significant effect on the machined surface roughness for all composites. An analytical model giving a correlation between the machined surface roughness and the ratio of cutting forces was formulated based on the geometry of work-tool contact. The predicted surface roughness using the model was found to agree well with the experimental values, especially when the tool nose radius is less than the depth of cut.

Investigation on surface defect machining of AISI 4340 steel

The super alloy exhibits great strength and fatigue behaviour when nickel (Ni) is present in major quantities. Moreover, it possesses good corrosive resistant behaviour at high temperatures. However, these alloys are very difficult to machine under normal machining conditions due to their great strength and low heat dissolution. In this work, machining was performed on Hastelloy C276 under various machining conditions (speed, feed and depth) and environments (dry and cryogenic). Liquid nitrogen was used as a coolant in the machining region. The machinability of Hastelloy C276 was investigated with machining forces, temperature, surface roughness and hardness under different cutting conditions. Turning experiments that resulted from passing LN2 drastically reduced temperature by up to 40%. Machining forces were minimal under cryogenic machining due to its effective lubrication property. Surface finish of the machined area improved by about 26% under cryogenic conditions. Both dry and cryogenic machining improved the hardness of the work material. The high cooling efficiency of LN2 improved hardness of the machined surface was about 8-15%. Chip width and side-flow of chip material were reduced under cryogenic cooling. Moreover, adhesion and abrasion wear were observed minimally in cryogenic machining compared to dry machining. But no significant difference was observed in notch wear for both types of machining. Machinability of Hastelloy C276 significantly improved when LN2 used as a cutting fluid.

The turning of hardened steel is the current problem with required minimum machining force and surface roughness. In this investigation on normal defect hard turning AISI 4340 steel has been used, because of it is medium carbon (0.4% C) high strength martensitic steel. This material is more important in the critical applications like aerospace and automotive transmissions. To machine a ferrous alloy which is over 45 HRC, the hard turning process has been used to remove the material which has single-point cutting tool at high speeds. For processing hard steel, it involves a traditional approach, and those operations were consumed significant amount of cost and time. When it is compared with hard turning (HT), it reduces the annealing and grinding requirements. Still researchers are finding the best combination of inserts in HT where tool life plays significant role due to its cost and obtaining better surface finish. To discover the best ideal parameters for normal hard turning process by considering the cutting speed, feed rate, and depth of cut as input parameters using L9 orthogonal array in Taguchi method and Grey relational analysis for ANOVA. Normal HT was performed on hardened AISI 4340 steel and obtained the results. The optimal setting for this normal HT with consideration of machining force & surface roughness to the level of factors applying Grey relation grade is speed—900 rpm, feed—0.05 mm/rev, and depth of cut—0.5 mm.

A five-axis machining center (5MC) is capable of synchronous control, which makes it a feasible tool for quickly and accurately machining complicated three-dimensional surfaces, such as propellers and hypoid gears. Recently, the necessity of improving both the machined shape accuracy and the machined surface roughness of free-form surfaces is growing. Therefore, in our previous study, we aimed to maintain the feed speed vector at the end-milling point by controlling two linear axes and a rotary axis of the 5MC to improve the quality of the machined surface. Additionally, we developed a method for maintaining the feed speed vector at the end-milling point by controlling the three axes of the 5MC to reduce the shape error of the machined workpieces (referred to as the shape error herein), considering the approach path of the tool determined via calculation. However, a high machining force at the start of the workpiece cutting was observed and the factor contributing to this phenomenon was not determined, although this phenomenon leads to a shape error to a certain degree according to the machining condition. In this study, the main objective is to suggest a method to reduce the machining force at the start of the workpiece cutting and shape error. Hence, we develop a theoretical method to estimate the machining force by using an instantaneous cutting force model, which considers the synchronized motion of two linear axes and a rotary axis of the 5MC. Subsequently, we determine the most suitable approach path of the tool considering the prediction of the machining force. The results of this study indicate that the machining force can be estimated by applying an instantaneous cutting force using the feed per tooth and machining angle, and that both a high machining force at the start of the workpiece cutting and shape error reduction can be realized by using the proposed approach path of the tool.

Effect of cutting edge radius on cutting force and surface roughness in machining of Ti-6Al-4V