Surface Roughness In End Milling Research Articles

Optimization of cutting force and surface roughness in end milling of AA-TiCp composite

Nowadays Aluminium alloy (AA) based composites are used in many fields such as automotive, aerospace, electronics, and marine industries. But for the utilization of these composites for different applications with different shapes, machining especially end milling is the essential process. Hence, this work describes the optimization of surface roughness and cutting forces during end milling of A356 aluminium alloy reinforced with titanium carbide particle (TiCp) metal matrix composites (MMCs). A356 alloy with 0, 3 and 6 vol% of TiCp (average size of 50 µm) composites were prepared using a stir casting process. The effects of milling process factors, namely cutting speed, feed, depth of cut, and volume % of TiCp, on average surface roughness (Ra) and cutting forces have been studied using a Box-Behnken design of the experiment technique. The end milling experiments were performed in a CNC vertical machining center without any cutting fluid using uncoated cemented carbide inserts. Using Design Ease software, the predictive model analysis and change in performance measurements for process variables were provided. The results showed that the depth of cut, followed by cutting speed, is the most important criterion and finally optimal levels of process variables were also identified to improve the machinability of the composites.

Investigation of delamination and surface roughness in end milling of glass fibre reinforced polymer composites using Fuzzy Model and Grey wolf Optimizer

Investigation of delamination and surface roughness in end milling of glass fibre reinforced polymer composites using Fuzzy Model and Grey wolf Optimizer

Influence of the Viscosity of Nanofluids on Surface Roughness in End Milling of Nickel Alloys with Minimum Quantity Lubrication

Minimum Quantity Lubrication (MQL) using vegetable oils is considered a sustainable lubrication method, particularly for machining difficult-to-machine materials like nickel and titanium alloys. Although a significant influence of nanofluid viscosity on lubrication has been observed in MQL machining, as evidenced by limited literature, the influence of viscosity on MQL machining of difficult-to-machine materials like nickel alloys is yet to be established. This research aimed to study the influence of viscosity on the MQL end milling machining performance of Inconel 718 alloy. Three representative nanofluids were prepared using metallic (Cu), ceramic (Al2O3), and non-metallic (CNT) nanoparticles and palm oil. It was found that the CNT had a significant influence on viscosity at the same concentration, resulting in the highest viscosity of 433.2cP at 30oC. When the machining performance was investigated under different lubricating conditions (dry machining, flood cooling, and MQL), the viscosity of the nanofluids was observed to have a substantial influence on the machining performance. The CNT nanofluid with the highest viscosity penetrated the machining zone producing the lowest surface roughness with improved lubrication by 65.4% and 30.18% when compared with dry machining and flood cooling, respectively. The surface topography study confirmed the superior lubrication performance of CNT nanofluid. Overall, MQL milling with 0.5wt% nanoparticle concentration demonstrated effective machining performance when compared with dry machining and flood cooling.

Feasibility Analysis of Machining Cobalt-Chromium Alloy (Stellite-6) Using TiN Coated Binary Inserts.

The objective of the study was to check the feasibility of machining Stellite 6, a cobalt–chromium superalloy, using TiN-coated carbide inserts in an end milling operation. The inserts were coated using the magnetron sputtering process. The sputtering power and gas flow rate were considered as the variables during the coating process. The performance of the coated binary carbide insert was checked during the end milling of Stellite 6 (~45 HRC) through an experiment with a Taguchi design. Experimental runs based on an orthogonal array were executed for each insert type to check the feasibility of machining this cobalt-based alloy. Adequate precision and the optimum parametric conditions were determined and are reported in this study. Analysis of variance (ANOVA) with a two-factor interaction model was also undertaken to forecast the key elements influencing surface roughness. Based on the ANOVA model, the depth of the cut, combined with the insert type, was the factor that had the greatest influence on surface roughness, followed by the cutting feed, whereas the cutting velocity had the least significance based on the tests. Moreover, the regression analysis demonstrated that the created model can be used to accurately forecast surface roughness in end milling of Stellite 6 with confidence intervals of 95%.

Comparative study on Mamdani-Type Fuzzy Inference Systems and Regression model for end milling process using AA 6082T6

The goal of this observes is to take a look at the effect of machining parameters on surface roughness in end milling. An incipient technique in modelling surface roughness that makes use of synthetic perspicacity implements is defined in this paper. This paper fixates on growing empirical models utilizing fuzzy logic and regression analysis. The values of surface roughness presaged with the aid of using those fashions are then in comparison. The effects confirmed that the proposed gadget can considerably boom the precision of the product profile whilst in comparison to the traditional approaches, like regression analysis. The effects designate that the regression modelling method may be effectively applied for the presage of surface roughness in dry machining.

Evolutionary Optimization of Machining Parameters Based on Surface Roughness in End Milling of Hot Rolled Steel.

Surface roughness measurements of machined parts are usually performed off-line after the completion of the machining operation. The objective of this work is to develop a surface roughness prediction method based on the processing of vibration signals during steel end milling operation performed on a vertical CNC machining center. The milling cuts were run under varying conditions (such as the spindle speed, feed rate, and depth of cut). This is a first step in the attempt to develop an online milling process monitoring system. The study presented here involves the analysis of vibration signals using statistical time parameters, frequency spectrum, and time-frequency wavelet decomposition. The analysis resulted in the extraction of 245 features that were used in the evolutionary optimization study to determine optimal cutting conditions based on the measured surface roughness of the milled specimen. Three feature selection methods were used to reduce the extracted feature set to smaller subsets, followed by binarization using two binarization methods. Three evolutionary algorithms—a genetic algorithm, particle swarm optimization and two variants, differential evolution and one of its variants, have been used to identify features that relate to the “best” surface finish measurements. These optimal features can then be related to cutting conditions (cutting speed, feed rate, and axial depth of cut). It is shown that the differential evolution and its variant performed better than the particle swarm optimization and its variants, and both differential evolution and particle swarm optimization perform better than the canonical genetic algorithm. Significant differences are found in the feature selection methods too, but no difference in performance was found between the two binarization methods.

Effects of tooling unbalance on surface roughness in end milling

The article discusses the issues of surface roughness when milling aluminum alloys. Effects of tooling unbalance are analyzed. The mathematical modeling method was used to calculate residual tooling equipment unbalance values. To assess model adequacy, experiments were conducted according to the fractional three-level four-factor plan. The unbalance effect was determined by regression analysis of the experimental data. Response surfaces were constructed. The experiments were conducted on a modern milling center. The results confirm that the mathematical model is adequate and the degree of surface roughness depends on residual tooling unbalance values.

Multi objective optimization of process parameters of Al 6061-SiCp metal matrix composite in end milling-Hybrid GRA-PCA approach

In this study optimization scheme for minimising machining force and surface roughness in end milling of Al 6061 reinforced with SiCp has been presented. The input parameters considered are cutting speed, feed, depth of cut and weight fraction. The measured responses are force and surface roughness. Experiments are designed and conducted based on Taguchi L25 orthogonal array. The parametric influences on responses are discussed through main effects plot and interaction effect plots. Further, integrated Grey Relational Analysis (GRA) and Principle Component Analysis (PCA) optimal scheme was performed. Results revealed that feed is the considerably influencing factor affecting the responses followed by depth of cut, weight fraction and speed. The optimum cutting parameters obtained are cutting speed 1000 rpm, feed rate 10 mm/rev, depth of cut 0.2 mm, and weight fraction 5%.

Performance evaluation method for cutting fluids using cutting force in micro-feed end milling

A simplified evaluation method for cutting fluids has not been established to accurately estimate cutting performance. In this study, the friction coefficients of cutting fluids were calculated from the cutting force in micro feed end milling, defined as μ-MFM for the cutting performance index. For cutting conditions in which the finished surface roughness exceeded a largely theoretical value and the feed marks were hardly recognized, there was a high correlation between μ-MFM and the finished surface roughness in end milling of AISI 1045 compared with results from conventional friction tests. Moreover, the finished surface roughness in turning AISI 1045 and AISI 440C also increased with μ-MFM.The finished surface roughness in turning AISI 1045 and AISI 440C showed nearly theoretical values regardless of the cutting fluids when the cutting speed exceeded a certain speed. However, even if the cutting speed was high, the cutting edge transcription error on the finished surface in low-feed turning conditions increased with decreasing μ-MFM. It was possible that the difference in cutting edge biting performance for work material in cutting fluids had a substantial influence on the cutting edge transcription error for small uncut chip thicknesses on the finished surface formation area, such as the low-feed rate in turning. Therefore, cutting edge biting performance was evaluated using the cutting force instability rate CFIR, which was the quantitative value of the drifting cutting force in one end milling cutting with the feed rate based on the maximum uncut chip thickness on the finished surface formation area of the low-feed turning. As a result, the cutting edge transcription error in turning of AISI 1045 and AISI 440C under low-feed cutting conditions increased with CFIR. Therefore, there was a correlation between the CFIR and the cutting edge transcription error in turning at low-feed conditions.

An experimental study on milling of natural fiber (jute)- reinforced polymer composites

The interest in materials having natural, environmentally friendly, renewable and low density/cost is increasing day by day due to sanctions imposed to reduce the emission rates, especially the Kyoto Protocol. In recent years, the use of environmentally friendly composites by using natural fibers such as flax, jute and sisal has increased in engineering applications. Milling operation has frequently been an important method of machining which can achieve the desired dimensions and tolerances for the plate-shaped parts. In this study, the effects of cutting parameters such as cutting speed and feed rate on cutting force, delamination factor and surface roughness in end milling of jute fiber-reinforced polymer composite plates with different orientation angle (0°/90°, 30°/−60° and ±45°) were examined by using the cemented carbide (WC) end mills (two, three and four number of flutes). Cutting force, deformation factor and surface roughness were found to be influenced by the feed rate and cutting speeds. In addition, increasing the number of the flutes of the cutting tools reduced the cutting force, delamination factor and surface roughness.

Modeling and optimization of surface roughness in end milling of graphene/epoxy nanocomposite

Abstract Epoxy resin finds its application in various sectors like marine, automotive, aerospace, construction and structures due to better thermal stability and mechanical properties. Addition of graphene into epoxy improves the chemical and physical properties at small loading condition which depends on the dispersion of graphene into epoxy. In the present investigation, end milling operation is executed on graphene/epoxy nanocomposite by varying the parameters like spindle speed (500–1500 rpm), feed rate (50–100 mm/min) and weight % of graphene (0.0–0.2) to investigate and optimize the surface roughness by using statistical analysis. Results show that weight % of graphene is the dominating factor followed by spindle speed and feed rate. Surface roughness decreases with increase in spindle speed and weight % of graphene while increases with increase in feed rate.

Optimization of Machining Parameters for Surface Roughness in End Milling of Magnesium AM60 Alloy

Objective: The present work is aimed to find an optimum combination of cutting parameters to achieve low surface roughness in end milling of magnesium AM60 with TiN coated carbide tool under dry conditions. Methods: Design of Experiments (DOE) with Response Surface Methodology (RSM) using Box-Behnken design and the regression equations are used to find the optimal combinations of cutting parameters to achieve low surface roughness. The developed RSM model was experienced through Analysis of Variance (ANOVA). An ANOVA analysis was performed to indicate the control of three machining parameters on the surface roughness. Findings: The cutting parameters assessed were spindle speed, depth of cut and feed rate have the greatest effect on the success of the milling operation. Confirmation experiments with the optimum combinations of cutting parameters were carried out in order to explain the efficiency of the response surface design concepts. From ANOVA results, the feed rate was found to be most significant factor affects surface roughness of milled surface. Feed rate, depth of cut and spindle speed affects the surface roughness by 76.18%, 2.94% and 1.99% respectively. It can be fulfilled that RSM method is effective and efficient method to optimize milling parameters for low surface roughness. Applications: Magnesium (Mg) is now emerging as a popular metal for replacing Aluminum (Al) and finding applications in automobile and aerospace industries where fine finishing of the machined component is ultimate requirements to achieve a product quality.

Optimization of MQL flow rate for minimum cutting force and surface roughness in end milling of hardened steel (HRC 40)

The present work focuses on the performance modeling of hard milling to attain an optimum parameter setting for the minimum cutting force and surface roughness. Furthermore, it was attempted to compute the minimum quantity lubricant flow rate precisely, besides the cutting speed and table feed rate, by adopting Grey-based Taguchi method and composite desirability function. The experimental data was collected by end milling of hardened AISI 4140 steel using carbide cutter under dry and minimum quantity lubrication conditions according to Taguchi L 16 orthogonal array. The predictive model of the responses was formulated by using response surface methodology. The analysis of variance revealed that the table feed has the maximum influence on cutting force, and the flow rate of lubricant has the highest effect on surface roughness. The parameter setting at lower table feed, higher cutting speed, and 150-ml/h lubricant flow yield the minimum value of the responses. Finally, the results of confirmation test verified the adequacy and supremacy of the optimization models; however, Grey-based Taguchi method induced a better optimization.

Optimization of cutting conditions for minimum residual stress, cutting force and surface roughness in end milling of S50C medium carbon steel

Residual stresses are usually imposed on a machined component due to thermal and mechanical loading. Tensile residual stresses are detrimental as it could shorten the fatigue life of the component; meanwhile, compressive residual stresses are beneficial as it could prolong the fatigue life. Thermal and mechanical loading significantly affect the behavior of residual stress. Therefore, this research focused on the effects of lubricant and milling mode during end milling of S50C medium carbon steel. Numerical factors, namely, spindle speed, feed rate and depth of cut and categorical factors, namely, lubrication and milling mode is optimized using D-optimal experimentation. Mathematical model is developed for the prediction of residual stress, cutting force and surface roughness based on response surface methodology (RSM). Results show that minimum residual stress and cutting force can be achieved during up milling, by adopting the MQL-SiO2 nanolubrication system. Meanwhile, during down milling minimum residual stress and cutting force can be achieved with flood cutting. Moreover, minimum surface roughness can be attained during flood cutting in both up and down milling. The response surface plots indicate that the effect of spindle speed and feed rate is less significant at low depth of cut but this effect significantly increases the residual stress, cutting force and surface roughness as the depth of cut increases.

Experimental and modeling investigation of surface roughness in end-milling of polyamide 6/multi-walled carbon nano-tube composite

Nowadays, Manufacturers tend to use polymer nano-composites as raw material because of their high thermo-mechanical properties and strength to weight ratio. Due to production methods of nano-composites which result in simple cross-sections and rising demands for private products, the need for machining polymers has increased. In this study, the surface roughness of polyamide 6/multi-walled carbon nano-tube composite has been investigated after end-milling. The nano-composite samples were produced with 3.5 wt% of multi-walled carbon nano-tube by friction stir process. In order to ensure the quality of fabricated nano-composites, X-ray diffraction and scanning electron microscopy were used to evaluate it. End-milling was conducted at different levels of spindle speed and feed per tooth on polyamide 6 (with and without multi-walled carbon nano-tube) to study the resulted surface roughness. Then, analysis of variance was conducted to evaluate the parameters’ influence on surface roughness. Additionally, the optimal cutting parameters were determined by coupling artificial neural network and harmony search algorithm. Results reveal that surface quality increases with decrease of feed per tooth, and increase of spindle speed and carbon nano-tubes improve the surface quality.

Estimate of cutting forces and surface roughness in end milling of glass fiber reinforced plastic composites using fuzzy logic system

Estimate of cutting forces and surface roughness in end milling of glass fiber reinforced plastic composites using fuzzy logic system

English

This paper presents a real time simulation for virtual end milling process. Alyuda NeuroIntelligence was used to design and implement an artificial neural network. Artificial neural networks (ANN’s) is an approach to evolve an efficient model for estimation of surface roughness, based on a set of input cutting conditions. Neural network algorithms are developed for use as a direct modeling method, to predict surface roughness for end milling operations. Prediction of surface roughness in end milling is often needed in order to establish automation or optimization of the machining processes. Supervised neural networks are used to successfully estimate the cutting forces developed during end milling processes. The training of the networks is preformed with experimental machining data. The neural network is used to predict surface roughness of the virtual milling machine to analyze and preprocess pre measured test data. The simulation for the geometrical modeling of end milling process and analytical modeling of machining parameters was developed based on real data from experiments carried out using Prolight2000 (CNC) milling machine. This application can simulate the virtual end milling process and surface roughness Ra (µm) prediction graphs against cutting conditions simultaneously. The user can also analyze parameters that influenced the machining process such as cutting speed, feed rate of worktable. Key words: Surface roughness, virtual reality, simulation, surface roughness, virtual end milling process, neural network.

Optimization of Surface Roughness in End Milling of Titanium Alloy Ti-6Al-4V under the Influence of Magnetic Field from Permanents Magnets

This paper presents the effect of cutting parameters on surface roughness in end milling of Titanium alloy Ti-6Al-4V under the influence of magnetic field from permanent magnets. Response Surface Methodology (RSM) with a small central composite design was used in developing the relationship between cutting speed, feed, and depth of cut, with surface roughness. In this experiment, three factors and five levels of central composite with 0.16817 alpha value was used as an approach to predict the surface roughness, in end milling of titanium alloy, with reasonable accuracy. The Design-Expert 6.0 software was applied to develop the surface roughness equation for the predictive model. The adequacy of the surface roughness model was validated to 95% by using ANOVA analysis. Finally, desirability function approach was used to determine the optimum possible surface roughness given the capabilities of the end machine.

Influence of Magnetic Field on Reduction Chatter and of Surface Roughness in End Milling of Titanium Alloy - Ti-6Al-4V

One of the most challenging issues in machining process is understanding the chatter phenomenon. Chatter mechanics is still not fully understood. It is inconsistent in character, making it difficult to analyze and predict. This research work investigates the influence of permanent magnets on chatter suppression in end milling of Titanium alloy (Ti-6Al-4V) using uncoated WC-Co insert. The experiments were designed based on the Response Surface Methodology (RSM) approach using DESIGN EXPERT (DOE) software. The experiments were performed under two different conditions: under normal condition and under the application of magnetic fields from two permanent magnets located in opposite direction. Ti-6Al-4V was used as the work material. The resultant average surface roughness was found to be reduced by a maximum of 50% due to magnet application. Scanning Electron Microscope (SEM) was used to analyze the chip morphology. The microphotographs showed the evidence of more stable chip formation under the influence of magnetic fields.

Surface Roughness Optimization in End Milling of Stainless Steel AISI 304 with Uncoated WC-Co Insert Under Magnetic Field

Chatter phenomenon is a major issue as it greatly affects the topography of machined parts. Due to the inconsistent character of chatter, it is extremely difficult to predict resultant surface roughness in a machining process, such as end milling. Also, recent studies have shown that chatter can be suitably damped using magnetic fields. This paper, thus, focuses on a novel approach of minimizing surface roughness in end milling of Mild (Low Carbon) Steel using uncoated WC-Co inserts under magnetic field from permanent magnets. In this experiment, Response Surface Methodology (RSM) approach using DESIGN EXPERT 6.0 (DOE) software was used to design the experiments. The experiments were performed under two different cutting conditions. The first one was cutting under normal conditions, while the other was cutting under the application of magnetic fields from two permanent magnets positioned on opposite sides of the cutter. Surface roughness was measured using Mitutoyo SURFTEST SV-500 profilometer. The subsequent analysis showed that surface roughness was significantly reduced (by as much as 67.21%) when machining was done under the influence of magnetic field. The experimental results were then used to develop a second order empirical mathematical model equation for surface roughness and validated to 95% confidence level by using ANOVA. Finally, desirability function approach was used to optimize the surface roughness within the limiting values attainable in end milling.