High-speed Steel End Mill Research Articles

The Evaluation of PVD Coated High Speed Steel End Mill

To enhance the cutting performance of high speed steel(HSS) end mill, single and multilayer coating is applied on the substrated of the HSS end mill. Coating material reduces cutting force and enhances resistance against abrasive wear. This paper presents the physical vapour deposition(PVD) coating technology and the machinability of PVD coated HSS end mill. The performance of coated HSS end mills are fifteen times better than uncoated HSS end mill on proposed cutting conditions. The TiAlN monolayer coated end mills(futura nano coating) are better than those of multilayer coated end mills(futura coating) on machined surface and tool wear.

Measurement and optimization of multi-attribute characteristics in milling epoxy granite composites using rsm and combined ahp-topsis

Epoxy granite composites with its wide range of applications in machine tool industries are manufactured by molding process and require post cast machining operations to meet the desired dimensional accuracy for assembly of machine tool structures. In this research work, milling of epoxy granite composites are carried out based on the experimental design from Response Surface Methodology (RSM) techniques and further the optimal solutions are determined by a novel hybrid algorithm AHP-TOPSIS. Central Composite Design (CCD) model is applied with three factors-three levels and the measured output responses are thrust force, tangential force and surface roughness. Experimental combinations of 20 different trials are performed using high speed steel end mill cutter of diameter 10 mm with three levels of input parameters: speed; fibre content and feed rate at a uniform depth of cut. The relative importance matrix formulated proved to be highly consistent with its consistency ratio to a maximum of 0.000641 which lies below the higher range of 0.1. Consistency ratio of 0.000641 reveals that the optimal solutions determined will be highly reliable and the decision making is much more judicious. Optimal solution determined from hybrid AHP-TOPSIS methods are: speed 1800 rpm; feed rate 0.03 m min−1 and 0% percent fibre content. Functional relationships among parameters and responses established by RSM are consistent upto 95% and its significance is tested by analysis of variance. Comparison among predicted and experimental values of three measured responses convey that the percentage variations are minimum with up to 2.03% for surface roughness, 2.50% for thrust force and 2.71% for tangential force components. This research work provides a systematic procedure and clear framework for determination of optimal machining conditions by hybrid methodology on the basis of technique for order preference by similarity to ideal solution (TOPSIS) combined with analytical hierarchy procedure (AHP) for attribute weights and further analyzes the influence of machining parameters over measured responses.

Выбор режимов резания базальтопластиков

The analysis of the prospects for the use of basalt plastics in various industries has been carried out. The types of cutting tools used for machining PCM based on basalt fiber were studied. The results of mechanical processing of samples of basalt plastics by an end mill made of high-speed steel R6AM5, using a machine with numerical control in various modes (with varying tool speed and cutting speed) without and with the use of a lubricating coolant (coolant) are presented. The results of assessing the quality of cut surfaces based on their microanalysis performed using an optical microscope are presented. The optimal modes of cutting basalt plastic up to 3 mm thick with a high-speed steel end mill were determined. The analysis results of the absorption of cutting fluid by basalt plastic after mechanical processing are given. It was established that the best quality of the cut surface was achieved with the use of a coolant and reduced processing modes or without the use of a coolant and increased processing modes.

RESEARCH AND REVIEW OF CLAY AND GLASS FIBER REINFORCED POLYESTER NANOCOMPOSITE MATERIALS USING OPTIMIZATION TECHNIQUES

Drilling process plays a vital role in the development of polymer clay nanocomposites. The effects of various parameters, such as tool, feed rate and speed to generate the impact on tangential force, thrust force and delamination factor acting on the material in production are studied during drilling operations in the aerospace and automotive industries. The input variable settings to adjust the speed and feed rate to show the outcomes of tangential force, thrust force and delamination factor of the material according to the consumption of the parts in the next stage of manufacturing are calculated by efficient feed rate optimization. A series of tests are performed by changing tools such as High speed steel (HSS), end mill (HEM), High speed steel (HSS), twist drill (HTD) and carbide twist drill (CTD) on various materials to evaluate the influence on the feed rate and speed. Based on the experimental analysis, mathematical modeling is implemented to study the properties of glass fiber reinforced polyester nanocomposite (GFRPNC; 3[Formula: see text]wt.%). Using the desirability approach, the optimum operating conditions of the selected process variables are considered to minimize delamination. A full factorial experiment design is adopted using the two fundamental concepts of replication and randomization of experimental design to research the relationship between the variables. Based on the analysis of variance (ANOVA), the process model is formulated using the [Formula: see text] statistical kit. It is inferred that the delamination factor is minimal for all tools (CTD, HTD and HEM) at 0.1[Formula: see text]mm/rev feed rate and speed at 852[Formula: see text]rpm.

Prediction of cutting force based on machining parameters on AL7075‐T6 aluminum alloy by response surface methodology in end milling

AbstractCutting forces modeling is the basic to understand the cutting process, which should be kept in minimum to reduce tool deflection, vibration, tool wear and optimize the process parameters in order to obtain a high quality product within minimum machining time. In this paper a statistical model has been developed to predict cutting force in terms of geometrical parameters such as rake angle, nose radius of cutting tool and machining parameters such as cutting speed, cutting feed and axial depth of cut. Response surface methodology experimental design was employed for conducting experiments. The work piece material is Aluminum (Al 7075‐T6) and the tool used is high speed steel end mill cutter with different tool geometry. The cutting forces are measured using three axis milling tool dynamometer. The second order mathematical model in terms of machining parameters is developed for predicting cutting forces. The adequacy of the model is checked by employing ANOVA. The direct effect of the process parameter with cutting forces are analyzed, which helps to select process parameter in order to keep cutting forces minimum, which ensures the stability of end milling process. The study observed that feed rate has the highest statistical and physical influence on cutting force.

Optimization techniques to optimize the milling operation with different parameters for composite of AA 3105

Manufacturing processes become a direction to get our vision into this modern environment. Despite its expense, it is broadly used in various manufacturing. Milling is one of the most robust radical machine tools with thorough material removal. Machining parameters can perform much more complex, indexing operations and much more for gear manufacturing. The whole effort gives you a snapshot of the result of development constraints taking place on machining. As a grid material, AA 3105 was used. The material for the workpiece for the study to advance the level of expulsive materials is carbohydrated eggshell powder (RHA). The orthogonal array is designed for three stages, three-factor designs and tests on the efficiency of machining are performed using Taguchi Model, ANOVA, and regression analysis. Milling data is used to generate a statistical equation which eventually determined useful in analyzing variance. Answer surface technique, an experimental design was used for experiments. Material for the workpiece was AA 3105 as matrix material, Carbonized eggshell powder, and rice husk ash (RHA), and the tool was a high-speed steel end mill cutter. The process parameter's direct and interaction effect with the speed of the spindle, depth of cut, and feed rate was studied, which helped select process parameters to material removal rate, indicating end-friction activity immobility. The predictive models in this analysis are assumed to be similar to those observed experimentally with a confidence interval of 95 percent.

EXPERIMENTAL INVESTIGATION OF EFFECT OF CRYO-TREATMENT ON MICROMILLING OF INCONEL 718

In this paper, the effect of cutting parameters during micromilling on surface finish and material removal rate is presented. Inconel 718 alloy and high-speed steel micro end mill are used as work material and cutting tool, respectively. High-speed steel end mill of 1 mm diameter is subjected to cryogenic treatment. Machining studies are performed on Inconel alloy using untreated and cryogenic treated cutters. The milling tests are conducted at three different values of feed rate, cutting speed and depth of cut. Also, tool wear, microstructure and microhardness of different treated and untreated end mill are investigated and discussed in detail. The results showed that cryogenic treatment significantly improved the tool wear. The surface finish produced on machining the work-piece is better with the cryogenic treated tools than when compared with the untreated tools. The material removal rate is better with the cryogenic treated tools than when compared with the untreated tools. Improvement in tool life was up to 53.16% for Inconel 718 material when machined with cryogenically treated micro end mill.

Optimization of Machining and Geometrical Parameters to Reduce Vibration While Milling Metal Matrix Composite

During end milling of metal matrix composite (MMC), it is subjected to varying impact load and cutting force. This action induces vibration during machining, which results in poor surface finish and increased tool wear. In this study, an attempt has been made to determine the optimum level of geometrical parameters such as helix angle, nose radius and rake angle and machining parameters such as cutting speed, feed rate and depth of cut to obtain minimum vibration amplitude during end milling operation using particle swarm optimization (PSO). Machining was carried out on aluminum (Al 356)/silicon carbide (SiC) particulate MMC using high-speed steel end mill cutter. The experimental design adapted for conducting the experiments was L27 orthogonal design array. The experiments were conducted using vertical machining, and the vibration amplitudes were measured at two positions (spindle and workpiece holder) using a twin-channel piezoelectric accelerometer. The empirical regression model was developed by relating the input parameters and the output vibration amplitudes, and optimization of process parameters to obtain minimum acceleration amplitude was done using PSO. Scanning electron microscopic images of the milled surfaces were examined, and it indicated the presence of cracks and holes due to pulling out of SiC particles.

Multi-attribute optimization of end milling epoxy granite composites using TOPSIS

ABSTRACTEpoxy granite composites are identified and recognized as better materials for machine tool applications due to inherent damping properties. However, end milling of these composites has not been explored much. Milling of epoxy granite composites presents a number of problems, namely, cutting forces and surface roughness appear during machining. This research work focuses on end milling of epoxy granite composite specimens using high-speed steel end mill cutter by varying the cutting conditions such as spindle speed and feed with a uniform depth of cut and selection of optimal machining parameters. The experimental runs of 27 different trials were carried out and three different attributes such as thrust force, tangential force, and surface roughness were analyzed. This research work presents a sequential procedure for machining parameters selection. Selection of optimal machining parameters is done on the basis of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method.

Machining deformation prediction of thin-walled workpieces in five-axis flank milling

Five-axis flank milling is the main processing method for manufacturing aerospace thin-walled workpieces. The sidewalls of such workpieces are prone to deformation during the milling process because of their low rigidity, which severely affects machining accuracy. Based on this fact, this paper proposes a novel model to predict surface form errors caused by milling force. This study describes an efficient cutter-workpiece engagement (CWE) extraction method for the computation of cutting forces and identification of machining geometry changes during the five-axis flank milling process. The paper then focuses on the establishment of an iterative scheme for machining deformation calculation. This procedure is carried out by considering the coupling effect between cutting forces and tool/workpiece (T/W) deformations, in which the radial cutting depth and the tool axis vector are taken as feedback variables for the iterative computation. Subsequently, the reliability of the proposed model is validated by a 3-mm S-shaped test piece machining with a high-speed steel end mill. The work shows that the surface deformation error comes from the joint effect of the instantaneous milling force and the local stiffness of the workpiece. The relative error rate of the simulation model is 18.8%, which shows good prediction accuracy compared with existing methods.

Optimisation of milling parameters with multi-performance characteristic on Al/SiC metal matrix composite using grey-fuzzy logic algorithm

PurposeThe purpose of this paper is to determine the optimum level of geometrical parameters such as helix angle, nose radius, rake angle and machining parameters such as cutting speed, feed rate and depth of cut to arrive minimum surface roughness and tool wear during end milling of Al 356/SiC metal matrix composites (MMCs) using high speed steel end mill cutter.Design/methodology/approachL27 Taguchi orthogonal design with six factors and three levels is employed for conducting experiments. Analysis of variance (ANOVA) is carried out using Minitab16 software to find the influence of each input parameter on output performance measure. Grey-fuzzy logic multi optimisation algorithm is used to find the optimum level of the input parameters for minimum surface roughness and tool wear simultaneously.FindingsIt is found that optimal combination of helix angle 40°, nose radius 0.8 mm, rake angle 12°, cutting speed 90 m/min, feed rate 0.04 mm/rev and depth of cut 1.5 mm have generated minimum surface roughness of 0.4063 µm and tool wear of 0.0375 mm. From ANOVA analysis, it is found that cutting speed influence is more on output performance followed by helix angle and rake angle compared with other machining and geometrical parameters.Originality/valueThe influence of tool geometry during end milling of MMC using Grey-fuzzy logic algorithm has not been explored previously.

Optimization of Processes Parameters on Temperature Rise in CNC End Milling of Al 7068 using Hybrid Techniques

Measuring temperature and the estimation of heat distribution in metal cutting is important because, it’s controlling contribution on tool deflection, tool life, cutting force and vibration as well as, the quality of the machined part. In this paper a statistical model has been evolved to estimate the temperature rise in terms of design parameters such as helix angle, radial rake angle of cutting tool and machining parameters such as cutting speed, feed rate and axial depth of cut under dry condition. Response surface methodology, experimental design was employed for execution of experiments. The work piece material was Aluminum Al 7068and the tool was high speed steel end mill cutter with different tool geometry. The temperature rise was evaluated by using a pyrometer. The second order mathematical model in terms of machining parameters was evolved for estimatinga temperature rise. The competence of the model was computed by employing ANOVA. The direct and interaction effect of the process parameter with temperature rise were analyzed, which aided to select process parameter in order to keep temperature rise minimum, which indicates the immobility of end milling process. The predictive models in this study are believed to produce values of the temperature rise close to those readings recorded experimentally with a 95% confidence interval.A Matlab Genetic algorithm solver was utilized to do the optimization.

Optimization and sensitivity analysis of geometrical and process parameters to reduce vibration during end milling process

ABSTRACTSelection of geometrical and machining parameters has great influence in machining performance. Predictive modeling can be used in optimization and control of process parameters. This study focuses on the optimization and sensitivity analysis of machining parameters, and fine-tuning requirements to obtain better machining performance. A statistical prediction model was developed in terms of tool geometrical parameters such as rake angle, nose radius, and machining parameters such as cutting speed, feed rate, and depth of cut. Central composite response surface methodology with five parameters and five levels was used to create a mathematical model, and the adequacy of the model was checked using analysis of variance. The experiments were conducted on aluminum Al 6351 with high-speed steel end mill cutter. Vibration in terms of acceleration amplitude during end milling was measured with two accelerometers—one in tool holder (channel I) and other in workpiece fixture (channel II) respectively. Optimizations of process parameters were performed using genetic algorithm. Sensitivity analysis was performed using developed equations to identify the parameter exerting most influence on vibration amplitude.

TECHNOLOGY ROUGHNESS OF A MACHINED SURFACE IN MILLING OPERATION FOR FERROUS AND NON FERROUS A FUZZY LOGIC BASED MODEL TO PREDICT SURFACE MATERIALS USING HSS END MILL CUTTING TOOL

Nowadays every manufacturing and industrial industry has to focus on the manufacturing of quality products. Manufacturing of these kinds product with higher accuracy, better surface finish, lower maintenance and lower process planning and manufacturing cost are very important factor that can achieved by using non-conventional optimization techniques instead of conventional techniques. Many of non-conventional optimization techniques like Fuzzy Logic approach based technique, Genetic algorithms, Artificial Neural Network, Particle Swarm optimization, Ant colony optimization, Scatter search technique and simulated Annealing etc. are used to optimization of surface roughness. Milling is a machining operation in which workpiece is fed below the cylindrical rotating multi point cutting tool, multi point cutting tool having multiple cutting edges. On the basis of literature review, many machining parameters such as cutting speed, feed rate, depth of cut, cutting fluid pressure etc. and performance parameters as surface roughness, material removal rate, tool wear ratio, tool vibration etc., were observed for CNC milling operation. The correct selection of machining parameters is very important factor to achieve best performance measure. In this research work, spindle speed (SS), feed rate (FR) and depth of cut (DOC) are selected as machining parameters while surface roughness is considered as performance parameters to perform end milling operation on the workpiece materials of 6101 Aluminum alloy, Copper of electrolytic grade and Mild Steel 2062 by using High Speed Steel (HSS) end mill cutter of 12 mm diameter. Minimum experiment trials are designed by Taguchi based L9 (3^3) orthogonal array with the help of Minitab 17.0 software and a fuzzy logic approach based model is taken as to predict the value of surface roughness of a machined surface in 6101 aluminum alloy, Copper of Electrolytic grade and Mild Steel 2062 milling operation using HSS end mill cutter of 12 mmdiameter. Three membership functions are allocated to be connected with each input of the model. The predicted results achieved via fuzzy logic model are compared to the experimental result. The result demonstrated settlement between the fuzzy model and experimental results with the 95.618% model accuracy for 6101 aluminum alloy material, 83.849% for copper (Electrolytic grade) and 98.334% Mild Steel 2062.

Multi-response optimization in orthogonal turn milling by analyzing tool vibration and surface roughness using response surface methodology

Turn milling is one of the machining processes used to mill circular work pieces while the work piece rotates about its own axis. Orthogonal milling is one of the turn milling processes where the bottom part of cutter removes material from the rotating work piece with high metal removal rate. In this article, tool condition was studied by analyzing surface roughness and vibration of cutter with the use of response surface methodology. According to design of experiments, 16 experiments were conducted on ASTM B139 phosphor bronze with high-speed steel end mill cutter on four-axis milling machine. The response surface methodology was used to find out significant parameters that are affecting surface roughness and amplitude of cutter vibration. A multi-response optimization technique was used to identify optimum cutting parameters for less surface roughness and amplitude of cutter vibration.

Modeling of geometrical and machining parameters on temperature rise while machining Al 6351 using response surface methodology and genetic algorithm

This paper focused on the effect of geometrical and machining parameters such as rake angle, nose radius, cutting speed, feed rate, and depth of cut on temperature rise during end milling operation. The experiments were conducted on Al 6351 with high-speed steel end mill cutter. Central composite rotatable experimental design methodology was employed to conduct the experiments. The temperature rise during machining was measured using K-type thermocouple coupled with the thermal indicator. A prediction model was developed using response surface methodology and the adequacy of the model was verified using analysis of variance. The predictive model was used to analyze direct and interaction effect of the machining parameters on temperature rise, which helps to select proper combination of machining parameters to obtain better quality in machining. Genetic algorithm was applied to optimize the machining process parameters to obtain minimum rise in temperature. A program was developed using C language to do the optimization and the best optimal combination of machining parameters gave a value of 0.0105 °C.

Modeling and Analysis of Surface Roughness of AL7075-T6 in End Milling Process Using Response Surface Methodology

The quality of the surface is significantly important for evaluating the productivity of machine tools and mechanical parts. Optimum cutting condition is an extremely important task as it determines the surface quality of the manufactured parts. In this paper, a statistical model has been developed to predict surface roughness in terms of tool geometry such as rake angle, nose radius of cutting tool and machining parameters such as cutting speed, cutting feed rate and axial depth of cut. Response surface methodology experimental design has been engaged in conducting experiments. The workpiece material is aluminum (AL7075-T6), and the tool used is high-speed steel end mill cutter with different geometry. The surface roughness of machined surface is measured by Mitutoyo Surf Test SJ201. The second-order mathematical model in terms of machining parameters was evolved for predicting surface roughness. The adequacy of the model was checked by employing ANOVA. The direct and interaction effect are graphically plotted which aids to study the significance of these parameters with surface roughness.

Optimization of machining parameters to minimize vibration amplitude while machining Al 6063 using gray-based Taguchi method

Vibration during end milling causes a destructive effect: it produces poor surface finish, accelerates tool wear and reduces tool life. This article presents the use of Taguchi method–based gray relational analysis to optimize the machining parameters such as helix angle of cutting tool, cutting speed, feed rate, axial depth of cut and radial depth of cut for reduced vibration amplitude in end milling operation. L25 Taguchi orthogonal design was employed for conducting the experiments. The experiments were conducted on aluminum Al 6063 by high-speed steel end mill cutter, and acceleration amplitude was measured using fast Fourier transform analyzer. The online signals recorded for vibration amplitude picked by accelerometer at two positions: one fixed in the spindle (channel I) and another fixed in the workpiece fixture (channel II). Signal-to-noise ratio and analysis of variance were employed to determine the optimum levels. Gray relational grade was used to optimize machining parameters by considering both responses at a time.

Optimization of end mill tool geometry parameters for Al7075-T6 machining operations based on vibration amplitude by response surface methodology

This paper aims at developing a statistical model to envisage vibration amplitude in terms of geometrical parameters such as radial rake angle, nose radius of cutting tool and machining parameters such as cutting speed, cutting feed and axial depth of cut. Experiments were conducted through response surface methodology experimental design. The material chosen is Aluminum (Al 7075-T6) and the tool used was high speed steel end mill cutter with different tool geometry. Two channels piezoelectric accelerometers were used to measure the vibration amplitude. The second order mathematical model in terms of machining parameters was built up to predict the vibration amplitude and ANOVA was used to verify the competency of the model. Further investigation on the direct and interactive effect of the process parameter with vibration amplitude was carried out for the selection of process parameter so that the vibration amplitude was maintained at the minimum which ensures the stability of end milling process. The optimum values obtained from end milling process are Radial rake angle-12°, Nose radius-0.8mm, Cutting speed-115m/min, Cutting feed rate-0.04mm/tooth, axial depth of cut-2.5mm. The vibration amplitude exhibited negative relationship with radial rake angle and nose radius. The dominant factors on the vibration amplitude are feed rate and depth of cut. Thus it is envisaged that the predictive models in this study could produce values of the vibration amplitude close to the experimental readings with a 95% confidence interval.

Optimization of machining parameters on temperature rise in end milling of Al 6063 using response surface methodology and genetic algorithm

This present study focused on the effect of machining parameters such as helix angle of cutter, spindle speed, feed rate, axial and radial depth of cut on temperature rise in end milling. A prediction model of the temperature rise was developed using response surface methodology. The experiments were conducted on Al 6063 by high-speed steel end mill cutter based on central composite rotatable designs consisting of 32 experiments. The temperature rise was measured using K-type thermocouple. The adequacy of the model was verified using analysis of variance. The given model is utilized to analyze direct and interaction effect of the machining parameters with temperature rise. The optimization of machining process parameters to obtain minimum temperature rise was done using genetic algorithms. A source code using C language was developed to do the optimization. The obtained optimal machining parameters gave a value of 0.173 °C for minimum temperature rise.