Surface Roughness In Micro-milling Research Articles

Optimization of Machining Parameters to Minimize Cutting Forces and Surface Roughness in Micro-Milling of Mg13Sn Alloy.

This comprehensive study investigates the micro-milling of a Mg13Sn alloy, a material of considerable interest in various high-precision applications, such as biomedical implants. The main objective of the study was to explore the optimizations of variable feed per tooth (fz), cutting speed (Vc), and depth of cut (ap) parameters on the key outcomes of the micro-milling process. A unique experimental setup was employed, employing a spindle capable of achieving up to 60,000 revolutions per minute. Additionally, the study leveraged linear slides backed by micro-step motors to facilitate precise axis movements, thereby maintaining a resolution accuracy of 0.1 μm. Cutting forces were accurately captured by a mini dynamometer and subsequently evaluated based on the peak to valley values for Fx (tangential force) and Fy (feed force). The study results revealed a clear and complex interplay between the varied cutting parameters and their subsequent impacts on the cutting forces and surface roughness. An increase in feed rate and depth of cut significantly increased the cutting forces. However, the cutting forces were found to decrease noticeably with the elevation of cutting speed. Intriguingly, the tangential force (Fx) was consistently higher than the feed force (Fy). Simultaneously, the study determined that the surface roughness, denoted by Sa values, increased in direct proportion to the feed rate. It was also found that the Sa surface roughness values decreased with the increase in cutting speed. This study recommends a parameter combination of fz = 5 µm/tooth feed rate, Vc = 62.8 m/min cutting speed, and ap = 400 µm depth of cut to maintain a Sa surface roughness value of less than 1 µm while ensuring an optimal material removal rate and machining time. The results derived from this study offer vital insights into the micro-milling of Mg13Sn alloys and contribute to the current body of knowledge on the topic.

Investigation, modeling, and optimization of surface roughness in micro-milling of graphite electrodes

Investigation, modeling, and optimization of surface roughness in micro-milling of graphite electrodes

The Effects of Cutting Parameters to the Surface Roughness in High Speed Cutting of Micro-Milling Titanium Alloy Ti-6Al-4V

Micro-milling offers high flexibility by producing complex 3D micro-scale products. Weight reduction are one of the optimizations of the product that can make it stronger and more efficient nowadays. Titanium are the most commonly used for micro-scale products especially in biomedical industries because of the biocompatibility properties. Titanium alloys offers high strength with low density and high corrosion resistance that is suitable for weight reduction. This study aims to investigate the influence of high speed cutting parameters to the surface roughness in micromilling of titanium alloy Ti-6Al-4V as high speed cutting offers more productivity since producing more cutting length in the same time. experiments are carried out by micromilling process with variations in high speed cutting parameters of spindle speed and feed rate with a constant depth of cut using a carbide cutting tool of with a diameter of 1 mm. The machining results in the form of a 4 mm slot with a depth as the same as depth of cut, which then measures its surface roughness. It was found that higher feed rate that is followed by higher spindle speed will produce better surface roughness.

Influence of Size Effect on Cutting Edge Rounding and Surface Roughness in Micro-Milling of Ti-6Al-4V

The quality of cutting depends most on the cutting tool condition. Towards having a good quality finish, cutting tolerance becomes a major concern, especially when machining at micro-scale where highly precise cutting is desired. This research investigates the size-effect during the micro-milling of Ti-6Al-4V under dry condition where the observations were made on cutting edge rounding (CER) and workpiece surface roughness. The result showed that the lower the feed rate, the greater rounding on the cutting edges were observed. Similar trend in result was obtained when measuring the surface roughness. The best feed rate for both observations was at 60 mm/min, where this setting has brought the mechanism to shearing, as the ratio between undeformed chip thickness and cutting edge radius started at 1.

Multiple regression prediction model for cutting forces and surface roughness in micro-milling of TA2

Abstract Titanium and its alloys are widely used in the biomedical fields, such as implant, surgical instrument, and the dynamic compression plate. Compared with Ti-6Al-4V, TA2 has lower thermal conductivity, better affinity, and adhesion, resulting in poor machinability during processing. Optimum machining solution is required to improve the surface quality, to reduce the wear of tool, and to save the cost of production. This manuscript presents experimental results of micro-milling test conducted on TA2 with changing cutting parameter, two coating tool, and different cooling methods. Also, the orthogonal regression prediction mathematical model of pure titanium micro-milling of TA2 cutting force and surface temperature have been obtained. And the model provides a reference value for the medical pure titanium processing, aiming at the smallest cutting force and surface roughness with optimum parameters.

Investigation of material removal rate and surface roughness using multi-objective optimization for micro-milling of inconel 718

PurposeThe purpose of this study is to realize the multi-objective optimization for MRR and surface roughness in micro-milling of Inconel 718.Design/methodology/approachTaguchi method has been applied to conduct experiments, and the cutting parameters are spindle speed, feed per tooth and depth of cut. The first-order models used to predict surface roughness and MRR for micro-milling of Inconel 718 have been developed by regression analysis. Genetic algorithm has been utilized to implement multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718.FindingsThis paper implemented the multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718. And some conclusions can be summarized. Depth of cut is the major cutting parameter influencing surface roughness. Feed per tooth is the major cutting parameter influencing MRR. A number of cutting parameters have been obtained along with the set of pareto optimal solu-tions of MRR and surface roughness in micro-milling of Inconel 718.Originality/valueThere are a lot of cutting parameters affecting surface roughness and MRR in micro-milling, such as tool diameter, depth of cut, feed per tooth, spindle speed and workpiece material, etc. However, to the best our knowledge, there are no published literatures about the multi-objective optimization of surface roughness and MRR in micro-milling of Inconel 718.

Forces, form deviations and surface roughness in micro-milling of CoCr alloys for dental prostheses

Micro-milling, besides selective laser melting, is commonly used for the manufacture of dental prostheses. Demands to the application of these prostheses require hard and tough materials like cobalt-chrome alloys or ceramics. The cutting process is governed by static as well as dynamic interactions between the process and the structure of the workpiece. These interactions deteriorate the work result, especially the shape accuracy. The deflection of the milling tool is one effect, which has a significant influence. However, the deflection can be minimised by optimising the manufacturing strategy, by an adjustment of the process parameters or by optimising the tool path, which leads to an increased quality of the final product. The work presented in this paper aims to establish a connection between tool deflection and resulting form deviations during micro-milling of defined geometries in CoCr alloys. This is achieved by measuring the active forces while machining an defined test geometry with different feed rates and radial infeeds and a subsequent evaluation of the resulting form. Furthermore, the influence of the workpiece geometry on the forces and form deviations is analysed to serve as a foundation for future optimisation steps. In order to quantify the surface quality, white light interferometry is applied to measure the surface roughness Sa and Sq and the influence of feed rate vf and radial infeed ae on the surface roughness is derived.

Forces, form deviations and surface roughness in micro-milling of CoCr alloys for dental prostheses

Forces, form deviations and surface roughness in micro-milling of CoCr alloys for dental prostheses

EFFECT OF MINIMUM QUANTITY LUBRICATION ON SURFACE ROUGHNESS IN TOOL-BASED MICROMILLING

Cutting fluid plays an important role in machining processes to achieve dimensional accuracy in reducing tool wear and improving the tool life. Conventional flood cooling method in machining processes is not cost effective and consumption of huge amount of cutting fluids is not healthy and environmental friendly. In micromachining, flood cooling is not recommended to avoid possible damage of the microstructures. Therefore, one of the alternatives to overcome the environmental issues to use minimum quantity of lubrication (MQL) in machining process. MQL is eco-friendly and has economical advantage on manufacturing cost. However, there observed lack of study on MQL in improving machined surface roughness in micromilling. Study of the effects of MQL on surface roughness should be carried out because surface roughness is one of the important issues in micromachined parts such as microfluidic channels. This paper investigates and compares surface roughness with the presence of MQL and dry cutting in micromilling of aluminium alloy 1100 using DT-110 milling machine. The relationship among depth of cut, feed rate, and spindle speed on surface roughness is also analyzed. All three machining parameters identified as significant for surface roughness with dry cutting which are depth of cut, feed rate, and spindle speed. For surface roughness with MQL, it is found that spindle speed did not give much influence on surface roughness. The presence of MQL provides a better surface roughness by decreasing the friction between tool and workpiece.

Research on the prediction model of micro-milling surface roughness of Inconel718 based on SVM

Purpose – The purpose of this paper is to establish a roughness prediction model of micro-milling Inconel718 with high precision. Design/methodology/approach – A prediction model of micro-milling surface roughness of Inconel718 is established by SVM (support vector machine) in this paper. Three cutting parameters are involved in the model (spindle speed, cutting depth and feed speed). Experiments are carried out to verify the accuracy of the model. Findings – The results show that the built SVM prediction model has high prediction accuracy and can predict the surface roughness value and variation law of micro-milling Inconel718. Practical implication – Inconel718 with high strength and high hardness under high temperature is the suitable material for manufacturing micro parts which need a high strength at high temperature. Surface roughness is an important performance indication for micro-milling processing. Establishing a roughness prediction model with high precision is helpful to select the cutting parameters for micro-milling Inconel718. Originality/value – The built SVM prediction model of micro-milling surface roughness of Inconel718 is verified by experiment for the first time. The test results show that the surface roughness prediction model can be used to predict the surface roughness during micro-milling Inconel718, and to provide a reference for selection of cutting parameters of micro-milling Inconel718.

Analysis of Surface Roughness in Micro Milling of Ti-6Al-4V Titanium Alloy

This work shows a study on the micro milling of Ti-6Al-4V Titanium alloy where the effect of tool wear on the surface finish of the machined part was analyzed. New and worn micro cutters were applied to produce grooves with 0.5 mm of width and 0.025 mm of heights. The surface roughness was measured in the Ra and Rz values and the results showed that the surface roughness was not influenced by the tool’s condition. However, when new tools were used the increase of cutting speed generated an improving of the surface roughness and the same effect occurred with the decrease of feed rate.

Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling

In this study, micro-milling of AISI 304 stainless steel with ball nose end mill was conducted using Taguchi method. The influences of spindle speed, feed rate and depth of cut on tool wear, cutting forces and surface roughness were examined. Taguchi's signal to noise ratio was utilized to optimize the output responses. The influence of control parameters on output responses was determined by analysis of variance. In this study, the models describing the relationship between the independent variables and the dependent variables were also established by using regression and fuzzy logic. Efficiency of both models was determined by analyzing correlation coefficients and by comparing with experimental values. The results showed that both regression and fuzzy logic modelling could be efficiently utilized for the prediction of tool wear, cutting forces and surface roughness in micro-milling of AISI 304 stainless steel.

An Approach to Improve Machined Surface Finish in Micro Milling

Micro milling has been known to behave quite differently from conventional macro milling. The effect of cutting speed on the machined surface roughness in micro milling of ductile materials was investigated in this paper. The micro flat end milling cutters of the diameters of 0.5 mm and 0.3 mm were taken to machine 6061 aluminum alloy and S20C low carbon steel. For comparison purpose, the cutter of the diameter of 10 mm was tested as well. It is found that the machined surface finish is closely related to the cutting speed. For a large size cutter, the roughness decreases with the increase of cutting speed as expected. But the decreasing trend is valid only up to a certain speed for the small diameter cutters. It is also noted that beyond this speed limit broken and powder type chips form easily. These chips will adhere to the machined surface and tool face and deteriorate machined surface as a result. Based on this finding, an approach by directing the cutting fluid to the cutting zone via high pressure air assistance is proposed. It is verified that the cutting force and its variation are greatly reduced, tool wear becomes less, and finish of the machined surface is significantly improved.

Research on the prediction model of micro-milling surface roughness

Surface roughness is an important performance indication for micro-milling processing. Establishing a roughness-prediction model with high-precision is helpful to select the cutting parameters for micro-milling. Two prediction models are established by response surface method (RSM) and support vector machine regression (SVM) in this paper. Four cutting parameters are involved in the models (extended length of micro-milling tool, spindle speed, feed per tooth, and cutting depth in the axial direction). The models are established for material of brass. Experiments are carried out to verify the accuracy of the models. The results show that SVM prediction model has higher prediction accuracy, predict the variation law of micro-milling surface roughness better than RSM.

Investigation of Vibration and Surface Roughness in Micro Milling of PMMA

This paper presents the vibration and surface roughness issue of poly methyl methacrylate (PMMA) workpiece produced by micro end milling using integrated multi-process machine tools DT 110 (Mikrotools Inc., Singapore) with control parameter; spindle speed, feed rate, and depth of cut. The vibration was measured using accelerometer, DYTRAN Instrument and the average surface roughness Ra was measured using Wyko NT1100. The optimum solution for minimum average vibration is 64.3 Hz with spindle speed 3000 rpm, feed rate 2 mm/min, and depth of cut 1.5 μm. However, the optimum solution for minimum average surface roughness, Ra is 0.352 μm with spindle speed 2000 rpm, feed rate 2 mm/min, and depth of cut 1.5 μm. The micro end milling parameters are suitable to machine PMMA to get good precision surface roughness. The analysis revealed that the feed rate and depth of cut is the most influential parameter on vibration during machining process meanwhile for average surface roughness, Ra spindle speed is the most influential parameter.

Optimization of Surface Roughness in Micromilling

Micromilling is a process that utilizes end mills that typically vary in diameter from 100 to 1000 µm and have edge radii that vary from 1 to 10 µm. In the present article, the machining of microsurfaces on aluminium alloy is made, using conventional CNC (Computer numerical control) milling center and commercially available miniature tools. In order to perform a comprehensive study on the surface roughness of the machined surfaces cutting parameters such as feed rate as well as machining strategies were varied to optimisation micromilling.