Face Milling Machining Research Articles

Utilization of Mechanically Recycled Carbon Fibers in Vinyl Ester Composites.

As we enter the twenty-first century, the aviation sector is expected to thrive as flying becomes the primary mode of transportation between states or nations. With such a demand, there is a corresponding need to manufacture aircraft components. The study focused on recycling carbon fiber composites received from the STRATA company, which were cut-off/waste material generated during the manufacture of airplane components. The cut-offs were then reduced to powder form using a standard face milling machine in three sizes (90, 150, and 250 µm). After, the powder was utilized to fabricate vinyl ester composites with four weight percentages (10%, 20%, 30%, and 40%). The results demonstrate that the tensile strength of all composites had risen by 30.2%, 21.3%, and 17.6% for 90, 150, and 250 µm respective with the addition of 20 wt% of reinforcement. Furthermore, subsequently decreased with the additional reinforcement for all particle sizes. The compressive strength increased by 30% from 187.5 MPa to 244 MPa with 10 wt% of recycled carbon powder composite of 90μm particle size. However, samples prepared with 150 μm and 250 μm fiber size show approximately 17% and 1% increase in the compression strength with the addition of 10wt% of recycled carbon powder. A similar trend was observed for the flexural strength with an highest increase of 9% for 90 µm particle size with addition of 20 wt% reinforcement. Nonetheless, the SEM images revealed that the fiber-matrix bonding was weak, proved through the clean pullout fibers at the fracture surfaces.

Comparative Study of the Effect of Dry, Mineral Oil, and TiO2 Nano-Lubricant on Tool Wear During Face-Milling Machining of Ti-6al-4v-Eli Using Carbide Tool Insert

Titanium alloys are valuable materials in the manufacturing industry. They are applied to develop major parts in the aerospace, automobile, and aeronautic industry. The major challenge faced by the manufacturer is the ability to cut Titanium alloys to the specific shape desired. The influence of nanoparticles as an additive to the mineral oil is vital in the machining of Titanium alloys to reduce vibration and friction, leading to high wear in the cutting tool. The quest for a sustainable and reliable method to minimize tool wear and increase efficiency has led to the nano-lubricants application during metal machining. Therefore, this research focus on the impact of three machining cutting conditions on TI-6AL-4V-ELI during face-milling operations. The Taguchi experimental design method was employed to study the machining factors effects on the tool wear by varies the machining factors, such as Cutting speed: 2000 rpm, 2500 rpm, and 3000 rpm, Feed rate: 150 mm/min, 200 mm/min, 250 mm/min, Depth of Cut: 0.3 mm, 0.6 mm, and 0.9 mm and the cutting conditions: dry, mineral oil and Titanium Oxide (TiO2) nano-lubricant to study their effects on the Carbide Insert cutting tool wear minimization for improving workpiece's machinability. This research also studies the interactions of the machining factors on the tool wear under the three lubrication cutting conditions. The findings confirm that the state of TiO2 nano-lubricants decreased the tool wear for all the experimental runs. The signal-to-noise ratio results from the Taguchi design show that the cutting condition has a significant role in having 5.794. The cutting speed of 2.145, the feed rate of 0.789, and the depth of cut were 0.724. Furthermore, the generated model could predict the cutting tool wear rate with 97%, proving that the experimental result is viable for application in the manufacturing industry.

Investigation of multi-variables through weight calculation approach for face milling of EN 31 alloy steel

Usually, the analysis of multi variables is done merely by giving equal weightage to the output responses, but in fact that doesn’t help us to achieve the precise results. Taking into consideration the same thing, the approach used in the present work is to evaluate the weight to be assigned to the output variables. Criteria Importance Through Inter Criteria (CRITIC) method is adopted for the calculation of weight. After weight evaluation, nearly equal weightage has been found for the tool performance parameters viz. tool vibration and tool wear. Different modes of cooling or lubrication such as Dry machining, Flood cooling and Minimum Quantity Lubrication have been used during experimental work. Face milling machining has been done on EN 31 alloy steel for the aforementioned input and output characteristics. The calculated weight assigned to the output variables and optimization technique named Grey relational analysis is implemented to get the final set of optimum parameters. The optimal settings gained are cutting speed 1200 rpm, feed 50 mm/min, lubricant 100 ml/hr. and depth 0.1 mm. Correlation between the output characteristics have been analyzed and it has been seen that both shows similar trend against the input variables. To describe that how much a model explains the variability of output against input parameters, mathematical models have also been developed for the individual and combined optimization of the responses as well.

Decoding of Factorial Experimental Design Models Implemented in Production Process

The paper deals with factorial experimental design models decoding. For the ease of calculation of the experimental mathematical models, it is convenient first to code the independent variables. When selecting independent variables, it is necessary to take into account the range covered by each. A wide range of choices of different variables is presented in this paper. After calculating the regression model, its variables must be returned to their original values for the model to be easy recognized and represented. In the paper, the procedures of simple first order models, with interactions and with second order models, are presented, which could be a very complicated process. Models without and with the mutual influence of independent variables differ. The encoding and decoding procedure on a model with two independent first-order parameters is presented in details. Also, the procedure of model decoding is presented in the experimental surface roughness parameters models’ determination, in the face milling machining process, using the first and second order model central compositional experimental design. The simple calculation procedure is recommended in the case study. Also, a large number of examples using mathematical models obtained on the basis of the presented methodology are presented throughout the paper.

Experimental analysis of cutting force during machining difficult to cut materials under dry, mineral oil, and TiO2 nano-lubricant

Difficult-to-machine materials, e.g., Titanium alloys, are highly applicable in diverse industries that yield strength and wear resistance. However, they prove difficult to machine due to high vibration, leading to high cutting forces during the machining process. This vibration occurs from chip discontinuity and thereby leads to high friction between the cutting tool and workpiece. In order to minimize these challenges, lubricants are employed in machining operations to reduce frictional and other unnecessary cutting forces and improve surface finish. This research focuses on studying the nano-lubricant effects in reducing cutting forces in the machining of TI-6AL-4V-ELI alloy. Also, carry out a comparative study of dry, mineral oil, and TiO2 nano-lubricant during face-milling machining for optimal performance. Additionally, the study develops a predictive mathematical model for cutting force using a Taguchi L9 orthogonal array. A two-step approach was employed to develop the nano-lubricant before the machining process. The dynamometer is used to collect the cutting force data at the end of each sample. The Results show that the lubrication conditions play a significant role in the reduction of cutting forces. The mineral oil-based-TiO2 nano-lubricant reduces the cutting force by 19 % compared with the mineral oil during the machining of TI-6AL-4V-ELI alloy. Furthermore, the optimal parameters to reduce cutting forces during face milling of TI-6AL-4V-ELI alloy are cutting speed at 3000 rpm, 200 mm/min feed rate, 0.3 mm depth of cut to obtain the minimum cutting force 30 (N). This study concludes that the application of TiO2 nanoparticles in mineral oil significantly improves the thermal and mechanical properties, which leads to a reduction of cutting force.

PENENTUAN KECEPATAN PEMOTONGAN EFEKTIF BERDASARKAN NILAI KEKASARAN PERMUKAAN MATERIAL AA-7075 PADA PROSES FACE MILLING

During face milling machining, several machining parameters such as feed rate and cuttingspeed determine the surface quality of the workpiece produced by the process. The selection of the rightparameters will lead to the surface quality as planned. Therefore, to improve machining effectiveness, amethod is needed to determine the appropriate machining parameters to produce the desired surfacequality. This research was conducted using a milling machine, five variations of cutting speed and fivevariations of feed rate were used to cut the workpiece aluminum alloy 7075. After machining, the surfaceroughness was measured using a surface test. The surface roughness value is then substituted into thefeed rate equation and effective cutting speed. By finding effective cutting parameters, the machiningprocess will be more efficient and effective without using unnecessary resources. From the results of thestudy note that the development equation to determine the feed rate based on the value of surfaceroughness is ???? = 0,6????√???? ????????0.443mm/tooth. Development equation to determine the effective cutting speedbased on Surface roughness value is ???????? = 3.0686????????0.124 mm/min

Analisis Kekasaran Permukaan Cast Iron Menggunakan Cairan Pendingin Berbasis Nabati Pada Proses Face milling

The machining process is a major activity in the industry, especially in the automotive industry. The machining process is inseparable from the coolant (coolant), where the role of the coolant in the machining process is to act as cool working conditions, material cleaning, and corrosion prevention. The use of cutting fluid has a negative impact when waste management is not carried out properly, and it will be damaging to the environment and public health. In order to reduce environmental and health damage, development and research have now been carried out for the use of vegetable-based cutting fluid. cutting fluid vegetable-based are biodegradable, eco-friendly, odorless, and non-toxic. This study wants to compare the value of surface roughness using CO (coconut oil) and dromus in face milling machining. The machine used in this research is the conventional Emco F3 milling machine and the tools used are the TPKN 22VC2 series carbide insert tools and the workpiece material used is cast iron (gray cast iron). the value of surface roughness by measuring the roughness using the TR200 surface roughness tool. The results of roughness observations show that the surface roughness value using CO cutting fluid at a cutting speed of 60 mm/min is 1,297 Ra, at a cutting speed of 70 mm/min the roughness value is 0.877 Ra and at a cutting speed of 80 mm/min, it is 0.429 Ra. While the results of measuring surface roughness on the material using a coolant dromus, cutting speed of 60 mm/min are 0.811 Ra, at a cutting speed of 70 mm/min the surface roughness value is 0.804 and at a cutting speed of 80 mm/min the surface roughness value is 0.698 Ra. So can the conclusion be that cutting fluid CO is good to use as a coolant at high cutting speeds.

Least Squares Boosting Ensemble and Quantum-Behaved Particle Swarm Optimization for Predicting the Surface Roughness in Face Milling Process of Aluminum Material

Surface roughness is a significant factor in determining the product quality and highly impacts the production price. The ability to predict the surface roughness before production would save the time and resources of the process. This research investigated the performance of state-of-the-art machine learning and quantum behaved evolutionary computation methods in predicting the surface roughness of aluminum material in a face-milling machine. Quantum-behaved particle swarm optimization (QPSO) and least squares gradient boosting ensemble (LSBoost) were utilized to simulate numerous face milling experiments and have predicted the surface roughness values with high extent of accuracy. The algorithms have shown a superior prediction performance over genetics optimization algorithm (GA) and the classical particle swarm optimization (PSO) in terms of statistical performance indicators. The QPSO outperformed all the simulated algorithms with a root mean square error of RMSE = 2.17% and a coefficient of determination R2 = 0.95 that closely matches the actual surface roughness experimental values.

Accurate Machine-Settings for the Face-Milling of Hypoid Gears

A mathematical model of the machine-settings for the face-milling of hypoid gears using accurate cutter system and a generic spiral bevel/hypoid face-milling machine is developed. Accurate cutter system is considered, which contains the inside and outside blades with pre-determined rake, relief, and blade angles in order to generate the cutter system envelope. Since, the machine-settings are based on the tangency between the gear flank and the cutter sweep envelope geometry and gear flank is sensitive to the cutter sweep geometry, therefore, the machine-settings are also sensitive to the cutter system geometry. Numerical applications for specific gear geometry are applied, in which machine-settings are calculated and compared with the previous research results. In the end of the article, a simulation was performed to generate gear tooth surfaces. Surface deviation between the gear teeth generated with the machine-settings for the simplified cutter system and the accurate cutter system are compared and the conclusion is drawn that the rake face of the blades in the cutter system has influence on the machine-settings. Due to the use of an accurate cutter system, the hypoid gears produced using this model are accurate and reduce the need of corrections in the machine-settings.

Research on cutting vibration characteristics of face-milling involute gear

Abstract Traditional machining methods, such as gear hobbing, gear shaping and gear milling, etc, are commonly used for cutting machining of gear tooth profile, which cannot meet huge machining demand of gears to a certain extent. This article proposes to utilize a face-milling machining method in involute gear machining, which can be used to reduce production cost effectively. Cutting vibration generated during cutting machining has a direct effect on the machining accuracy and machined surface quality of workpiece. Therefore, it is desiderated to perform in-depth research regarding this issue. ADAMS software was used to establish a rigid-flexible coupling virtual prototyping model of face-milling gear milling system and a cutting vibration system model. Cutting vibration analysis was performed for face-milling gear by adopting quick sine frequency sweep method, so that the frequency response characteristics of workpiece in three directions X , Y and Z and space were acquired. The research results will provide reference and theoretical foundation for actual application of face-milling gear machining technology.

Al2024의 고속 정면밀링 가공에서 표면 거칠기에 관한 연구

항공기와 자동차 부품과 관련된 많은 제조업에서, 낮은 비중과 높은 강도에서 뛰어난 알루미늄 합금(Al2024)은 효과적으로 사용되었다. 가공소재의 표면거칠기 품위를 위한 정면밀링 가공기술은 이들 분야에서 적용되어 왔다. 챔퍼된 드로우 어웨이 타입의 인서트를 갖는 정면밀링 가공은 단지 이론적으로 완전한 평면을 생산할 수 있다. 그러나 그것은 절삭온도, 소성변형, 동적효과 등으로 인하여 불가능하다. 본 논문에서 실험적 연구는 검증된 고속가공이 가능한 정면밀링커터 바디를 사용하여 Al2024의 고속가공 후 표면거칠기를 개선하기 위하여 수행되었다. In many manufacturing such as the components of airplane and automobile, aluminum alloys(Al2024) which remarkable in low specific gravity and high strength have been utilized effectively. Face milling machining technology for surface roughness quality of workpiece has been applied in these fields. A face milling machining with chamfered throw away type insert tip can produce a perfect flat surface only in theory. But It is impossible because of many unwanted factors, namely, cutting temperature, plastic deformation, dynamic effect, etc. In this paper, experimental investigations are performed to improve surface roughness after high speed machining of Al2024 using qualified face milling cutter body for high speed machining.

Tool Life Prediction in Face Milling Machining of 7075 Al by Using Artificial Neural Networks (ANN) and Taguchi Design of Experiment (DOE)

Tool Life Prediction in Face Milling Machining of 7075 Al by Using Artificial Neural Networks (ANN) and Taguchi Design of Experiment (DOE)

High Speed Face Milling Machining and Reliability Examination of Al-2024 by the Design of Experiment

High Speed Face Milling Machining and Reliability Examination of Al-2024 by the Design of Experiment

Artificial Neural Networks for Surface Roughness Prediction when Face Milling Al 7075-T7351

In this work, different artificial neural networks (ANN) are developed for the prediction of surface roughness (Ra) values in Al alloy 7075-T7351 after face milling machining process. The radial base (RBNN), feed forward (FFNN), and generalized regression (GRNN) networks were selected, and the data used for training these networks were derived from experiments conducted using a high-speed milling machine. The Taguchi design of experiment was applied to reduce the time and cost of the experiments. From this study, the performance of each ANN used in this research was measured with the mean square error percentage and it was observed that FFNN achieved the best results. Also the Pearson correlation coefficient was calculated to analyze the correlation between the five inputs (cutting speed, feed per tooth, axial depth of cut, chip’s width, and chip’s thickness) selected for the network with the selected output (surface roughness). Results showed a strong correlation between the chip thickness and the surface roughness followed by the cutting speed.

A Fiber-Optic Bending Sensor for the Vibration Monitoring of CNC Face-Milling Machine

Vibration measurement of CNC milling is one of the used techniques for prediction of tool wear. Monitoring of tool wear is very important since a worn tool will affect machine and workpiece either. We developed a fiber-optic sensor for spindle vibration of CNC face-milling machine. The sensor is based on monitoring loss of light from the fiber core. For this sensor a laser light transmitter circuit, a sense plane construction, and a light receiver circuit are designed. Designed fiber-optic sensor is tested on Taksan TMC 650V face-milling machine. Obtained signals from this sensor is investigated in time domain and frequency domain and showed that it is valuable to tool ware monitoring.

Progressive development of an absolute sensorless compensation system for cutting force-induced error

Cutting forces in traditional machining processes solely originates from the contact points on the cutting tool and workpiece. Therefore comprehensive mechanistic modeling of the machining process offers a means for realizing a sensorless cutting force monitoring system. This paper presents the progressive development of a sensorless compensation system for cutting force-induced error, whereby a learning and intelligent computer system is established, based on machining mechanics modeling and a reference compensation system. Experiences from normal machining sessions of new cutting tools and workpieces are modeled progressively and incorporated into the system. Finally with ample experience available, a full-fledged sensorless system is developed as a stand-alone solution. The sensorless system is economical, convenient, reliable and efficient. Administered on a CNC face milling machine, the model demonstrated exceptional performance and robustness.

Generation and curvature analysis of conjugate surfaces via a new approach

In the first part, the alternative formulation of the theory of gearing presented in [F. Di Puccio, M. Gabiccini, M. Guiggiani, Alternative formulation of the theory of gearing, Mechanism and Machine Theory 40 (5) (2005) 613–637] is generalized to cope with the case of modified roll and translating axes of the mating surfaces, that is with a setting typical of some computer numerically controlled (CNC) machine. The peculiarity of the proposed approach is that it always deals with vectors as such, thus avoiding the need of a chain of reference systems, necessary when components are used as in the traditional method. It results in a more compact and clearer formulation. In the second part the proposed approach is applied to obtain the normal curvatures and geodesic torsions of two conjugate surfaces in a way that fully exploits the rigid-body relative motions involved in gear generation. In order to prove the ease of use and effectiveness of the proposed approach, the curvature analysis of a spiral bevel pinion generated on a CNC face-milling machine is performed.

Performance of PVD TiN/TaN and TiN/NbN superlattice coated cemented carbide tools in stainless steel machining

Cemented carbide cutting tools coated with superlattice TiN/TaN and TiN/NbN lamellae thin films were evaluated in face milling machining tests of austenitic stainless steel (AISI/SAE 303/304). The coatings were grown by PVD on cemented carbide inserts by a combination of ion plating of TiN and reactive magnetron sputtering of NbN and TaN. The lamellae coatings were macroscopically disordered as a consequence of the relatively rough surface of the carbide substrate, but the coatings did exhibit a microscopic superlattice structure. For both TiN/TaN and TiN/NbN, the two constituent layers of the superlattices crystallized in the cubic B1 structure. The TiN/NbN lamellea coating showed a Vickers microhardness ( HV 0.5 N ) of 32 GPa and the TiN/TaN lamellea coating exhibited a HV 0.5 N value of 39 GPa. The results from the machining tests indicate a superior performance of tools coated with the harder lamellae coatings as compared to tools coated with single layer PVD or CVD coatings.