scholarly journals Study on model for cutting force when milling SCM440 steel

2021 ◽  
pp. 23-35
Author(s):  
Nguyen Van Thien ◽  
Do Duc Trung
Keyword(s):  
Cutting Force ◽  
Cutting Speed ◽  
Absolute Error ◽  
Experimental Results ◽  
Parameter Analysis ◽  
Coefficient Of Determination ◽  
Data Transformations ◽  
Study Results ◽  
Input Parameters ◽  
Cutting Insert

This article presents empirical study results when milling SCM440 steel. The cutting insert to be used was a TiN coated cutting insert with tool tip radius of 0.5 mm. Experimental process was carried out with 18 experiments according to Box-Behnken matrix, in which cutting speed, feed rate and cutting depth were selected as the input parameters of each experiment. In addition, cutting force was selected as the output parameter. Analysis of experimental results has determined the influence of the input parameters as well as the interaction between them on the output parameters. From the experimental results, a regression model showing the relationship between cutting force and input parameters was built. Box-Cox and Johnson data transformations were applied to construct two other models of cutting force. These three regression models were used to predict cutting force and compare with experimental results. Using parameters including coefficient of determination (R-Sq), adjusted coefficient of determination (R-Sq(adj)) and percentage mean absolute error (% MAE) between the results predicted by the models and the experimental results are the criteria to compare the accuracy of the cutting force models. The results have determined that the two models using two data transformations have higher accuracy than model not using two data transformations. A comparison of the model using the Box-Cox transformation and the model using the Johnson transformation was made with a t-test. The results confirmed that these two models have equal accuracy. Finally, the development direction for the next study is mentioned in this article

Influence of process variables on machining characteristics in turning of novel AM alloy

2020 ◽  
pp. 095440542097809
Author(s):  
Sunil Dutta ◽  
Suresh Kumar Reddy Narala
Keyword(s):  
Cutting Force ◽  
Tool Life ◽  
Flank Wear ◽  
Surface Condition ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Experimental Results ◽  
Depth Of Cut ◽  
Input Parameters ◽  
Response Variables

In this paper, the machinability of a fabricated AM alloy (Mg-7 wt%Al-0.9 wt%Mn) has been examined. The novel AM alloy was subjected to turning using a systemized CNC setup. The input turning variables: feed ( f), cutting speed ( v), and depth of cut (DOC) were suitably altered to analyze effects on response variables such as cutting force ( Fc), cutting temperature ( T), and tool life ( TL). Subsequently, the microstructure characterization of the machined surface was done for validating the experimental results. The experimental results established the influence of input parameters on response variables. The cutting force was mostly dominated by DOC, and the cutting temperature was predominantly influenced by cutting speed. The SEM images exhibited the adverse effect of higher values of input parameters on the surface condition. The finest surface was observed at f: 0.1 mm/rev, DOC: 0.5 mm, and v: 115 m/min. Further, the analysis of tool life was done by assessing the flank wear; the measured data showed the significant influence of cutting speed on flank wear. The maximum tool life of 51 min was achieved at the lowest levels of three input parameters.

Modeling and optimizing of cutting force and surface roughness in milling process of Inconel 738 using hybrid ANN and GA

2019 ◽  
Vol 234 (5) ◽  
pp. 920-932 ◽  
Author(s):  
Lila Imani ◽  
Ali Rahmani Henzaki ◽  
Reza Hamzeloo ◽  
Behnam Davoodi
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
Turbine Blades ◽  
Cutting Speed ◽  
Experimental Results ◽  
Input Parameters ◽  
Four Levels ◽  
Milling Forces ◽  
Axial Depth

Super-alloys have high thermal and mechanical strength and are widely used for heat exchangers, turbine blades, and other parts which work under severe creep conditions. Machinability of these alloys is directly affected by mechanical and physical properties. In addition, cutting force and surface roughness are two important factors in machinability of alloys. Hence, numerous studies have been conducted in order to illustrate their influences. However, among these alloys, the machining of Inconel 738 has been less studied. Milling parameters such as cutting speed, feed rate, the axial depth of cutting, and coolant have the most effects on machinability of nickel-based super-alloys. Therefore, in this research, they are considered as input parameters for investigation of milling of Inconel 738. The present study utilizes artificial intelligence as an effective method for predicting milling forces and surface roughness based on experimental results. To investigate the behavior of this alloy, four levels for the two former input parameters and two levels for the two other, totally 64 experiments, were fulfilled and studied. Based on the experimental results, the effect of input parameters on the outputs, that is, cutting force and surface roughness, was investigated, and then, neural network for modeling and predicting and genetic algorithm for the optimization of the outputs have been utilized. The optimized artificial network, which was obtained in this research, is useful for prediction of machining force and surface roughness of milling based on the values of cutting speed, feed rate, and the axial depth of cutting, for wet and dry milling of Inconel 738.

scholarly journals Multi-objective optimization of SKD11 Steel Milling Process by Reference Ideal Method

2021 ◽  
Vol 15 ◽  
pp. 1-16
Author(s):  
Do Duc Trung
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Cutting Speed ◽  
Milling Process ◽  
Multi Objective Optimization ◽  
Small Surface ◽  
Multi Objective ◽  
Input Parameters ◽  
Machining Productivity ◽  
Ideal Method

For all machining cutting methods, surface roughness is a parameter that greatly affects the working ability and life of machine elements. Cutting force is a parameter that not only affects the quality of the machining surface but also affects the durability of cutter and the level of energy consumed during machining. Besides, material removal rate (MRR) is a parameter that reflects machining productivity. Workpiece surface machining with small surface roughness, small cutting force and large MRR is desirable of most machining methods. This article presents a study of multi-objective optimization of milling process using a face milling cutter. The experimental material used in this study is SKD11 steel. Taguchi method has been applied to design an orthogonal experimental matrix with 27 experiments (L27). In which, five parameters have been selected as the input parameters of the experimental process including insert material, tool nose radius, cutting speed, feed rate and cutting depth. Reference Ideal Method (RIM) is applied to determine the value of input parameters to ensure minimum surface roughness, minimum cutting force and maximum MRR. Influence of the input parameters on output parameters is also discussed in this study.

scholarly journals Effects of Turning Parameters and Parametric Optimization of the Cutting Forces in Machining SiCp/Al 45 wt% Composite

Metals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 840 ◽  
Author(s):  
Rashid Ali Laghari ◽  
Jianguang Li ◽  
Mozammel Mia
Keyword(s):  
Mathematical Model ◽  
Cutting Force ◽  
Feed Rate ◽  
Cutting Forces ◽  
Desirability Function ◽  
Interactive Effect ◽  
Cutting Speed ◽  
Experimental Results ◽  
Machining Process ◽  
Depth Of Cut

Cutting force in the machining process of SiCp/Al particle reinforced metal matrix composite is affected by several factors. Obtaining an effective mathematical model for the cutting force is challenging. In that respect, the second-order model of cutting force has been established by response surface methodology (RSM) in this study, with different cutting parameters, such as cutting speed, feed rate, and depth of cut. The optimized mathematical model has been developed to analyze the effect of actual processing conditions on the generation of cutting force for the turning process of SiCp/Al composite. The results show that the predicted parameters by the RSM are in close agreement with experimental results with minimal error percentage. Quantitative evaluation by using analysis of variance (ANOVA), main effects plot, interactive effect, residual analysis, and optimization of cutting forces using the desirability function was performed. It has been found that the higher depth of cut, followed by feed rate, increases the cutting force. Higher cutting speed shows a positive response by reducing the cutting force. The predicted and experimental results for the model of SiCp/Al components have been compared to the cutting force of SiCp/Al 45 wt%—the error has been found low showing a good agreement.

scholarly journals An Improved Johnson–Cook Constitutive Model and Its Experiment Validation on Cutting Force of ADC12 Aluminum Alloy During High-Speed Milling

Metals ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 1038
Author(s):  
Xinxin Meng ◽  
Youxi Lin ◽  
Shaowei Mi
Keyword(s):  
Aluminum Alloy ◽  
Flow Stress ◽  
Constitutive Equation ◽  
Constitutive Model ◽  
Strain Rate ◽  
Cutting Force ◽  
High Speed ◽  
Cutting Speed ◽  
Experimental Results ◽  
Element Analysis

Because of the massive work and high cost of milling experiments, finite element analysis technology (FEA) was used to analyze the milling process of ADC12 aluminum alloy. An improved Johnson–Cook (J–C) constitutive equation was fitted by a series of dynamic impact tests in different strain rates and temperatures. It found that the flow stress gradually increases as the strain rate rises, but it decreases as the test temperature rises. Compared with the J–C constitutive model, the predicted flow stress by the improved J–C constitutive model was closer to the experimental results when the strain rate was larger than 8000 s−1 and the temperature was higher than 300 °C. A two-dimensional cycloidal cutting simulation model was constructed based on the two J–C constitutive equations which was validated by milling experiments at different cutting speeds. The simulation results based on the improved J–C constitutive equation were closer to the experimental results and showed the cutting force first increased and then decreased, with cutting speed increasing, reaching a maximum at 600 m/min.

scholarly journals Cutting Force Model for Thermal-Assisted Machining of Tool Steel Based on the Taguchi Method

Metals ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 992 ◽  
Author(s):  
Mac Thi-Bich ◽  
Dinh Van-Chien ◽  
Banh Tien-Long ◽  
Nguyen Duc-Toan
Keyword(s):  
Taguchi Method ◽  
Cutting Force ◽  
Tool Steel ◽  
Feed Rate ◽  
Elevated Temperatures ◽  
Cutting Speed ◽  
Force Model ◽  
Cutting Force Model ◽  
Cutting Depth ◽  
Study Results

This paper investigates cutting force in thermal-assisted machining (TAM) by induction heating for SKD11 tool steel which is widely used in the mold industry. Experimental studies were first conducted at room and elevated temperatures to evaluate the effectiveness of the heating process on chip morphology and the cutting forces during the thermal-assisted machining and comparing with conventional machining method. The Taguchi method based on orthogonal array and analysis of variance ANOVA method was then used to design the number of experiments and evaluate the influence of cutting speed, feed rate, cutting depth, and elevated temperature on the cutting force. Study results showed a decrease in the cutting force in the TAM process. The optimal condition of parameters obtained for thermal-assisted machining were cutting speed 280 m/min, feed rate 230 mm/min, cutting depth 0.5 mm and temperature 400 °C. Finally, a proposed equation was established to determine the cutting force that was presented as a function of elevated temperatures when milling SKD11 material. A proposed cutting force model was compared, evaluated and confirmed to be in good agreement with experimental results.

Dimensional analysis and ANN simulation of chip-tool interface temperature during turning SS304

2021 ◽  
Vol 23 (4) ◽  
pp. 47-64
Author(s):  
Atul Kulkarni ◽  
◽  
Satish Chinchanikar ◽  
Vikas Sargade ◽  
◽  
...  
Keyword(s):  
Cutting Force ◽  
Dimensional Analysis ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Absolute Error ◽  
Interface Temperature ◽  
Machining Performance ◽  
Cutting Depth ◽  
Tialn Coating ◽  
Coated Carbide

Introduction. During machining, the resulting temperature has a wider and more critical impact on machining performance. During machining, the power consumption is mainly converted into heat near the cutting edge of the tool. Almost all the work performed during plastic deformation turns into heat. Researchers have put a lot of effort into measuring the cutting temperature during machining, as it significantly affects tool life and overall machining performance. The purpose of the work: to investigate the temperature of the chip-tool interface, taking into account the influence of cutting parameters and the type of tool coating during SS304 turning. The chip-tool interface temperature is measured by changing the cutting speed and feed with a constant cutting depth for uncoated and PVD single-layer TiAlN and multi-layer TiN/TiAlN coated carbide tools. In addition, an attempt is made to develop a model for predicting the temperature of the chip-tool interface using dimensional analysis and ANN simulating to better understand the process. The methods of investigation. Experiments are carried out with varying the cutting speed (140-260 m/min), feed (0.08-0.2 mm/rev) and a constant cutting depth of 1 mm. The chip-tool interface temperature is measured using the tool-work thermocouple principle. The Calibration Setup is designed to establish the relationship between the produced electromotive force (EMF) and the cutting temperature during machining. Statistical dimensional analysis and artificial neural network models have been developed to predict the temperature of the chip-tool interface. Tangential cutting force and chip attributes such as chip width and thickness are also measured depending on the cutting conditions, which is a prerequisite for dimensional analysis simulation. Results and Discussion. A tool made of TiAlN carbide with PVD coating had a lower temperature at the chip-tool interface than a tool with TiN/TiAlN coating. It has been observed that the chip-tool interface temperature increases prominently with the cutting speed, followed by the chip cross-sectional area and the specific cutting pressure. However, a lower cutting force was observed when using a carbide tool with a multi-layer TiN/TiAlN coating, which can be attributed to a lower coefficient of friction created by the front surface of this tool for flowing chips. On the other hand, the greatest cutting force was observed in uncoated carbide tools. It was noticed that the developed models allow predicting the temperature of the chip-tool interface with an absolute error of 5%. However, the lowest average absolute error of 0.78% was observed with the ANN model and, therefore, can be reliably used to predict the chip-tool interface temperature during SS304 turning.

scholarly journals Influence of belt speed, grit sizes and pressure on the sanding of Eucalyptus grandis wood

CERNE ◽  
2012 ◽  
Vol 18 (2) ◽  
pp. 231-237 ◽  
Author(s):  
Francisco Mateus Faria de Almeida Varasquim ◽  
Manoel Cléber de Sampaio Alves ◽  
Marcos Tadeu Tiburcio Gonçalves ◽  
Luis Fernando Frezzatti Santiago ◽  
Alexandre Jorge Duarte de Souza
Keyword(s):  
Surface Roughness ◽  
Power Consumption ◽  
Cutting Force ◽  
Eucalyptus Grandis ◽  
Cutting Speed ◽  
Wood Products ◽  
Grit Size ◽  
Belt Speed ◽  
Surface Finishes ◽  
Input Parameters

The sanding process is important to the quality of wood products. Sanding reduces imperfections in wood surfaces and it is important to the final product and application of paints or varnishes. There are few studies about sanding in the literature and finding out the relationship between the input parameters (i.e., species of wood, grit size, abrasive) on the output parameters (i.e., roughness, force, pressure) will help to improve this process. This study analyzed the influence of input parameters as belt speed (cutting speed), grit size and pressure on the output parameters as surface roughness, cutting force (sanding force) and power consumption on cross-grain sanding of Eucalyptus grandis wood. The tests were performed with 3 types of grit sizes (80, 100 and 120 grit), 3 belt speeds (10, 11 and 12 m/s) and 2 pressures (219.89 and 283.44 g/cm²). The surface roughness was analyzed based on roughness average (Ra). Sanding efforts were analyzed by cutting force and power consumption. It was found that the 100 grit size provided the lowest cutting force. It was observed that the belt speed, pressure and grit size influenced the surface roughness, cutting force and power consumption. The best surface finishes were obtained in tests with higher pressure.

scholarly journals Performance assessment of gaussian process regression to predict the bond strength of FRP sheets to concrete

2021 ◽  
Vol 72 (4) ◽  
pp. 411-422
Author(s):  
Nguyen Thuy Anh ◽  
Ly Hai Bang
Keyword(s):  
Bond Strength ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Absolute Error ◽  
Experimental Results ◽  
Coefficient Of Determination ◽  
Lap Shear ◽  
Lap Shear Test ◽  
Externally Bonded ◽  
Efficient Alternative

A Gaussian process regression (GPR) model for predicting the bond strength of FRP-to-concrete is proposed in this study. Published single-lap shear test specimens are used to predict the bond strength of externally bonded FRP systems adhered to concrete prisms. A database of 150 experimental results collected from published works is used for the training and testing phases of the proposed GPR model, containing 6 input parameters (width of concrete prism, concrete compressive strength, FRP thickness, FRP width, FRP length, and FRP modulus of elasticity). The output parameter of the prediction problem is bond strength. Three statistical indicators, namely the coefficient of determination, root mean square error (RMSE), and mean absolute error (MAE) are used to evaluate the performance of the proposed GPR model over 500 simulations. The results of this study indicate that the GPR provides an efficient alternative method for predicting the bond strength of FRP-to-concrete when compared to experimental results.

scholarly journals Modeling of the fracture energy on the finite element simulation in Ti6Al4V alloy machining

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Carolina Bermudo Gamboa ◽  
Tobias Andersson ◽  
Daniel Svensson ◽  
Francisco Javier Trujillo Vilches ◽  
Sergio Martín-Béjar ◽  
...  
Keyword(s):  
Finite Element ◽  
Fracture Energy ◽  
Cutting Force ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Ti6al4v Alloy ◽  
Experimental Results ◽  
Correct Selection ◽  
Generation Process ◽  
Energy Value

AbstractOne of the main problems that exists when working with Finite Element Methods (FEM) applied to machining processes is the lack of adequate experimental data for simulating the material properties. Moreover, for damage models based on fracture energy, the correct selection of the energy value is critical for the chip formation process. It is usually difficult to obtain the fracture energy values and requires complex tests. In this work, an analysis of the influence of this fracture energy on the cutting force and the chip generation process has been carried out for different sets of cutting parameters. The aim is to present an empirical relationship, that allows selecting the fracture energy based on the cutting force and cutting parameters. The work is based on a FEM model of an orthogonal turning process for Ti6Al4V alloy using Abaqus/Explicit and the fracture energy empirical relation. This work shows that it is necessary to adjust the fracture energy for each combination of cutting conditions, to be able to fit the experimental results. The cutting force and the chip geometry are analyzed, showing how the developed model adapts to the experimental results. It shows that as the cutting speed and the feed increase, the fracture energy value that best adapts to the model decreases. The evolution shows a more pronounced decrease related to the feed increment and high cutting speed.

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