On the Surface Roughness for the Dry Superfinishing Process of Carbide Inserts

2019 ◽  
Vol 34 ◽  
pp. 40-45
Author(s):  
Nicolae Craciunoiu ◽  
Daniela Florentina Tărâţă ◽  
Cosmin Miritoiu ◽  
Dumitru Panduru ◽  
Emil Nicusor Patru
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Process Parameters ◽  
Point Of View ◽  
Rake Face ◽  
Tool Edge ◽  
Carbide Inserts

The surface roughness of tool edge and surface can affect the friction, between the rake face and chips and also the cutting force and temperature. For this reasons, in order to assure a good quality, some experimental determinations, from point of view of rake face roughness, are presented in this paper. The results, as capture from a SEM microscope, and, graphically dependencies of roughness as function of process parameters, are presented.

Optimization of Process Parameters for Cutting Force for Aluminium 7010 in CNC End Milling Process

2018 ◽  
Vol 10 (6) ◽  
Author(s):  
M. Kishanth ◽  
P. Rajkamal ◽  
D. Karthikeyan ◽  
K. Anand
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Process Parameters ◽  
End Milling ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
Optimization Of Process Parameters ◽  
Cnc End Milling ◽  
End Milling Process

In this paper CNC end milling process have been optimized in cutting force and surface roughness based on the three process parameters (i.e.) speed, feed rate and depth of cut. Since the end milling process is used for abrading the wear caused is very high, in order to reduce the wear caused by high cutting force and to decrease the surface roughness, the optimization is much needed for this process. Especially for materials like aluminium 7010, this kind of study is important for further improvement in machining process and also it will improve the stability of the machine.

MULTI-PARAMETER ANN MODEL FOR FLAT-END MILLING

2008 ◽  
Vol 32 (3-4) ◽  
pp. 523-536 ◽  
Author(s):  
Hazim El Mounayri ◽  
M. Affan Badar ◽  
Gustavo A. Rengifo
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Cutting Force ◽  
Process Parameters ◽  
End Milling ◽  
Predictive Ability ◽  
Ann Model ◽  
Network Output ◽  
Input Variables ◽  
Artificial Neural Network Ann

The quality, productivity and safety of machining can be significantly improved through the optimization of cutting conditions. The first step in achieving such an objective is the development of accurate and reliable models for predicting the critical process parameters. In this paper, an innovative Artificial Neural Network (ANN) model that predicts both cutting force and surface roughness in end milling is developed and validated. A set of five input variables is selected to represent the machining conditions while twelve quantities representing two key process parameters, namely, cutting force and surface roughness, form the variables of the network output. Full factorial design of experiments is used to generate data for both training and validation. Successful training of the neural network is demonstrated through comparison of simulated and experimental results for four different output variables, namely cutting force, surface roughness, feed marks, and tooth passing frequency. The predictive ability of the model is verified experimentally by comparing simulated output variables with their experimental counterparts. A good agreement is observed.

Investigation of Surface Roughness and Resulting Cutting Force for Dry Turning Operation of Different Types of Material Using CVD Coating Carbide Tool

2020 ◽  
Vol 50 ◽  
pp. 162-176
Author(s):  
Sophal Hai ◽  
Sung Hoon Oh ◽  
Kyo Seok Park ◽  
Hyung Gon Shin
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Cutting Parameters ◽  
Experimental Conditions ◽  
Turning Operation ◽  
Dry Turning ◽  
Main Effect ◽  
Level 1 ◽  
Coated Carbide ◽  
Carbide Inserts

In this paper, dry turning operation was carried out on four types of workpiece materials SCM420, SCM420H, SCM440 and SM45C in order to review the effect of cutting parameters on surface roughness (Ra) and resulting cutting force (F). The turning operation was conducted on the NC lathe with the CVD coated carbide inserts. The analysis of variance (ANOVA), linear regression model and main effect plot for mean were employed to investigate the correlation between responses (Ra and F) and cutting parameters. The predictive equations showed a satisfactory correlation with high coefficients of determination (R2) from 80.76 to 98.89%. The lowest feed rate and highest spindle speed were applied to minimize surface roughness, but both were performed at level 1 to minimize the resulting cutting force. The optimal experimental conditions showed the brilliant results as the surface roughness 1 μm and resulting cutting force 39.92 N. The SCM440 steel indicated the best surface roughness responses followed by SCM420, SCM420H and SM45C steel. The SCM440 steel revealed the lowest resulting cutting force followed by SCM420, SM45C and SCM420H steel.

Effect of conical micro-grooved texture on tool–chip friction property and cutting performance of WC-TiC/Co cemented carbide tools

2018 ◽  
Vol 233 (5) ◽  
pp. 791-804
Author(s):  
Minghua Pang ◽  
Xiaojun Liu ◽  
Kun Liu
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Metal Cutting ◽  
Cutting Performance ◽  
Cemented Carbide ◽  
Cutting Fluid ◽  
Rake Face ◽  
Friction Property ◽  
Cemented Carbide Tools ◽  
Tool Cutting

Purpose This study aimed to clarify the influence mechanism of conical micro-grooved texture on the tool–chip friction property and cutting performance of WC-TiC/Co cemented carbide tools under flood lubrication conditions. Design/methodology/approach Conical micro-grooved texture was fabricated on the tool rake face using laser texture technology. Metal cutting tests were conducted on AISI 1045 steel with conventional and developed tools for various cutting speeds (80 m/min to 160 m/min) and conical angles of micro-grooved texture (2 ° to 5 °) under flood lubrication condition. The effect of conical micro-grooved texture on the tool cutting force, tool–chip friction coefficient, surface roughness of the machined workpiece, and wear of the tool rake face was determined. Findings Unlike the conventional tools, the conical micro-grooved tools successfully resulted in reductions in metal cutting force, tool–chip frictional coefficient, surface roughness of the machined workpiece, and wear of the tool rake face. These reductions were more noticeable than those of conventional tools with increases in the cutting speed and conical angle of the micro-grooved texture. Detailed research indicated that conical micro-grooved channel exhibits a directional motion characteristic of liquid, which accelerated the infiltration of cutting fluid at the tool–chip interface. Substantial cutting fluid was supplied and stored at the tool–chip interface for the conical micro-grooved tools. Therefore, the conical micro-grooved texture on the tool rake face showed evident advantages in improving tool–chip friction and tool cutting performance. Originality/value The main contribution of this study is proposing a new conical micro-grooved texture on the tool rake face, which improved tool–chip friction and tool cutting performance.

High-pressure jet-assisted turning of AISI 304: Experimental and multi-objective optimization approach

2017 ◽  
Vol 232 (6) ◽  
pp. 734-750 ◽  
Author(s):  
Sayed E Mirmohammadsadeghi ◽  
H Amirabadi
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
High Pressure ◽  
Cutting Force ◽  
Process Parameters ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Aisi 304 ◽  
Multi Objective ◽  
304 Austenitic Stainless Steel

High-pressure jet-assisted turning is an effective method to decrease the cutting force and surface roughness. Efficiency of this process is related to application of proper jet pressure proportional to other process parameters. In this research, experiments were conducted for high-pressure jet-assisted turning in finishing AISI 304 austenitic stainless steel, based on response surface method. Against the expectations, the maximum jet pressure could not lead to the most efficient results, which means that applying high-pressure jet-assisted turning without considering optimal process parameters will diminish the improving effects of high-pressure jet assistance. For this purpose, two artificial neural networks were trained by genetic algorithm to model the surface roughness and cutting force based on the process parameters. Ultimately, nondominated sorting genetic algorithm was implemented for multi-objective optimization of process. Results demonstrated that the employed method provides an effective approach that indicates optimized range of process parameters.

scholarly journals Prediction of surface roughness and cutting force under MQL turning of AISI 4340 with nano fluid by using response surface methodology

2018 ◽  
Vol 5 ◽  
pp. 5 ◽  
Author(s):  
Pralhad B. Patole ◽  
Vivek V. Kulkarni
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Cutting Force ◽  
Response Surface ◽  
Process Parameters ◽  
Feed Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Nano Fluid ◽  
Aisi 4340

This paper presents an investigation into the minimum quantity lubrication mode with nano fluid during turning of alloy steel AISI 4340 work piece material with the objective of experimental model in order to predict surface roughness and cutting force and analyze effect of process parameters on machinability. Full factorial design matrix was used for experimental plan. According to design of experiment surface roughness and cutting force were measured. The relationship between the response variables and the process parameters is determined through the response surface methodology, using a quadratic regression model. Results show how much surface roughness is mainly influenced by feed rate and cutting speed. The depth of cut exhibits maximum influence on cutting force components as compared to the feed rate and cutting speed. The values predicted from the model and experimental values are very close to each other.

scholarly journals Optimization of process parameters for turning of titanium alloy (Grade II) in MQL environment using multi-CI algorithm

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Apoorva Shastri ◽  
Aniket Nargundkar ◽  
Anand J. Kulkarni ◽  
Luigi Benedicenti
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Tool Wear ◽  
Cutting Force ◽  
Process Parameters ◽  
Optimization Algorithm ◽  
Materials Science ◽  
Industrial Applications ◽  
Contact Length ◽  
Grade Ii

AbstractThe advancement of materials science during the last few decades has led to the development of many hard-to-machine materials, such as titanium, stainless steel, high-strength temperature-resistant alloys, ceramics, refractories, fibre-reinforced composites, and superalloys. Titanium is a prominent material and widely used for several industrial applications. However, it has poor machinability and hence efficient machining is critical. Machining of titanium alloy (Grade II) in minimum quantity lubrication (MQL) environment is one of the recent approaches towards sustainable manufacturing. This problem has been solved using various approaches such as experimental investigation, desirability, and with optimization algorithms. In the group of socio-inspired optimization algorithm, an artificial intelligence (AI)-based methodology referred to as Cohort Intelligence (CI) has been developed. In this paper, CI algorithm and Multi-CI algorithm have been applied for optimizing process parameters associated with turning of titanium alloy (Grade II) in MQL environment. The performance of these algorithms is exceedingly better as compared with particle swarm optimization algorithm, experimental and desirability approaches. The analysis regarding the convergence and run time of all the algorithms is also discussed. It is important to mention that for turning of titanium alloy in MQL environment, Multi-CI achieved 8% minimization of cutting force, 42% minimization of tool wear, 38% minimization of tool-chip contact length, and 15% minimization of surface roughness when compared with PSO. For desirability and experimental approaches, 12% and 8% minimization of cutting force, 42% and 47% minimization of tool wear, 53% and 40% minimization of tool-chip contact length, and 15% and 20% minimization of surface roughness were attained, respectively.

scholarly journals Fuzzy-MOORA Based Optimization of Machining Parameters for Machinability Enhancement of Titanium

2021 ◽  
Vol 8 (2) ◽  
pp. 189-198
Author(s):  
Durwesh Jhodkar ◽  
Akhtar Khan ◽  
Kapil Gupta
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Process Parameters ◽  
Flank Wear ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Tool Flank Wear ◽  
Moora Method ◽  
Fuzzy Moora

The aim of this study is to determine the optimal combination of process parameters when machining commercially pure titanium grade 2. The unification of Multi objective optimization based on ratio analysis (MOORA) and fuzzy approach has applied to optimize the process parameters. Three process parameters i.e. cutting speed, tool overhang, and microhardness have been varied at three levels each and a total of twenty seven experiments have been conducted based on Taguchi’s L27 design of experiment technique. Cutting force, tool flank wear, and average surface roughness have been considered a machinability indicators to measure the process performance. Feed rate and depth of cut have been kept constant. Successful optimization is done and results show that machining titanium at higher cutting speed (140 m/min) and higher tool overhang length (65 mm) with medium hardness (1934 HV) results in lower cutting force, tool flank wear, and surface roughness. Outcomes of the present work reveal that the hybrid fuzzy-MOORA method is convincing enough to obtain the best process parameter combination for the best machinability while machining titanium type difficult-to-machine materials.

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