Modeling and optimization of hard turning of X38CrMoV5-1 steel with CBN tool: Machining parameters effects on flank wear and surface roughness

2011 ◽  
Vol 25 (11) ◽  
pp. 2843-2851 ◽  
Author(s):  
Hamdi Aouici ◽  
Mohamed Athmane Yallese ◽  
Brahim Fnides ◽  
Kamel Chaoui ◽  
Tarek Mabrouki
Keyword(s):  
Surface Roughness ◽  
Hard Turning ◽  
Flank Wear ◽  
Machining Parameters ◽  
Modeling And Optimization ◽  
Cbn Tool

Machinability Investigation of HSLA Steel in Hard Turning with Coated Ceramic Tool: Assessment, Modeling, Optimization and Economic Aspects

2019 ◽  
Vol 18 (04) ◽  
pp. 625-655 ◽  
Author(s):  
Asutosh Panda ◽  
Sudhansu Ranjan Das ◽  
Debabrata Dhupal
Keyword(s):  
Surface Roughness ◽  
Tool Life ◽  
Hard Turning ◽  
Flank Wear ◽  
Desirability Function ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Ceramic Tool ◽  
Life Estimation

The present study addresses the machinability investigation in finish dry hard turning of high strength low alloy steel with coated ceramic tool by considering cutting speed, feed and depth of cut as machining parameters. The technological parameters like surface roughness, flank wear, chip morphology and economical feasibility have been considered to investigate the machinability performances. Twenty seven set of trials according to full factorial design of experiments are performed and analysis of variance, multiple regression method, Taguchi method, desirability function approach and finally Gilbert’s approach are subsequently applied for parametric influence study, mathematical modeling, multi-response optimization, tool life estimation and economic analysis. Results indicated that feed and cutting speed are the most significant controlled as well as dominant factors for hard turning operation if the minimization of the machined surface roughness and tool flank wear is considered. Abrasions, adhesion followed by plastic deformation have been observed to be the principal wear mechanism for tool life estimation and observed tool life for coated ceramic insert is 47[Formula: see text]min under optimum cutting conditions. The total machining cost per part is ensued to be lower ($0.29 only) as a consequence of higher tool life, reduction in downtime and enhancement in savings, which finds economical benefits in hard turning. The current work demonstrates the substitution of conventional, expensive and slow cylindrical grinding process, and proposes the most expensive CBN tool alternative using coated ceramic tools in hard turning process considering techno-economical and ecological aspects.

Prediction models for on-line cutting tool and machined surface condition monitoring during hard turning considering vibration signal

2020 ◽  
Vol 21 (5) ◽  
pp. 520
Author(s):  
Amlana Panda ◽  
Ashok Kumar Sahoo ◽  
Isham Panigrahi ◽  
Arun Kumar Rout
Keyword(s):  
Surface Roughness ◽  
Hard Turning ◽  
Flank Wear ◽  
Pearson Correlation ◽  
Cutting Speed ◽  
Vibration Signal ◽  
Machining Parameters ◽  
Pearson Correlation Coefficient ◽  
Vibration Signals ◽  
Acceleration Amplitude

Turning of hardened steel is an immense issue of interest concerning with machining technology and scientific research. A strategy to analyze vibration signals and its correlation on surface roughness and tool wear has not attracted much breakthrough in research so far in hard machining. Therefore, tool condition monitoring (TCM) study will be definitely worthwhile for the effective application in hard part turning. The current study examines about the online prediction of flank wear and surface roughness monitoring during dry hard turning of AISI 52100 steel (55 ± 1 HRC) utilizing MTCVD multilayer coated carbide insert (TiN/TiCN/Al2O3) considering machining parameters and vibration signals through development of prediction model (MLR and MQR) after studying the Pearson correlation coefficient and test for its accuracy. Pearson correlation coefficient for feed on flank wear is utmost pursued by acceleration amplitude of vibration (Vy) in radial direction, depth of cut and cutting speed. Similarly, acceleration amplitude of vibration followed by cutting speed and feed has strong correlation with surface roughness. MQR model predicts well for responses as percentage of error is quite less and cutting speed is obtained to be the most important parameter for vibration signal. Multiple quadratic regression (MQR) models are observed to be noteworthy, effective and adequate to predict response outputs with regards to the combined effect of machining parameters and vibration signals online. A corrective measure can safely be taken with reasonable degree of accuracy during hard turning.

scholarly journals Analysis of Surface Roughness and Flank Wear Using the Taguchi Method in Milling of NiTi Shape Memory Alloy with Uncoated Tools

Coatings ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1259
Author(s):  
Emre Altas ◽  
Hasan Gokkaya ◽  
Meltem Altin Karatas ◽  
Dervis Ozkan
Keyword(s):  
Surface Roughness ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Flank Wear ◽  
Cutting Tools ◽  
Dominant Factor ◽  
Niti Shape Memory Alloy ◽  
Machining Parameters ◽  
Nose Radius ◽  
Dry Cutting

The aim of this study was to optimize machining parameters to obtain the smallest average surface roughness (Ra) and flank wear (Vb) values as a result of the surface milling of a nickel-titanium (NiTi) shape memory alloy (SMA) with uncoated cutting tools with different nose radius (rε) under dry cutting conditions. Tungsten carbide cutting tools with different rε (0.4 mm and 0.8 mm) were used in milling operations. The milling process was performed as lateral/surface cutting at three different cutting speeds (Vc) (20, 35 and 50 m/min), feed rates (fz) (0.03, 0.07 and 0.14 mm/tooth) and a constant axial cutting depth (0.7 mm). The effects of machining parameters in milling experiments were investigated based on the Taguchi L18 (21 × 32) orthogonal sequence, and the data obtained were analyzed using the Minitab 17 software. To determine the effects of processing parameters on Ra and Vb, analysis of variance (ANOVA) was used. The analysis results reveal that the dominant factor affecting the Ra is the cutting tool rε, while the main factor affecting Vb is the fz. Since the predicted values and measured values are very close to each other, it can be said that optimization is correct according to the validation test results.

Machining characteristics of Inconel 718 under several cutting conditions based on Taguchi method

2012 ◽  
Vol 227 (9) ◽  
pp. 1889-1897 ◽  
Author(s):  
R Thirumalai ◽  
JS Senthilkumaar ◽  
P Selvarani ◽  
S Ramesh
Keyword(s):  
Surface Roughness ◽  
Inconel 718 ◽  
Flank Wear ◽  
Optimization Technique ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Machining Parameters ◽  
Dry Machining ◽  
Noise Factors

Extensive researchers have conducted several experiments in the past for selecting the optimum parameters in machining nickel based alloy – Inconel 718. These experiments conducted so far are dealt with dry machining and flooded coolant machining of nickel alloy Inconel 718. In this research study, the usage of refrigerated coolant is also dealt with and it is compared with dry machining and flooded coolant machining. Cutting speed, feed and depth of cut are considered as the machining parameters. The effectiveness of the refrigerated coolant in machining the heat resistant super alloy material Inconel 718 with respect to these machining parameters are described in this article. The machinability studies parameters were generated with surface roughness and flank wear. The performance of uncoated carbide cutting tool was investigated at various cutting condition under dry, flooded coolant and refrigerated coolant machining. The relationship between the machining parameters and the performance measures were established and using analysis of variance significant machining parameters determined. This article made an attempt to Taguchi optimization technique to study the machinability performances of Inconel 718. Taguchi approach is an efficient and effective experimental method in which a response variable can be optimized, given various control and noise factors, using fewer experiments than a factorial design. Taguchi’s optimization analysis indicates that the factors level, its significance to influence the surface roughness and flank wear for the machining processes. Confirmation tests were conducted at an optimal condition to make a comparison between the experimental results foreseen from the mentioned correlations.

Analysis and prediction of tool wear, surface roughness and cutting forces in hard turning with CBN tool

2012 ◽  
Vol 26 (11) ◽  
pp. 3605-3616 ◽  
Author(s):  
Samir Khamel ◽  
Nouredine Ouelaa ◽  
Khaider Bouacha
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Wear Surface ◽  
Cutting Forces ◽  
Hard Turning ◽  
Cbn Tool

scholarly journals Analysis and Optimization of Ceramic Cutting Tool In Hard Turning of EN-31 Using Factorial Design

2012 ◽  
pp. 53-58
Author(s):  
Chetan Darshan ◽  
Lakhvir Singh ◽  
APS Sethi
Keyword(s):  
Surface Roughness ◽  
Surface Quality ◽  
Analysis Of Variance ◽  
Feed Rate ◽  
Hard Turning ◽  
Flank Wear ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Good Surface ◽  
En 31

Manufacturers around the globe persistently looking for the cheapest and quality manufactured machined components to compete in the market. Good surface quality is desired for the proper functioning of the produced parts. The surface quality is influenced by cutting speed, feed rate and depth of cut and many other parameters. In the present study attempt has been made to evaluate the performance of ceramic inserts during hard turning of EN-31 steel. The analysis of variance is applied to study the effect of cutting speed, feed rate and depth of cut on Flank wear and surface roughness. Model is found to be statically significant using regression model, while feed and depth of cut are the factor affecting Flank wear and feed is dominating factors for surface roughness. The analysis of variance was used to analyze the input parameters and there interactions during machining. The developed model predicted response factor at 95% confidence level.

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