Study of Factors Effecting Surface Roughness and Tool Tip Temperature in Step Turning Using Factorial Technique

2015 ◽  
Vol 15 (4) ◽  
pp. 319-326
Author(s):  
Kondapalli Siva Prasad
Keyword(s):  
Mathematical Model ◽  
Surface Roughness ◽  
Regression Analysis ◽  
Process Parameters ◽  
Depth Of Cut ◽  
Turning Process ◽  
Nose Radius ◽  
Five Factors ◽  
The Mathematical Model ◽  
Anova Technique

AbstractThe paper focuses on the effect of various process parameters like spindle speed, feed, depth of cut, nose radius and machining condition on the Tool tip temperature and surface roughness in step turning process is investigated by using Factorial Technique. Five factors- Two levels are used and total 32 experiments are performed. The coefficients are calculated by using regression analysis and the model is constructed. The adequacy of the developed model is checked using Analysis of Variance (ANOVA) technique. By using the mathematical model the main and interaction effect of various process parameters on tool tip temperature and surface roughness are studied.

scholarly journals Taguchi Optimisation In Machining EN18 Using Coated Tool Under Dry and MQL Condition

2020 ◽  
Vol 184 ◽  
pp. 01013
Author(s):  
Kosaraju Satynarayana ◽  
Are Swathi ◽  
Kesari Neeraja ◽  
Madipali Samaikhya ◽  
Kumkuma Rajkiran
Keyword(s):  
Mathematical Model ◽  
Surface Roughness ◽  
Production Systems ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Turning Process ◽  
Anova Analysis ◽  
Coated Tool ◽  
The Mathematical Model ◽  
Minimum Quality

Turning is one of the initial basic machining operation that prevails in assembly and production process. Modern techniques have been practices in rapid and eco-friendly production systems. Present study deals with the investigation of turning process on EN 18 steel which is been shown its existence in automobiles industries. Turning operation was performed using a coated tool insert with varying cutting speed (100, 125 and 150 mm/min), feed rate (0.05, 0.5, 0.15 mm/rev) and depth of cut (0.4, 0.8, 1.2 mm) at both dry and MQL conditions. The results obtained was compared to optimize the effect of minimum quality lubrication on surface roughness. Experimentally it was observed that speed of 100 m/min with combination of feed of 0.05 mm/rev and 0.4 mm depth of cut was found to be optimized for surface roughness in both the cases. The mathematical model generated for surface roughness and MRR for both dry and MQL turning models having better regression fit as it closer to 100. From ANOVA analysis feed was proved to be the highest contributing factor for surface roughness and for MRR speed is the most significant factor for both dry and MQL turning

Evaluation of Process Parameters in Machining of AA6063 Alloy

2018 ◽  
Vol 1148 ◽  
pp. 109-114
Author(s):  
M. Balaji ◽  
C.H. Nagaraju ◽  
V.U.S. Vara Prasad ◽  
R. Kalyani ◽  
B. Avinash
Keyword(s):  
Surface Roughness ◽  
Regression Analysis ◽  
Process Parameters ◽  
Single Channel ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Roughness Parameters ◽  
Surface Roughness Parameters ◽  
Aa6063 Alloy

The main aim of this work is to analyse the significance of cutting parameters on surface roughness and spindle vibrations while machining the AA6063 alloy. The turning experiments were carried out on a CNC lathe with a constant spindle speed of 1000rpm using carbide tool inserts coated with Tic. The cutting speed, feed rate and depth of cut are chosen as process parameters whose values are varied in between 73.51m/min to 94.24m/min, 0.02 to 0.04 mm/rev and 0.25 to 0.45 mm respectively. For each experiment, the surface roughness parameters and the amplitude plots have been noted for analysis. The output data include surface roughness parameters (Ra,Rq,Rz) measured using Talysurf and vibration parameter as vibration amplitude (mm/sec) at the front end of the spindle in transverse direction using single channel spectrum analyzer (FFT).With the collected data Regression analysis is also performed for finding the optimum parameters. The results show that significant variation of surface irregularities and vibration amplitudes were observed with cutting speed and feed. The optimum cutting speed and feed from the regression analysis were 77.0697m/min and 0.0253mm/rev. for the minimum output parameters. No significant effect of depth of cut on output parameters is identified.

scholarly journals Developing an Analytical Model and Computing Tool for Optimizing Lapping Operations of Flat Objects Made of Alloyed Steels

Materials ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1343 ◽  
Author(s):  
Tudor Deaconescu ◽  
Andrea Deaconescu
Keyword(s):  
Mathematical Model ◽  
Surface Roughness ◽  
Material Properties ◽  
Depth Of Cut ◽  
Operation Optimization ◽  
Abrasive Grains ◽  
Finishing Process ◽  
Real World Applications ◽  
The Mathematical Model

Lapping is a finishing process where loose abrasive grains contained in a slurry are pressed against a workpiece to reduce its surface roughness. To perform a lapping operation, the user needs to set the values of the respective lapping conditions (e.g., pressure, depth of cut, the rotational speed of the pressing lap plate, and alike) based on some material properties of the workpiece, abrasive grains, and slurry, as well as on the desired surface roughness. Therefore, a mathematical model is needed that establishes the relationships among the abovementioned parameters. The mathematical model can be used to develop a lapping operation optimization system, as well. To this date, such a model and system are not available mainly because the relationships among lapping conditions, material properties of abrasive grains and slurry, and surface roughness are difficult to establish. This study solves this problem. It presents a mathematical model establishing the required relationships. It also presents a system developed based on the mathematical model. In addition, the efficacy of the system is also shown using a case study. This study thus helps systematize lapping operations in regard to real-world applications.

scholarly journals Development of surface roughness model in turning process of 3X13 steel using TiAlN coated carbide insert

2021 ◽  
pp. 113-124
Author(s):  
Nhu-Tung Nguyen ◽  
Do Duc Trung
Keyword(s):  
Surface Roughness ◽  
Machining Process ◽  
Quadratic Model ◽  
Depth Of Cut ◽  
Turning Process ◽  
Nose Radius ◽  
Machining Processes ◽  
Roughness Model ◽  
Input Parameters ◽  
Coated Carbide

Surface roughness that is one of the most important parameters is used to evaluate the quality of a machining process. Improving the accuracy of the surface roughness model will contribute to ensure an accurate assessment of the machining quality. This study aims to improve the accuracy of the surface roughness model in a machnining process. In this study, Johnson and Box-Cox transformations were successfully applied to improve the accuracy of surface roughness model when turning 3X13 steel using TiAlN insert. Four input parameters that were used in experimental process were cutting velocity, feed rate, depth of cut, and insert-nose radius. The experimental matrix was designed using Central Composite Design (CCD) with 29 experiments. By analyzing the experimental data, the influence of input parameters on surface roughness was investigated. A quadratic model was built to explain the relationship of surface roughness and the input parameters. Box-Cox and Johnson transformations were applied to develop two new models of surface roughness. The accuracy of three surface roughness models showed that the surface roughness model using Johnson transformation had the highest accuracy. The second one model of surface roughness is the model using Box-Cox transformation. And surface roughness model without transformation had the smallest accuracy. Using the Johnson transformation, the determination coefficient of surface roughness model increased from 80.43 % to 84.09 %, and mean absolute error reduced from 19.94 % to 16.64 %. Johnson and Box-Cox transformations could be applied to improve the acuaracy of the surface roughness prediction in turning process of 3X13 steel and can be extended with other materials and other machining processes

Experimental investigation of machinability characteristics in Al-TiB2 metal matrix composite (MMC) based on the Taguchi method with fuzzy logics

2016 ◽  
Vol 12 (1) ◽  
pp. 177-193 ◽  
Author(s):  
M.P. Jenarthanan ◽  
A. Ram Prakash ◽  
R. Jeyapaul
Keyword(s):  
Mathematical Model ◽  
Surface Roughness ◽  
Taguchi Method ◽  
Metal Matrix ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Fuzzy Logics ◽  
Content Type ◽  
Five Factors

Purpose – The purpose of this paper is to develop a mathematical model for metal removal rate and surface roughness through Taguchi method and analyse the influence of the individual input machining parameters (cutting speed, feed rate, helix angle, depth of cut and wt% on the responses in milling of aluminium-titanium diboride metal matrix composite (MMC) with solid carbide end mill cutter coated with nano-crystals. Design/methodology/approach – Taguchi OA is used to optimise the material removal rate (MRR) and Surface Roughness by developing a mathematical model. End Milling is used to create slots by combining various input parameters. Five factors, three-level Taguchi method is employed to carry out the experimental investigation. Fuzzy logic is used to find the optimal cutting factors for surface roughness (Ra) and MRR. The factors considered were weight percentage of TiB2, cutting speed, depth of cut and feed rate. The plan for the experiments and analysis was based on the Taguchi L27 orthogonal array with five factors and three levels. MINITAB 17 software is used for regression, S/N ratio and analysis of variance. MATLAB 7.10.0 is used to perform the fuzzy logics systems. Findings – Using fuzzy logics, multi-response performance index is generated, with which the authors can identify the correct combination of input parameters to get higher MRR and lower surface roughness value with the chosen range with 95 per cent confidence intervals. Using such a model, remarkable savings in time and cost can be obtained. Originality/value – Machinability characteristics in Al-TiB2 MMC based on the Taguchi method with fuzzy logic has not been analysed previously.

Investigating and Modeling the Machinability of Al 6061 Using Response Surface Methodology

2015 ◽  
Vol 761 ◽  
pp. 267-272
Author(s):  
Basim A. Khidhir ◽  
Ayad F. Shahab ◽  
Sadiq E. Abdullah ◽  
Barzan A. Saeed
Keyword(s):  
Mathematical Model ◽  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Feed Rate ◽  
Cutting Speed ◽  
Effect Of Temperature ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Turning Process

Decreasing the effect of temperature, surface roughness and vibration amplitude during turning process will improve machinability. Mathematical model has been developed to predict responses of the surface roughness, temperature and vibration in relation to machining parameters such as the cutting speed, feed rate, and depth of cut. The Box-Behnken First order and second-order response surface methodology was employed to create a mathematical model, and the adequacy of the model was verified using analysis of variance. The experiments were conducted on aluminium 6061 by cemented carbide. The direct and interaction effect of the machining parameters with responses were analyzed. It was found that the feed rate, cutting speed, and depth of cut played a major role on the responses, such as the surface roughness and temperature when machining mild steel AISI 1018. This analysis helped to select the process parameters to improve machinability, which reduces cost and time of the turning process.

scholarly journals Improving Surface Roughness of Burnished Components using Abrasive Particles

2018 ◽  
Vol 15 (3) ◽  
pp. 5592-5606
Author(s):  
Pavana Kumara ◽  
G. K. Purohit
Keyword(s):  
Mathematical Model ◽  
Surface Roughness ◽  
Silicon Carbide ◽  
Process Parameters ◽  
Machined Surface ◽  
Abrasive Particles ◽  
Burnishing Process ◽  
Number Of Passes ◽  
The Mathematical Model ◽  
Full Factorial

Roller burnishing process was carried out on free cutting brass materials in the presence of fine silicon carbide abrasives in the form of paste on a pre-machined surface. The results of ‘without-paste’ burnishing (plain burnishing, PB) and ‘with-paste’ burnishing (abrasive assisted burnishing, AAB) processes are compared to examine the effect of abrasive particles in the burnishing process. A 24 full factorial design is adopted to develop the mathematical model for surface roughness regarding four process parameters like burnishing force, burnishing speed, burnishing feed and number of passes for both the cases, i.e. PB and AAB. Analysis of variance (ANOVA) was carried out to find the effect of process parameters and to check the adequacy of the models. The results show that the parameters have a significant effect on the response in PB to improve the surface roughness by 75 % than the turned components. Whereas in AAB, fine abrasive particles as a single entity controlling the response and making other parameter effects as non-significant. Surface roughness further improved by 15 % in AAB process.

Surface roughness of the Ti-6Al-4V ELI titanium alloy after the turning process

2019 ◽  
Vol 2 (98) ◽  
pp. 74-80
Author(s):  
R. Rosik ◽  
N. Kępczak ◽  
M. Sikora ◽  
B. Witkowski ◽  
R. Wójcik ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Surface Roughness ◽  
Titanium Alloy ◽  
Multiple Regression ◽  
Roughness Parameter ◽  
Regression Function ◽  
Turning Process ◽  
Longitudinal Turning ◽  
Machining Parameter ◽  
The Mathematical Model

Purpose: The purpose of this article is discussing the methods of determining the surface roughness of the Ti-6Al-4V ELI titanium alloy obtained after longitudinal turning. The method of determining the mathematical model used for determining the Rz roughness parameter and then the results obtained were compared with values measured and calculated on the basis of equations available in the literature. Design/methodology/approach: The mathematical model in the form of multiple regression function of exponential polynomial was determined using the algorithm of the acceptance and rejection method. The data for calculations was obtained by measuring the surface roughness after turning with different machining parameter values. Findings: A mathematical model was elaborated in the form of a multiple regression function, enabling calculation of the Rz parameter describing the Ti-6Al-4V ELI titanium alloy surface roughness after longitudinal turning. The verification of the dependence obtained confirmed its accuracy. Research limitations/implications: Further research should encompass other values of machining plate geometry, as well as other types of cooling and lubricating fluids and method of applying them. Practical implications: The mathematical model can be helpful when choosing the conditions in which the turning process will be carried out. It also constitutes a basis for further optimisation of that process. Originality/value: The results of this research are a novelty on a worldwide scale. No research of this type has been conducted with regard to analyses and optimisation of longitudinal turning of the Ti-6Al-4V ELI titanium alloy.

Experimental Optimization of Process Parameters for Turning TC11 Titanium Alloy Using Taguchi Methodology Design

2016 ◽  
Vol 693 ◽  
pp. 1009-1014 ◽  
Author(s):  
Su Lin Chen ◽  
Bin Shen ◽  
Fang Hong Sun
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Cutting Force ◽  
Process Parameters ◽  
Feed Rate ◽  
Cutting Temperature ◽  
Cvd Diamond ◽  
Depth Of Cut ◽  
Taguchi Methodology ◽  
Turning Process

This paper presents a study of the influence of cutting conditions (cutting velocity, feed, cutting depth and lubrication) on turning TC11 (Ti-6.5Al-3.5Mo-1.5Zr-0.3Si) titanium alloy. Taguchi methodology design was adopt for carrying out experiments. Turning process parameters such as cutting speed, feed rate and depth of cut were varied to study their effect on process responses such as cutting force (Ft), surface roughness (Ra) and temperature on cutting zones (T). Minimum quantity lubrication (MQL) technology was adopt to increase the lubricating and cooling effect. Meanwhile, CVD diamond coating was deposited on the cemented carbide insert to reduce its friction with workpiece and increase its wear resistance. From the analysis of orthogonal tests, depth of cut contributes the most for the main cutting force and cutting temperature, while feed rate had the most significant effect on surface roughness on the workpiece. MQL can reduce the cutting temperature at the cutting zones, especially for the uncoated cutting inserts whose temperature decreases by an average of 60~80°C. The cutting force, surface roughness and cutting temperature of CVD diamond coated inserts were all higher than those of uncoated tools, especially with MQL lubrication. Considering the cutting efficiency and cost, the optimal parameters in the turning process of TC11 for minimizing the cutting force, surface roughness and cutting temperature are obtained as Vc=115m/min, f=0.08mm, ap=0.5mm under MQL lubricating with uncoated cemented carbide as the cutting tool.

scholarly journals Application of TOPSIS for multi response optimization of Process Parameters in dry hard turning of AISI 52100 steel

2021 ◽  
Vol 13 (1) ◽  
pp. 211-224
Author(s):  
P. UMAMAHESWARRAO ◽  
D. RANGARAJU ◽  
K. N. S. SUMAN ◽  
B. RAVISANKAR
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Hard Turning ◽  
Rake Angle ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Workpiece Surface ◽  
Aisi 52100 ◽  
Machining Force ◽  
Aisi 52100 Steel

In the present work by employing the Technique for order of preference by similarity to ideal solution (TOPSIS) machining parameters optimization is performed with polycrystalline cubic boron nitride (PCBN) tools while AISI 52100 steel hard turning (HT). Based on the CCD of RSM, 32 experimental runs were performed by varying cutting speed, feed, depth of cut, nose radius, and negative rake angle to identify the optimal level of the process parameters. In this study, the multiple performance characteristics measured are machining force, surface roughness, and workpiece surface temperature. To ascertain the impact of cutting parameters on responses, Analysis of Variance (ANOVA) was deployed. An optimum combination of input process parameters for the multiple performance characteristics should be as follows: speed 200 rpm, feed 0.1 mm/rev, depth of cut 0.8 mm, nose radius 1.2 mm, and negative rake angle 45º leading to the value of optimum response variables machining force 561.163 N, Surface roughness 0.507μm and workpiece surface temperature 84.38°C.

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