Use of Response Surface Methodology to Select a Cutting Tool to Maximize Profit

1976 ◽  
Vol 98 (1) ◽  
pp. 63-65 ◽  
Author(s):  
W. W. Claycombe ◽  
W. G. Sullivan
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Cutting Tool ◽  
Cutting Speed ◽  
Operating Conditions ◽  
Depth Of Cut ◽  
Maximum Profit ◽  
Decision Variables ◽  
Conventional Tests ◽  
Economic Decision Theory

A technique is presented which uses Response Surface Methodology to select a cutting tool in order to maximize profit. Different tools are analyzed to determine what combinations of cutting speed, feed, and depth of cut will give maximum profit for each kind of tool. Only then may different tools be compared. There is a possibility that the tool which is capable of yielding maximum profit may be most economical at operating conditions which are somewhat anomolous. With conventional tests a suboptimal tool may be selected, or the best tool may be used at suboptimal operating conditions. The contribution of this article is the expression of profit as a function of the direct physical decision variables and the subsequent optimization. The use of these techniques with economic decision theory is unique.

Modeling and Optimization of Vibration Amplitude in Turning of Ti-6Al-4V Using Response Surface Methodology

2012 ◽  
Vol 217-219 ◽  
pp. 1567-1570
Author(s):  
A.K.M. Nurul Amin ◽  
Muammer Din Arif ◽  
Syidatul Akma Sulaiman
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Vibration Amplitude ◽  
Desirability Function ◽  
Cutting Speed ◽  
Vibration Sensor ◽  
Experimental Investigations ◽  
Depth Of Cut ◽  
Work Piece ◽  
Inferior Surface

Chatter is detrimental to turning operations and leads to inferior surface topography, reduced productivity, dimensional accuracy, and shortened tool life. Avoidance of chatter has mostly been through reliance on heuristics such as: limiting material removal rates or selecting low spindle speeds and shallow depth of cuts. But, modern industries demand increased output and not steady operational limits. Various research efforts have therefore focused on developing mathematical models for chatter formation. However, as yet there is no existent model that meets all experimental verification. This research employed a novel technique based on the synergy of statistical modeling and experimental investigations in order to develop an effective empirical mathematical model for chatter amplitude and to subsequently find optimal machining conditions. Ti-6Al-4V, Titanium alloy, was used as the work-piece due to its increased popularity in applications related to aerospace, automotive, nuclear, medical, marine etc. A sequence of 15 experimental runs was conducted based on a small Central Composite Design (CCD) model in Response Surface Methodology (RSM). The primary (independent) parameters were: cutting speed, feed, and depth of cut. The tool overhang was kept constant at 70 mm. An engine lathe (Harrison M390) was employed for turning purposes. The data acquisition system comprised a vibration sensor (accelerometer) and a signal conditioning unit. The resultant vibrations were analyzed using the DASYLab 5.6 software. The best model was found to be quadratic which had a confidence level of 95% (ANOVA) and insignificant Lack of Fit (LOF) in Fit and Summary analyses. Desirability Function (DF) approach predicted minimum vibration amplitude of 0.0276 Volts and overlay plots identified two preferred machining regimes for optimal vibration amplitude.

Life Prediction of Cutting Tool by the Workpiece Cutting Condition

2011 ◽  
Vol 223 ◽  
pp. 554-563 ◽  
Author(s):  
Noemia Gomes de Mattos de Mesquita ◽  
José Eduardo Ferreira de Oliveira ◽  
Arimatea Quaresma Ferraz
Keyword(s):  
Cutting Tool ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Operating Conditions ◽  
Production Costs ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Workpiece Material ◽  
The Cost

Stops to exchange cutting tool, to set up again the tool in a turning operation with CNC or to measure the workpiece dimensions have direct influence on production. The premature removal of the cutting tool results in high cost of machining, since the parcel relating to the cost of the cutting tool increases. On the other hand the late exchange of cutting tool also increases the cost of production because getting parts out of the preset tolerances may require rework for its use, when it does not cause bigger problems such as breaking of cutting tools or the loss of the part. Therefore, the right time to exchange the tool should be well defined when wanted to minimize production costs. When the flank wear is the limiting tool life, the time predetermination that a cutting tool must be used for the machining occurs within the limits of tolerance can be done without difficulty. This paper aims to show how the life of the cutting tool can be calculated taking into account the cutting parameters (cutting speed, feed and depth of cut), workpiece material, power of the machine, the dimensional tolerance of the part, the finishing surface, the geometry of the cutting tool and operating conditions of the machine tool, once known the parameters of Taylor algebraic structure. These parameters were raised for the ABNT 1038 steel machined with cutting tools of hard metal.

scholarly journals Prediction of Machining Characteristics of Hybrid Composites Using Response Surface Methodology Approach

2018 ◽  
Vol 7 (3.1) ◽  
pp. 162 ◽  
Author(s):  
Ramanan. G ◽  
Rajesh Prabha.N ◽  
Diju Samuel.G ◽  
Jai Aultrin. K. S ◽  
M Ramachandran
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Hybrid Composites ◽  
Removal Rate ◽  
Cutting Speed ◽  
Chemical Properties ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Optimal Parameters ◽  
Rsm Technique

This manuscript presents the influencing parameters of CNC turning conditions to get high removal rate and minimal response of surface roughness in turning of AA7075-TiC-MoS2 composite by response surface method. These composites are particularly suited for applications that require higher strength, dimensional stability and enhanced structural rigidity. Composite materials are engineered materials made from at least two or more constituent materials having different physical or chemical properties. In this work seventeen turning experiments were conducted using response surface methodology. The machining parameters cutting speed, feed rate, and depth of cut are varied with respect to different machining conditions for each run. The optimal parameters were predicted by RSM technique. Turning process is studied by response surface methodology design of experiment. The optimal parameters were predicted by RSM technique. The most influencing process parameter predicted from RSM techniques in cutting speed and depth of cut.   

Optimization of Surface Roughness in Turning of Ti-6Al-4V Using Response Surface Methodology and TLBO

2015 ◽  
Author(s):  
Neelesh Ku. Sahu ◽  
A. B. Andhare
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Titanium Alloys ◽  
Response Surface ◽  
Feed Rate ◽  
Cutting Speed ◽  
Maximum Error ◽  
Depth Of Cut ◽  
Interaction Variables ◽  
Teaching Learning

Surface roughness is an important surface integrity parameter for difficult to cut alloys such as Titanium alloys (Ti-6Al-4V). In the present work, initially a mathematical model is developed for predicting surface roughness for turning operation using Response Surface Methodology (RSM). Later, a recently developed advanced optimization algorithm named as Teaching Learning Based Optimization (TLBO) is used for further parameter optimization of the equation developed using RSM. The design of experiments was performed using central composite design (CCD). Analysis of variance (ANOVA) demonstrated the significant and non-significant parameters as well as validity of predicted model. RSM describes the effect of main and mixed (interaction) variables on the surface roughness of titanium alloys. RSM analysis over experimental results showed that surface roughness decreased as cutting speed increased whereas it increased with increase in feed rate. Depth of cut had no effect on surface roughness. By comparing the predicted and measured values of surface roughness the maximum error was found to be 7.447 %. It indicates that the developed model can be effectively used to predict the surface roughness. Further optimization of the roughness equation was carried out by TLBO method. It gave minimum surface roughness as 0.3120 μm at the cutting speed of 1704 RPM (171.217 m/min), feed rate of 55.6 mm/min (.033 mm/rev) and depth of cut of 0.7 mm. These results were confirmed by confirmation experiment and were better than that of RSM.

Prediction the Influence of Machining Parameters for CNC Turning of Aluminum Alloy Using RSM and ANN

2020 ◽  
Vol 38 (6A) ◽  
pp. 887-895
Author(s):  
Hind H. Abdulridha ◽  
Aseel J. Haleel ◽  
Ahmed A. Al-duroobi
Keyword(s):  
Response Surface Methodology ◽  
Aluminum Alloy ◽  
Prediction Model ◽  
Response Surface ◽  
Feed Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Rsm Model ◽  
Good Agreement

The main objective of this paper is to develop a prediction model using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) for the turning process of Aluminum alloy 6061 round rod. The turning experiments carried out based on the Central Composite Design (CCD) of Response Surface Methodology. The influence of three independent variables such as Cutting speed (150, 175 and 200 mm/ min), depth of cut (0.5, 1 and 1.5 mm) and feed rate (0.1, 0.2 and 0.3 mm/rev) on the Surface Roughness (Ra) were analyzed through analysis of variance (ANOVA). The response graphs from the Analysis of Variance (ANOVA) present that feed-rate has the strongest influence on Ra dependent on cutting speed and depth of cut. Surface response methodology developed between the machining parameters and response and confirmation experiments reveals that the good agreement with the regression models. The coefficient of determination value for RSM model is found to be high (R2 = 0.961). It indicates the goodness of fit for the model and high significance of the model. From the result, the maximum error between the experimental value and ANN model is less than the RSM model significantly. However, if the test patterns number will be increased then this error can be further minimized. The proposed RSM and ANN prediction model sufficiently predict Ra accurately. However, ANN prediction model is found to be better compared to RSM model. The artificial neutral network is applied to experimental results to find prediction results for two response parameters. The predicted results taken from ANN show a good agreement between experimental and predicted values with the mean squared error of training indices equal to (0.000) which produces flexibility to the manufacturing industries to select the best setting based on applications.

Surface Roughness Prediction in End Milling of Machinable Glass Ceramic and Optimization by Response Surface Methodology

2011 ◽  
Vol 189-193 ◽  
pp. 1376-1381
Author(s):  
Moola Mohan Reddy ◽  
Alexander Gorin ◽  
Khaled A. Abou El Hossein
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Glass Ceramic ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Speed ◽  
Lower Surface ◽  
Depth Of Cut ◽  
Machining Parameters

This paper presents the prediction of a statistically analyzed model for the surface roughness,R_a of end-milled Machinable glass ceramic (MGC). Response Surface Methodology (RSM) is used to construct the models based on 3-factorial Box-Behnken Design (BBD). It is found that cutting speed is the most significant factor contributing to the surface roughness value followed by the depth of cut and feed rate. The surface roughness value decreases for higher cutting speed along with lower feed and depth of cut. Additionally, the process optimization has also been done in terms of material removal rate (MRR) to the model’s response. Ideal combinations of machining parameters are then suggested for common goal to achieve lower surface roughness value and higher MRR.

scholarly journals Statistical Modelling to Study the Implications of Coated Tools for Machining AA 2014 Using Grey Taguchi-Based Response Surface Methodology

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Madhanagopal Manoharan ◽  
Arul Kulandaivel ◽  
Adinarayanan Arunagiri ◽  
Mohamad Reda A. Refaai ◽  
Simon Yishak ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Process Parameter ◽  
Depth Of Cut ◽  
Input Process ◽  
Coated Tools ◽  
Grey Taguchi

Milling is the surface machining process by removing material from the raw stock using revolving cutters. This process accounts for a major stake in most of the Original Equipment Manufacturing (OEM) industries. This paper discusses optimizing process parameters for machining the AA 2014 T 651 using a vertical milling machine with coated cutting tools. The process parameters such as cutting speed, depth of cut, and type of the cutting tool with all its levels are identified from the previous literature study and several trial experiments. The Taguchi L9 Orthogonal Array (OA) is used for the experimental order with the chosen input parameters. The commonly used cutting tools in the machining industry, such as High-Speed Steel (HSS) and its coated tools, are considered in this study. These tools are coated with Titanium Nitride (TiN) and Titanium Aluminum Nitride (TiAlN) by Physical Vapor Deposition (PVD) technique. The output responses such as cutting forces along the three-axis are measured using a milling tool dynamometer for the corresponding input factors. The input process parameters are optimized by considering the output responses such as MRR, machining torque, and thrust force. Grey Taguchi-based Response Surface Methodology (GTRSM) is used for multiobjective multiresponse optimization problems to find the optimum input process parameter combination for the desired response. Polynomial regression equations are generated to understand the mathematical relation between the input factor and output responses as well as Grey Relational Grade (GRG) values. The optimum process parameter combination from the desirability analysis is the HSS tool coated with TiAlN at a cutting speed of 270 rpm and a depth of cut value of 0.2 mm.

scholarly journals Optimization of Cutting Parameters on Turning Process Based on Surface Roughness Using Response Surface Methodology

2011 ◽  
Vol 117-119 ◽  
pp. 1561-1565
Author(s):  
Muhammad Yusuf ◽  
Mohd Khairol Anuar Ariffin ◽  
N. Ismail ◽  
S. Sulaiman
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Process ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Dry Cutting ◽  
Optimization Of Cutting Parameters

This paper describes effect of cutting parameters on surface roughness for turning of aluminium alloy 7050 using carbide cutting tool with dry cutting condition. The model is developed based on cutting speed, feed rate and depth of cut as the parameters of cutting process. The selection of cutting process was based on the design of experiments Response Surface Methodology (RSM). The objective of this research is finding the optimum cutting parameters based on surface roughness. The relation between cutting parameters and surface roughness were discussed.

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.

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.

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