Cutting Parameters and Surface Roughness Research Papers (original) (raw)

In this work optimization of turning parameters for turning Ti-6Al-7Nb Titanium alloy has been investigated by orthogonal array along with Grey relational analysis coupled with principle component. Parameters such as surface roughness, tool wear, roundness, material removal rate, temperature and power consumption are considered as performance characteristics of the turning process. By orthogonal array eighteen experiments are carried out in the CNC machine by considering cutting environment, cutting speed, feed rate, depth of cut, nose radius, tool coating type and insert shape angle as turning parameters. From the grey relational grade the optimum turning parameters were predicted. Also the influence of individual turning parameters is carried out by Analysis of Variance. From the largest value of grey relational grade parameters , cutting environment: wet, feed rate : 0.08 mm /rev, cutting speed: 100 m/min ,insert angle: 80 0 , nose radius: 0.4 mm, tool coating type : TiAlN and depth of cut:0.4 mm are found to be better turning parameters levels. About 11.3 % enhancement of grey relation grade value is achieved when compared to initial parameter grey relational grade value.

- by
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- Mathematics Education, Production, Engineering Product Design, Metal Cutting

The accuracy of achieving required surface finish or surface roughness is of great importance in case of any mass production environment. Thus, understanding and recognizing the optimal process parameters for machining is the key to achieving the required surface roughness and gaining competitive advantage. This research is concerned with obtaining the optimal machining process parameters (cutting
speed, depth of cut, feed) which will in turn effect in optimizing the surface roughness in turning glass fiber reinforced polymer (GFRP) matrix composite using coated carbide insert. To find the optimal machining process parameters Taguchi design method has been implemented. An L-16 orthogonal array, signal to noise ratio and ANOVA have been implemented to analyze and understand various process parameters in determining possible relationship with surface roughness. The optimum surface roughness value for this experiment is (3.664µm) which is obtained from Taguchi design method. The results from ANOVA conclude that feed is the most influential factor affecting the outcome having a contribution of (56.04%). And the contributions of depth of cut and cutting speed are separately in order of (17.94%) and (16.42%) respectively. This research provides optimal process parameters for any desired value of surface roughness which results in gaining a competitive edge over others in any mass production environment.

- by Md. Shafiul Alam and +1
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- Manufacturing, Composite Materials, ANOVA, Cutting Parameters and Surface Roughness

In machining technologies, the most important criterion taken into consideration when evaluating the product quality is seen as the surface roughness. In the consideration of production quality and cost, tool wear is one of the factors that directly affect the cost of production. In the machining process, the most important parameters affecting the surface roughness and tool temperature are the cutting depth, speed and feed rate of rotation. In order to obtain the best surface quality and to keep the cost at the optimum level, the most suitable processing parameters should be selected by taking into consideration the effect of these parameters on each other. In this study, it is aimed that to prediction of surface roughness (Ra.) and tool temperature (°C) values for turning which has an important position in machining. For this purpose, Artificial Neural Networks (ANN) method and Multi Linear Regression Model (MLRM) were used separately. The data obtained from ANN, Regression Model were compared with the actual test data, and the results were examined. According to the obtained results, it is seen that the ANN method has more successful results than Regression model in surface roughness and tool temperature estimation.

In machining technologies, one of the most important criteria that is taken into consideration when evaluating the product quality is seen as surface roughness. In order to achieve the desired level of surface roughness of the machined part, the tool vibration should be decreased to minimum levels. In the machining process, the most important parameters affecting the surface roughness and vibration are cutting parameters such as depth of cut, feed rate, rotational speed, tool wear and overhang. To obtain better surface roughness must be optimized this important parameters. There are many artificial intelligence optimization tools to parameter optimization. In this study, it is aimed that to investigate relation between surface roughness and tool vibration at first. Besides that the effects of cutting parameters such as depth of cut, feed rate, spindle speed on surface roughness and optimization of all parameters for best product quality. The optimum parameter combination developed by using Taguchi Method was confirmed with the help of confirmation tests. As a result, with the help of optimization, the surface roughness and the tool vibration was reduced.

This paper investigated the feasibility of drilling aluminum 7050 with enhanced cutting speed. Two
identical tools and two identical Al-7050 work-piece plates were utilised during the experiment. Wet
cooling drilling on both conventional and high cutting speeds was conducted to compare the results. A total
of 338 holes were drilled towards the end of the experiment. Tests for tool wear, borehole diameter, and
hole quality were carried out to observe the different drilling performances by drilling with different
cutting speeds. The result shows that the proposed high speed drilling produced better diameter accuracy,
better surface roughness but poor exit burr hole tendency.

Abrasive Waterjet (AWJ) cutting has proven to be an effective technology for material processing with the distinct advantages of no thermal distortion, high machining versatility, high flexibility and small cutting forces. In this paper, Taguchi robust design analysis is employed to determine optimal combination of process parameters. The Analysis of Variance (ANOVA) is also applied to identify the most significant factor. The process parameters such as pressure, transverse speed, stand of distance and abrasive flow rate are optimized to investigate their influence on Metal Removal Rate (MRR) and Surface Roughness (Ra) of Inconel. Experiments are carried out by L9 orthogonal array and the results are provided to verify this approach and credible tendencies of output parameters with respect to the input parameters are discussed, from which recommendations are made for process control and optimization. I. INTRODUCTION Abrasive water jet machining makes use of the principles of both abrasive jet machining and water jet machining. In abrasive water jet machining a small stream of fine grained abrasive particles is mixed in suitable proportion, which is forced on a work piece surface through a nozzle. Material removal occurs due to erosion caused by the impact of abrasive particles on the work surface. AWJM is especially suitable for machining of brittle material like glass, ceramics and stones as well as for composite materials and ferrous and nonferrous material. The characteristics of surface produced by this technique depend on many factors like jet pressure, Stand-off distance of nozzle from the target. Abrasive flow rate, Traverse rate, works materials. Non-contact of the tool with work piece, no heat affected zone, low machining force on the work surface and ability to machine wide range of materials has increase the use of abrasive water jet machining over other machining processes.

This study presents an experimental investigation ofwire electric discharge machining (WEDM) for improving the process performance. The effects of the machining parameters were investigated on the machining performance. Adaptive neuro-fuzzy inference system (ANFIS) was applied to determine the effect of significant parameters on WEDM performance. In addition, ANFIS was used to predict the cutting speed, surface roughness and heat affected zone in WEDM. The predicted cutting speed, surface roughness, and heat affected zone were compared with measured data, and the average prediction error for cutting speed, surface roughness, and heat affected zone were 3.41, 3.89, and 4.1 respectively.

- by Ibrahem Maher
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- Fuzzy Logic, Surface Roughness, Cutting Parameters and Surface Roughness, ANFIS

Present work considers the parametric optimization of CNC MAX MILL machining for Mild Steel (EN18) with Cemented Carbide as cutting tool under constant flow of coolant. The machining cutting parameters (cutting speed, feed rate and depth of cut) optimized to evaluate high material removal rate and minimum surface roughness. Response surface method interpreted the experiment data with the help of Design of experiment. Analysis of variance (ANOVA) shows the different parameters which provide the significant impact on the values of surface roughness and material removal rate. The optimum solution of Material Removal Rate (MRR) and Roughness (SR) can be found at the cutting speed of 4186 rpm, feed rate of 1831 mm/min. and depth of cut of 0.60 mm. KEYWORD CNC MAX MILL Machine, Response Surface Method (RSM), Material Removal Rate (MRR), Cutting Parameter, Output Parameter

Electric Discharge Machining (EDM) has become a popular machining process for dies and moulds industry. Hole making operations constitute a majority of the operations in dies and molds industry. EDM utilizes an electrode for its principle requirements for the erosion of work piece materials. This research paper is aimed to find the effects of different types of electrode materials namely Copper, Graphite and Tungsten Carbide on the quality of blind holes generated during the operation. The quality of blind holes is evaluated in term of surface roughness, diameter error and axial error. Graphite as an electrode is found to be the most suitable for most of performance characteristics.

- by Ibrahem Maher
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- Mechanical Engineering, Applied Mathematics, Materials Science, Microstructure

This study presents experimental result of surface roughness and chip-tool interface temperature developed during turning of AISI 316 austenitic stainless steels using TiAlSiN coated cemented carbide insert. TiAlSiN coating is deposited by Cathodic Arc Evaporation (PVD) technique. The work-tool thermocouple calibration set-up was developed. The air heater was used as a heating element at the work-tool junction. The experiments were conducted at cutting speeds in the range of 140 to 320 m/min, feed in the range of 0.08 to 0.26 mm/rev keeping depth of cut constant at 1 mm. The influence of cutting parameters and tool coating were investigated on the average chip-tool interface temperature and surface roughness. Experimentally interface temperature 979°C was observed at 260 m/min cutting speed and 0.14 mm/rev feed. The interface temperature in turning is strongly dependent on the cutting speed followed by feed and exactly reverse case was observed in case of surface roughness.

The paper presents a study of the effect of operating variable parameter; cutting speed, feed rate, depth of cut and width of cut on heat being generated when end milling under MQL condition. The response surface methodology (RSM) was employed in the experiment, and a Box–Behnken design was used to determine the cause and effect of the relationship between the input variables and response. The investigated milling parameters were cutting speed (100 - 140 m/min), feed rate (0.1 - 0.2 mm/tooth), depth of cut (0.5-1.0 mm) and width of cut (0.2 -1.8 mm). Result of this study show ball nose end milling generates low temperature ranging from 69°C to 359°C. Experimental data and statistical analysis showed that heat generation was dominated by radial depth of cut, followed by axial depth of cut. Feed rate and cutting speed were found statistically not significant. The linear models were developed with a 92% confidence level. The optimum condition required for minimum heat generated include...

- by Shahir Kasim
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- Heat Transfer, Response Surface Methodology, Multidisciplinary, Optimization

End Milling is a slot making operation which is extensively used in various industrial applications such as aerospace, automotive and dies molds making industry. Functioning of the parts manufactured in above mentioned industries is linked with the quality of the machined slots. This paper is aimed at investigating the quality of end milled slots taking into account different input process parameters whereas Titanium Nitride coated tools are employed for end milling operation. Quality of end milled slots is measured in terms of surface roughness and diameter error. Taguchi orthogonal array is used as the experimental design which resulted in better quality of end milled slots.

Polyetheretherketones (PEEK) have been increasingly employed as biomaterials for trauma, orthopedic, and spinal implants. These implants are commonly fabricated by extrusion and injection molding, and for this fact, additional machining operations are required. Surface roughness is a vital factor for medical implants since the cells of the surrounding tissue interact with the underlying substrate on the micro and nanometer scales. For some application, such as self-mating articulation cervical disc implants smooth surface finish is critical so as to minimize the contact friction and wear. The requirement for a fine surface roughness poses a major concern in machining of polymeric base materials due to its low thermal conductivity. Machining performance such as surface roughness is directly affected by the milling parameter and should be methodically analyzed. Thus, this paper aims to study the effect of milling parameter on surface roughness of PEEK plastic under dry machining condition. Response Surface Methodology (RSM) technique was used to evaluate the influence of milling parameter namely cutting speed, feed rate and depth of cut on machined surface. From the conducted study, based on the statistical analysis result it is found that feed rate is the main factor that influence the surface roughness followed by milling speed and depth of cut. In addition, optical observation on the machined surface indicated that the mechanisms of the surface finish obtained from machining of polymeric based composites are different from those obtained from machining of the metals. It shows that there is some form of polymeric softening taking place when the cutting speed exceeded a critical cutting speed.

- by R. Izamshah
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- Response Surface Methodology, Machining, Multidisciplinary, Surface Roughness

The paper presents a studyof the effect of operating variable parameter; cutting speed, feed rate, depth of cut and width of cut on heat being generated when end milling under MQL condition. The response surface methodology (RSM) was employed in the experiment, and a Box–Behnken design was used to determine the cause and effect of the relationship between the input variables and response. The investigated milling parameters were cutting speed (100 - 140 m/min), feed rate (0.1 - 0.2 mm/tooth), depth of cut (0.5-1.0 mm) and width of cut (0.2 -1.8 mm). Result of this study show ball nose end milling generates low temperature ranging from 69°C to 359°C. Experimental data and statistical analysis showed that heat generation was dominated by radial depth of cut, followed by axial depth of cut. Feed rate and cutting speed were found statistically not significant. The linear models were developed with a 92% confidence level. The optimum condition required for minimum heat generated include cutting speed of 117 m/min, feed rate of 0.11 mm/rev, axial depth of cut of 0.57 mm, and radial depth of cut of 0.21 mm. With this optimum condition, a minimum heat generated of 68°C was obtained.

This paper investigated the feasibility of drilling aluminum 7050 with enhanced cutting speed. Two identical tools and two identical Al-7050 work-piece plates were utilised during the experiment. Wet cooling drilling on both conventional and high cutting speeds was conducted to compare the results. A total of 338 holes were drilled towards the end of the experiment. Tests for tool wear, borehole diameter, and hole quality were carried out to observe the different drilling performances by drilling with different cutting speeds. The result shows that the proposed high speed drilling produced better diameter accuracy, better surface roughness but poor exit burr hole tendency.

An attempt has been made into the role of conducting an experimental research on aluminium alloys by using the technique to find and correlate Taguchi technological factors for the economy of the machining process. The powerful statistical tool Taguchi method is a systematic application of design and analysis of experiments. It is an effective way to produce high quality at a relatively low cost method. Improving one parameter leads to degradation of other parameters and optimization of various parameters is much more complicated. Therefore, Taguchi technique is used to investigate the characteristic multiple performance in drilling operation. In this paper, the effect of the variation of machining parameters such as speed, power, depth of cut and radius nose in Al6063T6 has been studied and presented. First, the optimal arrangement of the four parameters of rotation has been determined using the configuration of the Taguchi L9 technique with a variation in three levels. After machining is completed, the values are documented and compared using statistical analysis software. I. INTRODUCTION Machining is a term that covers a large collection of manufacturing processes designed to remove unwanted material, usually in the form of chips, from a work-piece. Machining is used to convert castings, forgings, or preformed blocks of metal into desired shapes, with size and finish specified to fulfil design requirements. Almost every manufactured product has components that require machining, often to great precision. The majority of industrial applications of machining are in metals. Although the metal cutting process has resisted theoretical analysis because of its complexity, the application of these processes in the industrial world is widespread. Metal cutting processes can be viewed as consisting of independent input variables, dependent variables, and independent-dependent interactions or relationships. The engineer or machine tool operator has direct control over the input variables and can specify or select them when setting up the machining process. The three most common cutting tool materials currently in use for production machining operations are High-Speed Steel, both in wrought and powder metallurgy form, carbides and coated tools. Cubic Boron Nitride, ceramics, and diamonds are also being widely employed. Selection of a tool material that provides reliable service while fulfilling the functional requirements is still an art. The harder the tool material, the better it can resist wear at faster cutting speeds. The faster the cutting speed, higher the cutting temperature and the shorter the tool life. Retention of hardness at elevated temperatures as well as long tool life is desirable characteristics in cutting tools.

Due lo the conslant p rogress and exigencies of metalmech anics indu s tries. fuey have been forced lo look for high ly a ccurat e methods to predict a d es ired s u rface roughness value a t a lower price. Al though the grinding process. comp ared to other machining p rocess (turning and milling) , is a good on e to r emove material as w ell to obtain a good surface finish , requires a good controlan the operative variables to guar antee a b est surface finish. Ba sed on the influ ence of the grinding culting variables, such as, workplece speed fVp), down feed (d) and different hardn ess for an AlSI 1045 and AlSl4140 s teel, a simple mathematical model for rou ghness Ha was obtain ed in order lo predict the value of rough ness r quired for lhe established cutting con d ítions .

- by Carolina Payares-Asprino and +3
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- Surface Roughness, Cutting Parameters and Surface Roughness

The accuracy of achieving required surface finish or surface roughness is of great importance in case of any mass production environment. Thus, understanding and recognizing the optimal process parameters for machining is the key to achieving the required surface roughness and gaining competitive advantage. This research is concerned with obtaining the optimal machining process parameters (cutting speed, depth of cut, feed) which will in turn effect in optimizing the surface roughness in turning glass fiber reinforced polymer (GFRP) matrix composite using coated carbide insert. To find the optimal machining process parameters Taguchi design method has been implemented. An L- 16 orthogonal array, signal to noise ratio and ANOVA have been implemented to analyze and understand various process parameters in deter- mining possible relationship with surface roughness. The optimum surface roughness value for this experiment is (3.664µm) which is obtained from Taguchi design method. The results from ANOVA conclude that feed is the most influential factor affecting the outcome having a contribution of (56.04%). And the contributions of depth of cut and cutting speed are separately in order of (17.94%) and (16.42%) respectively. This research provides optimal process parameters for any desired value of surface roughness which results in gaining a competitive edge over others in any mass production environment.

- by Ahmed yusuf
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- Engineering, Materials Science, Manufacturing, Composite Materials

The experimental study presented in this paper aims to select the most suitable cutting and offset parameter combination for the wire electrical discharge machining process in order to get the desired surface roughness value for the machined workpieces. A series of experiments have been performed on 1040 steel material of thicknesses 30, 60 and 80 mm, and on 2379 and 2738 steel materials of thicknesses 30 and 60 mm. The test specimens have been cut by using different cutting and offset parameter combinations of the "Sodick Mark XI A500 EDW" wire electrical discharge machine in the Middle East Technical University CAD/CAM/Robotics Center. The surface roughness of the testpieces has been measured by using a surface roughness measuring device. The related tables and charts have been prepared for 1040, 2379, 2738 steel materials. The tables and charts can be practically used for WEDM parameter selection for the desired workpiece surface roughness.

- by Amran Ali and +3
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- Response Surface Methodology, Surface Roughness, Cutting Parameters and Surface Roughness, Plastics

The paper presents a studyof the effect of operating variable parameter; cutting speed, feed rate, depth of cut and width of cut on heat being generated when end milling under MQL condition. The response surface methodology (RSM) was employed in the experiment, and a Box-Behnken design was used to determine the cause and effect of the relationship between the input variables and response. The investigated milling parameters were cutting speed (100-140 m/min), feed rate (0.1-0.2 mm/tooth), depth of cut (0.5-1.0 mm) and width of cut (0.2-1.8 mm). Result of this study show ball nose end milling generates low temperature ranging from 69°C to 359°C. Experimental data and statistical analysis showed that heat generation was dominated by radial depth of cut, followed by axial depth of cut. Feed rate and cutting speed were found statistically not significant. The linear models were developed with a 92% confidence level. The optimum condition required for minimum heat generated include cutting speed of 117 m/min, feed rate of 0.11 mm/rev, axial depth of cut of 0.57 mm, and radial depth of cut of 0.21 mm. With this optimum condition, a minimum heat generated of 68°C was obtained.

- by Raja Izamshah
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- Materials Science, Heat Transfer, Response Surface Methodology, Multidisciplinary

The accuracy of achieving required surface finish or surface roughness is of great importance in case of any mass production environment. Thus, understanding and recognizing the optimal process parameters for machining is the key to achieving the required surface roughness and gaining competitive advantage. This research is concerned with obtaining the optimal machining process parameters (cutting speed, depth of cut, feed) which will in turn effect in optimizing the surface roughness in turning glass fiber reinforced polymer (GFRP) matrix composite using coated carbide insert. To find the optimal machining process parameters Taguchi design method has been implemented. An L-16 orthogonal array, signal to noise ratio and ANOVA have been implemented to analyze and understand various process parameters in deter-mining possible relationship with surface roughness. The optimum surface roughness value for this experiment is (3.664μm) which is obtained from Taguchi design method. The results from ANOVA conclude that feed is the most influential factor affecting the outcome having a contribution of (56.04%). And the contributions of depth of cut and cutting speed are separately in order of (17.94%) and (16.42%) respectively. This research provides optimal process parameters for any desired value of surface roughness which results in gaining a competitive edge over others in any mass production environment.

- by Abir Rahman
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- Manufacturing, Composite Materials, Machining, Optimization Methods

The accuracy of achieving required surface finish or surface roughness is of great importance in case of any mass production environment. Thus, understanding and recognizing the optimal process parameters for machining is the key to achieving the required surface roughness and gaining competitive advantage. This research is concerned with obtaining the optimal machining process parameters (cutting speed, depth of cut, feed) which will in turn effect in optimizing the surface roughness in turning glass fiber reinforced polymer (GFRP) matrix composite using coated carbide insert. To find the optimal machining process parameters Taguchi design method has been implemented. An L-16 orthogonal array, signal to noise ratio and ANOVA have been implemented to analyze and understand various process parameters in determining possible relationship with surface roughness. The optimum surface roughness value for this experiment is (3.664µm) which is obtained from Taguchi design method. The result...

- by Inzamam Ul Haq
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- Manufacturing, Composite Materials, Machining, Optimization Methods