Relationship Between Measurement of Cutting Force and Sensor Location in Turning Process

2012 ◽  
Vol 59 (2) ◽  
Author(s):  
Jaharah A. Ghani ◽  
Poh Siang Jye ◽  
Che Hassan Che Haron ◽  
Muhammad Rizal ◽  
Mohd Zaki Nuawi
Keyword(s):  
Cutting Force ◽  
Strain Gauge ◽  
Cutting Speed ◽  
Strain Gauges ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Sensor Location ◽  
Turning Process ◽  
Feed Force ◽  
Cutting Point

Turning process is widely used in the production of components for automotive and aerospace applications. The machinability of a work material is commonly assessed in terms of cutting tool life, surface finish, and cutting force. These responses are dependent on machining parameters such as cutting speed, feed rate, and depth of cut. In this study, the relationships between cutting force, cutting speed, and sensor location in the turning process were investigated. Strain gauge was chosen as the sensor for the detection of cutting force signal during turning of hardened plain carbon steel JIS S45C. Two strain gauges were mounted on a tool holder at a defined location of I, II, or III at a distance of 37, 42, or 47 mm, respectively, from the cutting point. Only one set of machining experiments was conducted at spindle speed = 1000 rpm, feed = 0.25 mm/rev, and depth of cut = 0.80 mm. The turning process was stopped and the insert was discarded when average flank wear reached 0.30 mm. The main cutting force and the feed force for each cycle measured by the strain gauges at location I, II, and III were collected and analyzed. Results show that when cutting speed was increased, the main cutting force and the feed force were decreased accordingly. The change of was inversely proportional to the change of cutting speed, but the did not decrease continuously and behaved contrarily. A strain gauge placed at a distance of approximately 43 mm from the cutting point was found to be the best and most suitable for sensing accurate force signals.

Micro Textured Cutting Inserts with Solid Lubrication as Alternative Coolant to Mineral Oil-Based Cutting Fluid in Turning Operation

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
C. Divya ◽  
L. Suvarna Raju ◽  
B. Singaravel
Keyword(s):  
Process Parameters ◽  
Orthogonal Array ◽  
Inconel 718 ◽  
Cutting Speed ◽  
Solid Lubricants ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Turning Process ◽  
Optimization Of Process Parameters ◽  
Cutting Inserts

Turning process is a primary process in engineering industries and optimization of process parameters enhance the machining performance. Inconel 718 is a nickel-based superalloy, widely found applications in the manufacturing of blades, sheets and discs in aircraft engines and rocket engines. It provides toughness at low temperature, with stand high mechanical stresses at elevated temperature and creep resistance. In this work, turning process is carried out on Inconel 718 with micro whole textured cutting inserts filled with solid lubricants. Three different solid lubricants are used namely molybdenum-di-sulfide (MoS2), tungsten-di-sulfide (WS2) and calcium-di-fluoride (CaF2). Experiments are performed as per L9 orthogonal array. Statistical approaches such as orthogonal array, Signal-to-Noise (S/N) ratio and Analysis of Variance (ANOVA) are used to find the importance and effects of machining parameters. In this study, input parameters included are feed, cutting speed and depth of cut and output parameter includes surface roughness. Optimization of process parameters is carried out and the significance is estimated. The result suggested that WS2 followed by MoS2 and CaF2 given good surface finish value. Also, solid lubricant in machining enhances the sustainability in manufacturing.

scholarly journals Investigation of AlCrN-Coated Inserts on Cryogenic Turning of Ti-6Al-4V Alloy

Metals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1338
Author(s):  
Lakshmanan Selvam ◽  
Pradeep Kumar Murugesan ◽  
Dhananchezian Mani ◽  
Yuvaraj Natarajan
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
Physical Vapor Deposition ◽  
Metal Cutting ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Machined Surface ◽  
Coated Carbide ◽  
Cryogenic Conditions

Over the past decade, the focus of the metal cutting industry has been on the improvement of tool life for achieving higher productivity and better finish. Researchers are attempting to reduce tool failure in several ways such as modified coating characteristics of a cutting tool, conventional coolant, cryogenic coolant, and cryogenic treated insert. In this study, a single layer coating was made on cutting carbide inserts with newly determined thickness. Coating thickness, presence of coating materials, and coated insert hardness were observed. This investigation also dealt with the effect of machining parameters on the cutting force, surface finish, and tool wear when turning Ti-6Al-4V alloy without coating and Physical Vapor Deposition (PVD)-AlCrN coated carbide cutting inserts under cryogenic conditions. The experimental results showed that AlCrN-based coated tools with cryogenic conditions developed reduced tool wear and surface roughness on the machined surface, and cutting force reductions were observed when a comparison was made with the uncoated carbide insert. The best optimal parameters of a cutting speed (Vc) of 215 m/min, feed rate (f) of 0.102 mm/rev, and depth of cut (doc) of 0.5 mm are recommended for turning titanium alloy using the multi-response TOPSIS technique.

Taguchi Method Based Experiments of Titanium TC11 Flank Milling Parameters

2009 ◽  
Vol 407-408 ◽  
pp. 608-611 ◽  
Author(s):  
Chang Yi Liu ◽  
Cheng Long Chu ◽  
Wen Hui Zhou ◽  
Jun Jie Yi
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Feed Rate ◽  
High Speed ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Flank Milling ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Mill Machining

Taguchi design methodology is applied to experiments of flank mill machining parameters of titanium alloy TC11 (Ti6.5A13.5Mo2Zr0.35Si) in conventional and high speed regimes. This study includes three factors, cutting speed, feed rate and depth of cut, about two types of tools. Experimental runs are conducted using an orthogonal array of L9(33), with measurement of cutting force, cutting temperature and surface roughness. The analysis of result shows that the factors combination for good surface roughness, low cutting temperature and low resultant cutting force are high cutting speed, low feed rate and low depth of cut.

In-Process Monitoring and Prediction of Surface Roughness in CNC Turning Process

2011 ◽  
Vol 199-200 ◽  
pp. 1958-1966 ◽  
Author(s):  
Somkiat Tangjitsitcharoen
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Prediction Accuracy ◽  
Cutting Speed ◽  
Least Square ◽  
Depth Of Cut ◽  
Turning Process ◽  
Roughness Model ◽  
Force Ratio ◽  
Cnc Turning Process

The objective of this research is to propose a practical model to predict the in-process surface roughness during the turning process by using the cutting force ratio. The proposed in-process surface roughness model is developed based on the experimentally obtain result by employing the exponential function with six factors of the cutting speed, the feed rate, the rank angle the tool nose radius, the depth of cut, and the cutting force ratio. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method. The prediction accuracy of the in-process surface roughness model has been verified to monitor the in-process predicted surface roughness at 95% confident level. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It has been proved by the cutting tests that the proposed and developed in-process surface roughness model can be used to predict the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.

scholarly journals Experimental Investigation and Optimization of Machining Parameters in Turning of Aluminum Alloy 075-T651

2021 ◽  
Vol 27 (4) ◽  
pp. 296-305
Author(s):  
Arpit Srivastava ◽  
Mukesh Kumar Verma ◽  
Ramendra Singh Niranjan ◽  
Abhishek Chandra ◽  
Praveen Bhai Patel
Keyword(s):  
Aluminum Alloy ◽  
Process Parameters ◽  
Cutting Speed ◽  
Radial Force ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Optimization Of Process Parameters ◽  
Feed Force ◽  
Aluminum 7075 ◽  
Aluminum Alloy 7075

Abstract Aluminum alloy 7075-T651 is a widely used material in the aviation, marine, and automobile sectors. The wide application marks the importance of this material’s research in the manufacturing field. This research focuses on optimizing input process parameters of the turning process in the machining of Aluminum 7075-T651 with a tungsten carbide insert. The input machining parameters are cutting speed, feed, and depth of cut for the output response parameters cutting force, feed force, radial force, material removal, and surface roughness of the workpiece. For optimization of process parameters, the Taguchi method, with standard L9 orthogonal array, is used. ANOVA is applied to obtain signifi-cant factors and optimal combinations of process parameters.

scholarly journals Neural Network Modeling of Cutting Force and Chip Thickness Ratio for Turning Aluminum Alloy 7075-T6

2018 ◽  
Vol 14 (1) ◽  
pp. 67-76
Author(s):  
Mohanned Mohammed H. AL-Khafaji
Keyword(s):  
Cutting Force ◽  
Feed Rate ◽  
Cutting Speed ◽  
Chip Thickness ◽  
Cutting Parameters ◽  
Thickness Ratio ◽  
Depth Of Cut ◽  
Feed Force ◽  
Machining Force ◽  
Force Components

The turning process has various factors, which affecting machinability and should be investigated. These are surface roughness, tool life, power consumption, cutting temperature, machining force components, tool wear, and chip thickness ratio. These factors made the process nonlinear and complicated. This work aims to build neural network models to correlate the cutting parameters, namely cutting speed, depth of cut and feed rate, to the machining force and chip thickness ratio. The turning process was performed on high strength aluminum alloy 7075-T6. Three radial basis neural networks are constructed for cutting force, passive force, and feed force. In addition, a radial basis network is constructed to model the chip thickness ratio. The inputs to all networks are cutting speed, depth of cut, and feed rate. All networks performances (outputs) for all machining force components (cutting force, passive force and feed force) showed perfect match with the experimental data and the calculated correlation coefficients were equal to one. The built network for the chip thickness ratio is giving correlation coefficient equal one too, when its output compared with the experimental results. These networks (models) are used to optimize the cutting parameters that produce the lowest machining force and chip thickness ratio. The models showed that the optimum machining force was (240.46 N) which can be produced when the cutting speed (683 m/min), depth of cut (3.18 mm) and feed rate (0.27 mm/rev). The proposed network for the chip thickness ratio showed that the minimum chip thickness is (1.21), which is at cutting speed (683 m/min), depth of cut (3.18 mm) and feed rate (0.17 mm/rev).

scholarly journals Modeling and Optimization of Cutting Parameters during Machining of Austenitic Stainless Steel AISI304 Using RSM and Desirability Approach

2020 ◽  
Vol 65 (1) ◽  
pp. 10-26
Author(s):  
Septi Boucherit ◽  
Sofiane Berkani ◽  
Mohamed Athmane Yallese ◽  
Riad Khettabi ◽  
Tarek Mabrouki
Keyword(s):  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Cutting Force ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Desirability Approach ◽  
Optimization Of Cutting Parameters ◽  
Coated Carbide

In the current paper, cutting parameters during turning of AISI 304 Austenitic Stainless Steel are studied and optimized using Response Surface Methodology (RSM) and the desirability approach. The cutting tool inserts used in this work were the CVD coated carbide. The cutting speed (vc), the feed rate (f) and the depth of cut (ap) were the main machining parameters considered in this study. The effects of these parameters on the surface roughness (Ra), cutting force (Fc), the specific cutting force (Kc), cutting power (Pc) and the Material Removal Rate (MRR) were analyzed by ANOVA analysis.The results showed that f is the most important parameter that influences Ra with a contribution of 89.69 %, while ap was identified as the most significant parameter (46.46%) influence the Fc followed by f (39.04%). Kc is more influenced by f (38.47%) followed by ap (16.43%) and Vc (7.89%). However, Pc is more influenced by Vc (39.32%) followed by ap (27.50%) and f (23.18%).The Quadratic mathematical models, obtained by the RSM, presenting the evolution of Ra, Fc, Kc and Pc based on (vc, f, and ap) were presented. A comparison between experimental and predicted values presents good agreements with the models found.Optimization of the machining parameters to achieve the maximum MRR and better Ra was carried out by a desirability function. The results showed that the optimal parameters for maximal MRR and best Ra were found as (vc = 350 m/min, f = 0.088 mm/rev, and ap = 0.9 mm).

scholarly journals The effect of changes in depth of cut and cutting speed of CNC toolpaths on turning process performance

2018 ◽  
Vol 38 (1) ◽  
pp. 40-44
Author(s):  
Krzysztof Jarosz ◽  
Piotr Niesłony ◽  
Piotr Löschner
Keyword(s):  
Cutting Force ◽  
Tool Life ◽  
Cutting Speed ◽  
Machining Process ◽  
Depth Of Cut ◽  
Process Performance ◽  
Turning Process ◽  
Machining Time ◽  
Toolpath Optimization ◽  
The Impact

Abstract In this article, a novel approach to computer optimization of CNC toolpaths by adjustment of cutting speed vcand depth of cut apis presented. Available software works by the principle of adjusting feed rate on the basis of calculations and numerical simulation of the machining process. The authors wish to expand upon this approach by proposing toolpath optimization by altering two other basic process parameters. Intricacies and problems related totheadjustment of apand vcwere explained in the introductory part. Simulation of different variant of the same turning process with different parameter values were conducted to evaluate the effect of changes in depth of cut and cutting speed on process performance. Obtained results were investigated on the account of cutting force and tool life. The authors have found that depth of cut substantially affects cutting force, while the effect of cutting speed on it is minimal. An increase in both depth of cut and cutting speed affects tool life negatively, although the impact of cutting speed is much more severe. An increase in depth of cut allows for a more significant reduction of machining time, while affecting tool life less negatively. On the other hand, the adjustment of cutting speed helpsto reduce machining time without increasing cutting force component values and spindle load.

Optimizing Feed Force in Turning Process Using Taguchi’s Parameter Design Approach

2014 ◽  
Vol 592-594 ◽  
pp. 668-672
Author(s):  
Praveen Kumar ◽  
Hari Singh
Keyword(s):  
Process Parameters ◽  
Feed Rate ◽  
Cutting Speed ◽  
Design Approach ◽  
Parameter Design ◽  
Depth Of Cut ◽  
Turning Process ◽  
Feed Force ◽  
Optimal Value ◽  
Optimal Setting

The objective of the paper is to obtain an optimal setting of turning process parameters (cutting speed, depth of cut and feed rate) resulting in an optimal value of the feed force when machining En19 steel with tungsten carbide cutting tool inserts. The effects of the selected turning process parameters on feed force and the subsequent optimal settings of the parameters have been accomplished using Taguchi’s parameter design approach. It was indicated by the results that the selected turning process parameters significantly affect the selected machining characteristic. The percent contributions of parameters as quantified in the S/N ANOVA envisage that the relative power of cutting speed (72.09 %) in controlling variation and mean feed force is significantly higher than that of the depth of cut (22.30 %) and feed rate (05.31 %). The predicted optimum feed force is 98.067 N. The results have been validated by the confirmation experiments.

Multi Response Optimization of Setting Process Variables in Face Milling of ZE41 Magnesium Alloy using Ranking Algorithms and ANOVA

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
S.P. Sundar Singh Sivam ◽  
V.G. Umasekar ◽  
Ganesh Babu Loganathan ◽  
D Kumaran ◽  
K. Saravanan
Keyword(s):  
Cutting Speed ◽  
Chip Thickness ◽  
Numerical Control ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Setting Process ◽  
Order Preference ◽  
Tool Diameter ◽  
Cutting Point

This study presents the optimization of machining parameters on ZE41 Mg alloy fabricated by gravity die casting and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Focus on the optimization of machining parameters using the technique to get minimum surface roughness, cutting force, thermal stress, residual stress, chip thickness and maximum MRR. A number of machining experiments were conducted based on the L27 orthogonal array on computer numerical control vertical machining center. The experiments were performed on ZE41 using cutting tool of an ISO 460. 1-1140-034A0-XM GC3 of 20, 25 and 30mm diameter with cutting point 140 degrees, for different cutting conditions. TOPSIS and ANOVA were used to work out the fore most important parameters cutting speed, feed rate, depth of cut and tool diameter which affect the response. The expected values and measured values are fairly close. Finally, the study for optimizing machining process is surveyed and results show improvement in real experiments.

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