scholarly journals Prediction Model of Cutting Parameters for Turning High Strength Steel Grade-H: Comparative Study of Regression Model versus ANFIS

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Adel T. Abbas ◽  
Mohanad Alata ◽  
Adham E. Ragab ◽  
Magdy M. El Rayes ◽  
Ehab A. El Danaf
Keyword(s):  
Experimental Data ◽  
Surface Roughness ◽  
Regression Analysis ◽  
High Strength Steel ◽  
Removal Rate ◽  
High Strength ◽  
Cutting Parameters ◽  
Turning Operations ◽  
Cnc Turning ◽  
Accuracy And Repeatability

The Grade-H high strength steel is used in the manufacturing of many civilian and military products. The procedures of manufacturing these parts have several turning operations. The key factors for the manufacturing of these parts are the accuracy, surface roughness (Ra), and material removal rate (MRR). The production line of these parts contains many CNC turning machines to get good accuracy and repeatability. The manufacturing engineer should fulfill the required surface roughness value according to the design drawing from first trail (otherwise these parts will be rejected) as well as keeping his eye on maximum metal removal rate. The rejection of these parts at any processing stage will represent huge problems to any factory because the processing and raw material of these parts are very expensive. In this paper the artificial neural network was used for predicting the surface roughness for different cutting parameters in CNC turning operations. These parameters were investigated to get the minimum surface roughness. In addition, a mathematical model for surface roughness was obtained from the experimental data using a regression analysis method. The experimental data are then compared with both the regression analysis results and ANFIS (Adaptive Network-based Fuzzy Inference System) estimations.

Experimental Study on Milling Parameters Regression and Optimization of Super High Strength Steel 30CrMnSiNi2A

2012 ◽  
Vol 723 ◽  
pp. 293-298 ◽  
Author(s):  
Ming Chen ◽  
Cheng Dong Wang ◽  
Li Jiang ◽  
Qiu Lin Niu
Keyword(s):  
Surface Roughness ◽  
Regression Model ◽  
Cutting Force ◽  
High Strength Steel ◽  
Metal Removal ◽  
Removal Rate ◽  
Multiple Linear Regression Model ◽  
High Strength ◽  
Parameters Optimization ◽  
Milling Parameters

In this paper, effects of milling parameters on cutting force and surface roughness during symmetrical face dry milling process of super high strength steel 30CrMnSiNi2A were presented. Multiple linear regression model and orthogonal rotary quadratic regression model were established to analyze cutting force and surface roughness, respectively. Their adequacy was estimated by variance analysis and experimental data comparison. Parameters optimization for maximum metal removal rate and minimum surface roughness were also discussed.

scholarly journals A surrogate-assisted optimization approach for multi-response end milling of aluminum alloy AA3105

2020 ◽  
Vol 111 (9-10) ◽  
pp. 2419-2439
Author(s):  
Tamal Ghosh ◽  
Yi Wang ◽  
Kristian Martinsen ◽  
Kesheng Wang
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Material Removal Rate ◽  
Cutting Forces ◽  
Material Removal ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Data Driven ◽  
Optimal Cutting Parameters

Abstract Optimization of the end milling process is a combinatorial task due to the involvement of a large number of process variables and performance characteristics. Process-specific numerical models or mathematical functions are required for the evaluation of parametric combinations in order to improve the quality of the machined parts and machining time. This problem could be categorized as the offline data-driven optimization problem. For such problems, the surrogate or predictive models are useful, which could be employed to approximate the objective functions for the optimization algorithms. This paper presents a data-driven surrogate-assisted optimizer to model the end mill cutting of aluminum alloy on a desktop milling machine. To facilitate that, material removal rate (MRR), surface roughness (Ra), and cutting forces are considered as the functions of tool diameter, spindle speed, feed rate, and depth of cut. The principal methodology is developed using a Bayesian regularized neural network (surrogate) and a beetle antennae search algorithm (optimizer) to perform the process optimization. The relationships among the process responses are studied using Kohonen’s self-organizing map. The proposed methodology is successfully compared with three different optimization techniques and shown to outperform them with improvements of 40.98% for MRR and 10.56% for Ra. The proposed surrogate-assisted optimization method is prompt and efficient in handling the offline machining data. Finally, the validation has been done using the experimental end milling cutting carried out on aluminum alloy to measure the surface roughness, material removal rate, and cutting forces using dynamometer for the optimal cutting parameters on desktop milling center. From the estimated surface roughness value of 0.4651 μm, the optimal cutting parameters have given a maximum material removal rate of 44.027 mm3/s with less amplitude of cutting force on the workpiece. The obtained test results show that more optimal surface quality and material removal can be achieved with the optimal set of parameters.

OPTIMAL PREDICTION AND DESIGN OF SURFACE ROUGHNESS FOR CNC TURNING OF AL7075-T6 BY USING THE TAGUCHI HYBRID QPSO ALGORITHM

2016 ◽  
Vol 40 (5) ◽  
pp. 883-895 ◽  
Author(s):  
Wen-Jong Chen ◽  
Chuan-Kuei Huang ◽  
Qi-Zheng Yang ◽  
Yin-Liang Yang
Keyword(s):  
Surface Roughness ◽  
Particle Swarm Optimization ◽  
Predictive Models ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Cutting Parameters ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Cnc Turning ◽  
Multi Objective

This paper combines the Taguchi-based response surface methodology (RSM) with a multi-objective hybrid quantum-behaved particle swarm optimization (MOHQPSO) to predict the optimal surface roughness of Al7075-T6 workpiece through a CNC turning machining. First, the Taguchi orthogonal array L27 (36) was applied to determine the crucial cutting parameters: feed rate, tool relief angle, and cutting depth. Subsequently, the RSM was used to construct the predictive models of surface roughness (Ra, Rmax, and Rz). Finally, the MOHQPSO with mutation was used to determine the optimal roughness and cutting conditions. The results show that, compared with the non-optimization, Taguchi and classical multi-objective particle swarm optimization methods (MOPSO), the roughness Ra using MOHQPSO along the Pareto optimal solution are improved by 68.24, 59.31 and 33.80%, respectively. This reveals that the predictive models established can improve the machining quality in CNC turning of Al7075-T6.

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.

The effect of trace elements (Cu and Sn) on mechanical properties of steel castings

1980 ◽  
Vol 295 (1413) ◽  
pp. 125-125
Keyword(s):  
Mechanical Properties ◽  
Trace Elements ◽  
Regression Analysis ◽  
Experimental Work ◽  
High Strength Steel ◽  
Mechanical Performance ◽  
High Strength ◽  
Residual Elements ◽  
Steel Castings ◽  
Effect Of Copper

Because of the increase in the levels of residual elements in steel, a programme of work was initiated to determine the limits of copper and tin impurities that were tolerable in steel castings. A 1.5 % Mn—Mo steel was chosen as a base, since any effect of trace elements would be readily apparent in terms of mechanical performance in this medium—high strength steel. The effect of copper was investigated within the range < 0.01-0.5 %, and tin within the range < 0.01-0.26%. The results were analysed by using factorial analysis in the first instance and later, as the amount of experimental work expanded and more results became available, a regression analysis was used.

scholarly journals Cutting Mechanics and Surface Finish for Turning with Differently Shaped CBN Tools

2017 ◽  
Vol 64 (3) ◽  
pp. 347-357
Author(s):  
Krzysztof Żak
Keyword(s):  
Surface Roughness ◽  
Hard Turning ◽  
Cutting Parameters ◽  
Material Distribution ◽  
Roughness Parameters ◽  
Turning Operations ◽  
Cutting Power ◽  
Finish Turning ◽  
Corner Radius ◽  
Relevant Material

Abstract In this paper, the basic cutting characteristics such as cutting forces, cutting power and its distribution, specific cutting energies were determined taking into account variable tool corner radius ranging from 400 to 1200 µm and constant cutting parameters typical for hard turning of a hardened 41Cr4 alloy steel of 55±1 HRC hardness. Finish turning operations were performed using chamfered CBN tools. Moreover, selected roughness profiles produced for different tool corner radius were compared and appropriate surface roughness parameters were measured. The measured values of Ra and Rz roughness parameters are compared with their theoretical values and relevant material distribution curves and bearing parameters are presented.

Effects of Cutting Parameters on the Surface Roughness of Ti6Al4V Titanium Alloys in Side Milling

2011 ◽  
Vol 175 ◽  
pp. 289-293 ◽  
Author(s):  
Hao Liu ◽  
Chong Hu Wu ◽  
Rong De Chen
Keyword(s):  
Surface Roughness ◽  
Titanium Alloys ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Cutting Depth ◽  
Side Milling ◽  
Milling Parameters ◽  
Fine Grain

Side milling Ti6Al4V titanium alloys with fine grain carbide cutters is carried out. The influences of milling parameters on surface roughness are investigated and also discussed with average cutting thickness, material removal rate and vibration. The results reveal that the surface roughness increases with the increase of average cutting thickness and is primarily governed by the radial cutting depth.

scholarly journals Investigations on Surface Roughness and Tool Wear Characteristics in Micro-Turning of Ti-6Al-4V Alloy

Materials ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 2998 ◽  
Author(s):  
Kubilay Aslantas ◽  
Mohd Danish ◽  
Ahmet Hasçelik ◽  
Mozammel Mia ◽  
Munish Gupta ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Removal Rate ◽  
Cutting Speed ◽  
Small Diameter ◽  
Cutting Parameters ◽  
Ti6al4v Alloy ◽  
Depth Of Cut ◽  
Turning Process ◽  
Micro Turning

Micro-turning is a micro-mechanical cutting method used to produce small diameter cylindrical parts. Since the diameter of the part is usually small, it may be a little difficult to improve the surface quality by a second operation, such as grinding. Therefore, it is important to obtain the good surface finish in micro turning process using the ideal cutting parameters. Here, the multi-objective optimization of micro-turning process parameters such as cutting speed, feed rate and depth of cut were performed by response surface method (RSM). Two important machining indices, such as surface roughness and material removal rate, were simultaneously optimized in the micro-turning of a Ti6Al4V alloy. Further, the scanning electron microscope (SEM) analysis was done on the cutting tools. The overall results depict that the feed rate is the prominent factor that significantly affects the responses in micro-turning operation. Moreover, the SEM results confirmed that abrasion and crater wear mechanism were observed during the micro-turning of a Ti6Al4V alloy.

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