AGILE MODELING AND OPTIMIZATION OF END MILLING

2009 ◽  
Vol 08 (01) ◽  
pp. 71-80
Author(s):  
K. HANS RAJ ◽  
RAHUL SWARUP SHARMA ◽  
VIKAS UPADHYAY ◽  
ALOK K. VERMA
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
End Milling ◽  
Fuzzy Model ◽  
Stochastic Search ◽  
Average Surface Roughness ◽  
Search Technique ◽  
Machining Time ◽  
Neuro Fuzzy ◽  
Stochastic Search Technique

The rising demand for precision and quality in manufacturing necessitates that vast amounts of manufacturing knowledge be incorporated in manufacturing systems. Surface finish in end milling depends upon a number of variables such as cutting speed, feed rate, spindle speed, radial depth of cut, etc. The relative effect of these variables on surface roughness and machining time is quite considerable. A complex relationship exists between these process parameters and hence there is a need to develop models which can capture this complex interrelationship and enable fast computation of the average surface roughness and machining time based on process parameters. Neuro Fuzzy (NF) modeling has gained prominence recently on account of its fast reaction times, improved ease of operation and flexibility to respond to change in process parameters. In the present work, initially a Neuro Fuzzy Model is trained with experimental results of end milling. Subsequently, a generic approach is developed for optimization of end milling where the applicability and effectiveness of Neuro Fuzzy Model for function approximation is used to rapidly estimate average surface roughness and machining time in an integrated framework of Hybrid Stochastic Search Technique (HSST) to form a Neuro Fuzzy Hybrid Stochastic Search Technique (NFHSST). The results indicate that the NFHSST heuristic converges to better solutions rapidly as it provides the values of various process parameters for optimizing the objectives in a single run. Thus, NFHSST assists in the improvement of quality by developing multiple sound parts in an agile manner.

Fuzzy Modeling to Simulate Surface Roughness in CNC Turning of AISI 1045 Steel

2011 ◽  
Vol 486 ◽  
pp. 262-265
Author(s):  
Amit Kohli ◽  
Mudit Sood ◽  
Anhad Singh Chawla
Keyword(s):  
Experimental Data ◽  
Surface Roughness ◽  
Process Parameters ◽  
Fuzzy Model ◽  
Fuzzy Modeling ◽  
Cnc Machine ◽  
Model Based ◽  
Aisi 1045 Steel ◽  
Aisi 1045 ◽  
1045 Steel

The objective of the present work is to simulate surface roughness in Computer Numerical Controlled (CNC) machine by Fuzzy Modeling of AISI 1045 Steel. To develop the fuzzy model; cutting depth, feed rate and speed are taken as input process parameters. The predicted results are compared with reliable set of experimental data for the validation of fuzzy model. Based upon reliable set of experimental data by Response Surface Methodology twenty fuzzy controlled rules using triangular membership function are constructed. By intelligent model based design and control of CNC process parameters, we can enhance the product quality, decrease the product cost and maintain the competitive position of steel.

EFFECT OF ABRASIVE TYPE ON THE SURFACE ROUGHNESS AND MRR IN MAF OF AISI 304 STEEL

2021 ◽  
Author(s):  
MAHMUT ÇELIK ◽  
HAKAN GÜRÜN ◽  
ULAŞ ÇAYDAŞ
Keyword(s):  
Surface Roughness ◽  
Removal Rate ◽  
Three Dimensional ◽  
Steel Plates ◽  
304 Stainless Steel ◽  
Average Surface Roughness ◽  
Machining Time ◽  
Aisi 304 ◽  
Average Surface ◽  
Different Levels

In this study, the effects of experimental parameters on average surface roughness and material removal rate (MRR) were experimentally investigated by machining of AISI 304 stainless steel plates by magnetic abrasive finishing (MAF) method. In the study in which three different abrasive types were used (Al2O3, B4C, SiC), the abrasive grain size was changed in two different levels (50 and 80[Formula: see text][Formula: see text]m), while the machining time was changed in three different levels (30, 45, 60[Formula: see text]min). Surface roughness values of finished surfaces were measured by using three-dimensional (3D) optical surface profilometer and surface topographies were created. MRRs were measured with the help of precision scales. The abrasive particles’ condition before and after the MAF process was examined and compared using a scanning electron microscope. As a result of the study, the surface roughness values of plates were reduced from 0.106[Formula: see text][Formula: see text]m to 0.028[Formula: see text][Formula: see text]m. It was determined that the best parameters in terms of average surface roughness were 60[Formula: see text]min machining time with 50[Formula: see text][Formula: see text]m B4C abrasives, while the best result in terms of MRR was taken in 30[Formula: see text]min with 50[Formula: see text][Formula: see text]m SiC abrasives.

scholarly journals Stochastic Search Technique with Variable Deterministic Constraints for the Estimation of Induction Motor Parameters

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 273
Author(s):  
Carmenza Moreno Roa ◽  
Adolfo Andrés Jaramillo Matta ◽  
Juan David Bastidas Rodríguez
Keyword(s):  
Experimental Data ◽  
Induction Motor ◽  
Stochastic Search ◽  
The Other ◽  
Objective Functions ◽  
Search Technique ◽  
Single Cage ◽  
New Strategy ◽  
A New Technique ◽  
Stochastic Search Technique

This paper deals with the implementation of a new technique of stochastic search to find the best set of parameters in a mathematical model, applied to the single cage (SC) model of the induction motor (IM). The technique includes a new strategy to generate variable constraints of the domain, seven error functions, weight for the operating zones of the IM, and multi-objective functions. The results are validated with experimental data of the torque and current in an IM, and show better fitting to the experimental curves compared with the results of two different techniques, one deterministic and the other one stochastic. The results obtained allow us to conclude that the best set of parameters for the model depends on the weights assigned to the objective functions and to the operating zones.

scholarly journals Study of the cutting process parameters that influence the surface quality machined by end milling of the aluminum alloy

2019 ◽  
Vol 290 ◽  
pp. 02010
Author(s):  
Alina Bianca Pop ◽  
Aurel Mihail Țîțu
Keyword(s):  
Surface Roughness ◽  
Industrial Organization ◽  
Aluminum Alloy ◽  
Process Parameters ◽  
Applied Research ◽  
Cutting Tool ◽  
End Milling ◽  
Motion Direction ◽  
Experimental Planning ◽  
Aeronautical Industry

This research aims to carry out an elaborate experiment by witch resulting in relevant conclusions that have practical applicability in the aeronautical industry. The surface roughness measured transversely and longitudinally on the feed motion direction of the cutting tool constitutes the dedicated objective function on which the study was conducted in this case. The end milling was chosen of an aluminum alloy used explicitly in the aeronautical industry. The actual experiments were carried out in the only aeronautical industry in Romania carrying out these types of machining and were made according to the methodology with rigorous experimental planning of the research. The experimental plan conceived after which the practical experiments were conducted led to applied research already put into practice within the above-mentioned industrial organization.

Optimization of Process Parameters for Cutting Force for Aluminium 7010 in CNC End Milling Process

2018 ◽  
Vol 10 (6) ◽  
Author(s):  
M. Kishanth ◽  
P. Rajkamal ◽  
D. Karthikeyan ◽  
K. Anand
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Process Parameters ◽  
End Milling ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
Optimization Of Process Parameters ◽  
Cnc End Milling ◽  
End Milling Process

In this paper CNC end milling process have been optimized in cutting force and surface roughness based on the three process parameters (i.e.) speed, feed rate and depth of cut. Since the end milling process is used for abrading the wear caused is very high, in order to reduce the wear caused by high cutting force and to decrease the surface roughness, the optimization is much needed for this process. Especially for materials like aluminium 7010, this kind of study is important for further improvement in machining process and also it will improve the stability of the machine.

Optimization of Machining Condition in WEDM for Titanium Grade 6 Using MOORA Coupled with PCA — A Multivariate Hybrid Approach

2017 ◽  
Vol 16 (02) ◽  
pp. 81-99 ◽  
Author(s):  
Himadri Majumder ◽  
Kalipada Maity
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Hybrid Approach ◽  
Cutting Speed ◽  
Kerf Width ◽  
Average Surface Roughness ◽  
Average Surface ◽  
Titanium Grade ◽  
Pulse On Time ◽  
Pulse Off Time

This paper represents a multivariate hybrid approach, combining Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) and Principal Component Analysis (PCA) to optimize different correlated responses during Wire Electrical Discharge Machining (WEDM) process of titanium grade 6. The response parameters selected are the average cutting speed, average Kerf width and average surface roughness (Ra). All of them have been studied in terms of pulse-ON time, pulse-OFF time, wire feed and wire tension. As indicated by Taguchi’s signal-to-noise ratio, the optimum process parameters were achieved for the desired average cutting speed, average Kerf width and average surface roughness, respectively. At last, the optimum combination of process parameters was validated by affirmation test which gave considerably improved various quality characteristics. Confirmation test outcome revealed that multivariate hybrid approach MOORA coupled with PCA was a competent strategy to decide available cutting parameters for a desired response quality for WEDM of titanium grade 6.

Research of LS-WEDM Finishing in Gas Based on Gray Relation Grade

2013 ◽  
Vol 274 ◽  
pp. 74-77
Author(s):  
Yu Mei Lu ◽  
Tong Wang ◽  
Ling He ◽  
Wen Wen Yan ◽  
Shi Cai Fang
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Supply Voltage ◽  
Orthogonal Experiment ◽  
Relevance Theory ◽  
Processing Parameters ◽  
Pulse Interval ◽  
Power Supply Voltage ◽  
Machining Time ◽  
Wedm Process

Authors have presented a new procedure as gas-liquid combined multiple cut. In order to acquire the best machining quality and the greatest efficiency, it is necessary to analyze the WEDM process with multiple performance characteristics including machining time and surface roughness and to optimize some processing parameters such as pulse duration, pulse interval, peak current, main power supply voltage, servo feed, offset and servo voltage. The orthogonal experiment is designed to reveal the relationship among the parameters, the gray relevance theory is used to optimize the processing parameters under the multiple process index of the LS-WEDM in gas, namely optimizing the process parameters under the surface roughness and machining time, and the optimized process parameters can be obtained from gray relation grade.

MULTI-PARAMETER ANN MODEL FOR FLAT-END MILLING

2008 ◽  
Vol 32 (3-4) ◽  
pp. 523-536 ◽  
Author(s):  
Hazim El Mounayri ◽  
M. Affan Badar ◽  
Gustavo A. Rengifo
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Cutting Force ◽  
Process Parameters ◽  
End Milling ◽  
Predictive Ability ◽  
Ann Model ◽  
Network Output ◽  
Input Variables ◽  
Artificial Neural Network Ann

The quality, productivity and safety of machining can be significantly improved through the optimization of cutting conditions. The first step in achieving such an objective is the development of accurate and reliable models for predicting the critical process parameters. In this paper, an innovative Artificial Neural Network (ANN) model that predicts both cutting force and surface roughness in end milling is developed and validated. A set of five input variables is selected to represent the machining conditions while twelve quantities representing two key process parameters, namely, cutting force and surface roughness, form the variables of the network output. Full factorial design of experiments is used to generate data for both training and validation. Successful training of the neural network is demonstrated through comparison of simulated and experimental results for four different output variables, namely cutting force, surface roughness, feed marks, and tooth passing frequency. The predictive ability of the model is verified experimentally by comparing simulated output variables with their experimental counterparts. A good agreement is observed.

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