scholarly journals Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

Materials ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2070 ◽  
Author(s):  
Ireneusz Zagórski ◽  
Monika Kulisz ◽  
Mariusz Kłonica ◽  
Jakub Matuszak
Keyword(s):  
Neural Networks ◽  
Cutting Force ◽  
Magnesium Alloys ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Milling Process ◽  
Machining Parameters ◽  
Cutting Force Components ◽  
Multilayered Perceptron ◽  
Force Components

This paper set out to investigate the effect of cutting speed vc and trochoidal step str modification on selected machinability parameters (the cutting force components and vibration). In addition, for a more detailed analysis, selected surface roughness parameters were investigated. The research was carried out for two grades of magnesium alloys—AZ91D and AZ31—and aimed to determine stable machining parameters and to investigate the dynamics of the milling process, i.e., the resulting change in the cutting force components and in vibration. The tests were performed for the specified range of cutting parameters: vc = 400–1200 m/min and str = 5–30%. The results demonstrate a significant effect of cutting data modification on the parameter under scrutiny—the increase in vc resulted in the reduction of the cutting force components and the displacement and level of vibration recorded in tests. Selected cutting parameters were modelled by means of Statistica Artificial Neural Networks (Radial Basis Function and Multilayered Perceptron), which, furthermore, confirmed the suitability of neural networks as a tool for prediction of the cutting force and vibration in milling of magnesium alloys.

On-line wear estimation using neural networks

1998 ◽  
Vol 212 (2) ◽  
pp. 105-112 ◽  
Author(s):  
A Ghasempoor ◽  
T N Moore ◽  
J Jeswiet
Keyword(s):  
Neural Networks ◽  
Tool Wear ◽  
Cutting Force ◽  
Machining Parameters ◽  
Dynamic Neural Networks ◽  
Line Wear ◽  
Cutting Force Components ◽  
On Line ◽  
Force Components ◽  
Share Information

In this paper, a neural network based system for ‘on-line’ estimation of tool wear in turning operations is introduced. The system monitors the cutting force components and extracts the tool wear information from the changes occurring over the cutting process. A hierarchical structure using multilayered feedforward static and dynamic neural networks is used as a specialized subsystem, for each wear component to be monitored. These subsystems share information about the tool wear components they are monitoring and their error in estimating the cutting force components is used to update the dynamic neural networks. The adaptability property of neural networks ensures that changes in machining parameters can be accommodated. Simulation studies are undertaken using experimental data available from manufacturing literature. The results are promising and show good estimation ability.

Modelling and Analysis of Relationship between Cutting Parameters Surface Roughness and Cutting Forces Using Response Surface Methodology when Hard Turning with Coated Ceramic Inserts

2016 ◽  
Vol 686 ◽  
pp. 19-26 ◽  
Author(s):  
Ildikó Maňková ◽  
Marek Vrabeľ ◽  
Jozef Beňo ◽  
Mária Franková
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Cutting Force ◽  
Response Surface ◽  
Hard Turning ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Oxide Ceramic ◽  
Cutting Force Components ◽  
Force Components

Experimental research and modeling in the field of turning hardened bearing steel with hardness of 62 HRC using TiN coated mixed oxide ceramic inserts is presented. The main objective of the article is investigation the relationship between cutting parameters (cutting speed and feed rate) and output machining variables (surface roughness and cutting force components) through the response surface methodology (RSM). The mathematical model of the effect of process parameters on the cutting force components and surface roughness is presented. Moreover, the influence of TiN coating on above mentioned variables was monitored. The design of experiment according to Taguchi L9 orthogonal matrix (32) was applied for trials. Pearson´s correlation matrix was used to examine the dependence between the factors (f, vc) and the machining variables (surface roughness and cutting force components). The results show how much surface roughness and cutting force components is influenced by cutting speed and feed in hard turning with coated ceramics.

Artificial neural networks back propagation algorithm for cutting force components predictions

2013 ◽  
Vol 14 (6) ◽  
pp. 431-439 ◽  
Author(s):  
Issam Hanafi ◽  
Francisco Mata Cabrera ◽  
Abdellatif Khamlichi ◽  
Ignacio Garrido ◽  
José Tejero Manzanares
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cutting Force ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Cutting Force Components ◽  
Artificial Neural ◽  
Force Components

scholarly journals Experiments in the Machining of Ice at Negative Rake Angles

1984 ◽  
Vol 30 (104) ◽  
pp. 77-81 ◽  
Author(s):  
D.K. Lieu ◽  
C.D. Mote
Keyword(s):  
Cutting Force ◽  
Chip Formation ◽  
Cutting Tool ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Rake Angle ◽  
Cutting Depth ◽  
Orthogonal Machining ◽  
Cutting Force Components ◽  
Force Components

AbstractThe cutting force components and the cutting moment on the cutting tool were measured during the orthogonal machining of ice with cutting tools inclined at negative rake angles. The variables included the cutting depth (< 1 mm), the cutting speed (0.01 ms−1to 1 ms−1), and the rake angles (–15° to –60°). Results of the experiments showed that the cutting force components were approximately independent of cutting speed. The resultant cutting force on the tool was in a direction approximately normal to the cutting face of the tool. The magnitude of the resultant force increased with the negative rake angle. Photographs of ice-chip formation revealed continuous and segmented chips at different cutting depths.

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).

Metallurgical methods of improving the machinability of structural steels to increase the productivity of processing in the conditions of automated production

2021 ◽  
pp. 51-54
Author(s):  
Keyword(s):  
Structural Steel ◽  
Cutting Force ◽  
Tool Life ◽  
Cutting Speed ◽  
Structural Steels ◽  
Automated Production ◽  
Metallic Inclusions ◽  
Cutting Force Components ◽  
Force Components

The influence of non-metallic inclusions on the main indicators of steel machinability is investigated. The influence of non-metallic inclusions on the cutting force is determined. Generalized formulas for calculating tool life, cutting speed and cutting force components are proposed. Keywords: machinability, productivity, structural steel, non-metallic inclusions. [email protected]

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