Evaluation of sustainability of mould steels based on machinability data

2010 ◽  
Vol 7 (1/2) ◽  
pp. 58 ◽  
Author(s):  
I. Zaghbani ◽  
V. Songmene ◽  
G. Kientzy ◽  
H. Lehuy
Keyword(s):  
Machinability Data

Single Input Multi Output Adaptive Network Based Fuzzy Inference System for Machinability Data Selection in Turning Operations

2011 ◽  
Vol 383-390 ◽  
pp. 1062-1070
Author(s):  
Adeel H. Suhail ◽  
N. Ismail ◽  
S.V. Wong ◽  
N.A. Abdul Jalil
Keyword(s):  
Surface Roughness ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Cutting Parameters ◽  
Machining Process ◽  
Data Selection ◽  
Adaptive Network ◽  
Inference System ◽  
Single Input ◽  
Machinability Data

The selection of machining parameters needs to be automated, according to its important role in machining process. This paper proposes a method for cutting parameters selection by fuzzy inference system generated using fuzzy subtractive clustering method (FSCM) and trained using an adaptive network based fuzzy inference system (ANFIS). The desired surface roughness (Ra) was entered into the first step as a reference value for three fuzzy inference system (FIS). Each system determine the corresponding cutting parameters such as (cutting speed, feed rate, and depth of cut). The interaction between these cutting parameters were examined using new sets of FIS models generated and trained for verification purpose. A new surface roughness value was determined using the cutting parameters resulted from the first steps and fed back to the comparison unit and was compared with the desired surface roughness and the optimal cutting parameters ( which give the minimum difference between the actual and predicted surface roughness were find out). In this way, single input multi output ANFIS architecture presented which can identify the cutting parameters accurately once the desired surface roughness is entered to the system. The test results showed that the proposed model can be used successfully for machinability data selection and surface roughness prediction as well.

Evaluation of Machinability Data

1994 ◽  
Vol 22 (3) ◽  
pp. 204 ◽  
Author(s):  
DR Petersen ◽  
L-Z Jin ◽  
R Sandström
Keyword(s):  
Machinability Data

Sequential Estimation of Machinability Parameters for Adaptive Optimization of Machinability Data Base Systems

1985 ◽  
Vol 107 (2) ◽  
pp. 159-166 ◽  
Author(s):  
P. Balakrishnan ◽  
M. F. DeVries
Keyword(s):  
Data Base ◽  
Tool Life ◽  
Prior Information ◽  
Sequential Estimation ◽  
Estimation Procedure ◽  
Model Parameters ◽  
Mathematical Tool ◽  
Adaptive Optimization ◽  
Life Data ◽  
Machinability Data

Mathematical model type machinability data base systems require suitable model building procedures to estimate the model parameters. The estimation procedure should be capable of using subjective prior information about the models and must also be capable of adapting the model parameters to the particular machining environment for which the data are needed. In this paper, the sequential Maximum A Posteriori (MAP) estimation procedure is proposed as the mathematical tool for performing these functions. Mathematical details of this estimation procedure are presented. The advantages of this method over conventional regression analysis are discussed based on the analysis of an experimental tool life data set. Details regarding the selection of the various initial values needed for starting the sequential procedure are presented. The use of prior information about the models in order to improve the parameter estimates is investigated. The adaptive capability of the procedure is analyzed using simulated tool life data. The results of this analysis indicate that the proposed sequential estimation procedure is a valuable tool for estimating machinability parameters and for the adaptive optimization of machinability data base systems.

Statistical Wear Model for Adhesively Bonded Tools

2011 ◽  
Vol 264-265 ◽  
pp. 1802-1811
Author(s):  
S.M. Darwish ◽  
Ali M. Al Samhan ◽  
H.A. Helmy
Keyword(s):  
Statistical Models ◽  
Cutting Speed ◽  
Computer Integrated Manufacturing ◽  
Depth Of Cut ◽  
Data Systems ◽  
Optimum Conditions ◽  
Adhesively Bonded ◽  
Wear Model ◽  
Machinability Data ◽  
Selection Of

Computerized machinability data systems are essential for the selection of optimum conditions during process planning, and they form an important component in the implementation of computer integrated manufacturing (CIM) systems. Since statistical models for adhesively bonded tools are unavailable, the present paper presents a study of the development of a tool life, surface roughness and cutting force models for turning constructional steels, using adhesively bonded tools. These models are developed in terms of cutting speed, federate and depth of cut. These variables are investigated using design of experiments and utilization of response surface methodology (RMS).

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