Fuzzy and Regression Modeling for Nd: YAG Laser Cutting of Ti-6Al-4V Superalloy Sheet

Titanium super alloys are known as one of the difficult-to-cut materials through conventional machining processes, although it has superior characteristics. As well, laser cutting process is highly non-linear complex process which involves several process variables. With attention of many process va...

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Bibliographic Details
Published in:Journal for Manufacturing Science and Production 2016-09, Vol.16 (3), p.153-162
Main Authors: Rajamani, D., Tamilarasan, A.
Format: Article
Language:English
Subjects:
Aluminum
box-behnken design
Design of experiments
Energy
Fuzzy logic
kerf deviation
laser cutting
Lasers
Mathematical analysis
Mathematical models
Mechanical engineering
metal removal rate
Process planning
Regression analysis
regression model
Studies
Superalloys
Titanium alloys
Variance analysis
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Summary:Titanium super alloys are known as one of the difficult-to-cut materials through conventional machining processes, although it has superior characteristics. As well, laser cutting process is highly non-linear complex process which involves several process variables. With attention of many process variables, it is difficult to develop a precise functional relationship between input and output variables. Therefore, the aim of present work to predict the performance characteristics of kerf deviation and metal removal rate on the Nd-YAG laser cutting of Titanium (Ti-6Al-4V) super alloy sheet using fuzzy and regression modeling techniques. The pulse width, pulse energy, cutting speed and gas pressure were considered as process state variables. The experiments were conducted using RSM based box-behnken design methodology. A fuzzy rule based models were developed to predict the responses. The predicted fuzzy and regression results were compared and examined with experimental results. It is remarkable that, obtained R and average error values for each response are very consistent with small variations. Thus, the developed fuzzy model can be effectively used to predict laser cutting parameters in automated manufacturing environments to reduce the complexity of process planning activities.
ISSN:2191-4184
0793-6648
2191-0375
DOI:10.1515/jmsp-2016-0010