An Artificial Intelligence Approach for the Prediction of Surface Roughness in Co2 Laser Cutting

نویسندگان

  • MILOŠ MADIĆ
  • MIROSLAV RADOVANOVIĆ
چکیده

In laser cutting, the cut quality is of great importance. Multiple non-linear effects of process parameters and their interactions make very difficult to predict cut quality. In this paper, artificial intelligence (AI) approach was applied to predict the surface roughness in CO2 laser cutting. To this aim, artificial neural network (ANN) model of surface roughness was developed in terms of cutting speed, laser power and assist gas pressure. The experimental results obtained from Taguchi’s L25 orthogonal array were used to develop ANN model. The ANN mathematical model of surface roughness was expressed as explicit nonlinear function of the selected input parameters. Statistical results indicate that the ANN model can predict the surface roughness with good accuracy. It was showed that ANNs may be used as a good alternative in analyzing the effects of cutting parameters on the surface roughness.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Comparative modeling of CO2 laser cutting using multiple regression analysis and artificial neural network

In this paper, empirical modeling of surface roughness in CO2 laser cutting of mild steel using the multiple regression analysis (MRA) and artificial neural network (ANN) was presented. To cover wider range of laser cutting parameters such as cutting speed, laser power and assist gas pressure as well as to obtain experimental database for MRA and ANN model development, Taguchi’s L25 orthogonal ...

متن کامل

Prediction of Surface Roughness by Hybrid Artificial Neural Network and Evolutionary Algorithms in End Milling

Machining processes such as end milling are the main steps of production which have major effect on the quality and cost of products. Surface roughness is one of the considerable factors that production managers tend to implement in their decisions. In this study, an artificial neural network is proposed to minimize the surface roughness by tuning the conditions of machining process such as cut...

متن کامل

On the use of back propagation and radial basis function neural networks in surface roughness prediction

Various artificial neural networks types are examined and compared for the prediction of surface roughness in manufacturing technology. The aim of the study is to evaluate different kinds of neural networks and observe their performance and applicability on the same problem. More specifically, feed-forward artificial neural networks are trained with three different back propagation algorithms, ...

متن کامل

Application of orthogonal array technique and particle swarm optimization approach in surface roughness modification when face milling AISI1045 steel parts

Face milling is an important and common machining operation because of its versatility and capability to produce various surfaces. Face milling is a machining process of removing material by the relative motion between a work piece and rotating cutter with multiple cutting edges. It is an interrupted cutting operation in which the teeth of the milling cutter enter and exit the work piece during...

متن کامل

A new approach to enhance final surface quality in drilling operation: evaluation of using alumina micro-particle additives on oil-water emulsion cutting fluid

Reaming is a common finishing process for improving the drilled holes surface quality. Choosing an appropriate finishing method in drilling process has a significant effect on the surface quality of holes and in decreasing the process total cost and time. In this study, four similar holes were drilled on the AISI 4340 workpiece with different two pair feed rates. The drilling process was perfor...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2013