Application of Genetic Algorithm for the Optimization of Process Parameters in Keyway Milling
The aim of this work is to develop an integrated study of surface roughness for modeling and optimization of cutting parameters during end milling operation of C40 steel with HSS tools under wet condition. The experimentation is carried out using full fac
- PDF / 464,953 Bytes
- 16 Pages / 439.37 x 666.142 pts Page_size
- 112 Downloads / 203 Views
Abstract The aim of this work is to develop an integrated study of surface roughness for modeling and optimization of cutting parameters during end milling operation of C40 steel with HSS tools under wet condition. The experimentation is carried out using full factorial design (three factor depth of cut, feed and spindle speed and three level). Artificial neural network (ANN) based on Back-propagation (BP) learning algorithm is used to construct the surface roughness model and second-order response surface model for the surface roughness is developed using Response surface methodology. By analysis three different surface curves it can be concluded that the minimum surface roughness (2.1779 µm) will be achieved when spindle speed, feed and depth of cut are 486 rpm, 46 mm/min and 0.31 mm respectively. Optimum parameters are obtained using GA, is near about same as value of optimum parameters obtained using RSM so it is concluded that RSM method is verified by GA Optimization.
Keywords End milling Surface roughness Design of experiment neural network Response surface methodology Genetic algorithm
Artificial
1 Introduction For any manufacturing company higher productivity as well as very good quality is primary concern. End milling, a machining process is commonly used in industry due to its ability to remove material faster giving reasonably good surface quality, S.C. Mondal (&) P. Mandal G. Ghosh Department of Mechanical Engineering, Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711103, India e-mail: [email protected] P. Mandal e-mail: [email protected] G. Ghosh e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2017 A. Chakrabarti and D. Chakrabarti (eds.), Research into Design for Communities, Volume 1, Smart Innovation, Systems and Technologies 65, DOI 10.1007/978-981-10-3518-0_7
71
72
S.C. Mondal et al.
high efficiency and the accuracy of the cutting surface. The end milling is used for the production of slots for keyway, pockets and dies, where quality is an important factor. The dimensional accuracy and surface roughness of products are always given to the primary attention by the industry. The quality of the manufactured parts not only depends on their geometries but also their surfaces textures such as roughness and waviness. Surface roughness affects friction, wear, fatigue, corrosion, and electrical and thermal conductivity [1]. The influence of cutting parameters (spindle speed, feed and depth of cut) on surface roughness is discussed in this study. The surface roughness depends on the various process parameters and machining condition. Due to its nonlinearity, the analytical approach for its modeling is very difficult. Different theoretical models have been proposed so far by the various researchers but these types of models are not accurate enough for wide range of cutting conditions. So, there is a need for a tool that can accurately predict the surface roughness of a product and also select optimum machining parameters. Mansour an
Data Loading...
