Analysis the effectiveness of cryogenic treatment through roughness and temperature prediction using bonn technique / Manjunath.S and Ajay Kumar

• Main Authors: S., Manjunath (Author), Ajay, Kumar (Author)
• Format: Article
• Language: English
• Published: Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM), 2017.
• Subjects: Temperature measurements; Energy conservation; Article
• Online Access: Get fulltext; View Fulltext in UiTM IR

 Surface roughness is one of the major factor affecting the work piece surface finish in face milling operation. The main criteria discussed on this paper is to predict the ideal cutting performance of cryogenic treatment tool in surface roughness optimization. The surface roughness in machining process is generally formed by the irregularities in cutting tool properties like strength, hardness, toughness etc. For the optimization process, the cutting tool is initially subjected to cryogenic treatment for the improvement in the tool property. The cryogenic treatment is cold treatment process at low temperature to increase the material property. In this paper, the face milling operation processed out in the cryo treated tool which can lower the surface roughness of the machined material. For the validation of the machined work carried out in this paper is verified theoretically by the developing a foreboding prototype by Bat Optimization based artificial Neural Network (BONN) technique using a real time experimental data. The gathered outcome is executed in mathematical modelling using Mat lab and the result shows that cryogenic treatment tool is more efficient than untreated tool with higher cutting accuracy and tool life. Thus, the bat algorithm coupled with artificial neural network is a dynamic and specific method in advancing the overall least possible method for surface roughness prediction in face milling operations.