Abstract
CNC lathe is one of the best machining techniques which provides us with better accuracy and precision. Considering speed, feed and depth of cut as inputs and among all possible outputs, in the present work Material Removal Rate and Surface Roughness would be considered as the factors those affect the quality, machining time and cost of machining. Design of experiments (DOE) would be carried out in order to minimize the number of experiments. In the later stages application of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) would be used for the Optimization in the advanced manufacturing considering CNC lathe. The obtained output would be minimized (for surface roughness) and maximized (for MRR) using Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO). The combination of various input parameters for the same would be identified and a comparison would be drawn with the various above methods.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
