Abstract
Surface roughness is the main indicator of technological performances of a component for electrical discharge machining (EDM). EDM process of manganese alloyed cold-work tool steel was modelled. In this paper we used the fuzzy logic (FL) and neural network (NN) to predict the effect of machining variables (discharge current and pulse duration) on the surface roughness of manganese alloyed cold-work tool steel in order to improve and increase its range of application. The experiments are carried out on manganese alloyed cold-work tool steel, processed with electrodes made of copper. The values of surface roughness predicted by these models are then compared. All models show good agreement with experimental results. When compared to the NN and FL models, the NN model has shown a significant forecast improvement. The results indicate that the NN model is an effective algorithm to forecast the surface roughness in EDM.
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 callMore From: International Journal of Recent advances in Mechanical Engineering
Disclaimer: 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.
