Modeling Cutting Forces in High-Speed Turning using Artificial Neural Networks

Cutting forces are very important variables in machining performance because they affect surface roughness, cutting tool life, and energy consumption. Reducing electrical consumption manufacturing processes not only provides economic benefits to manufacturers but also improves their environmental performance. Many factors, such as material, speed, time, have an impact on Recently, many studies investigated the of machine tools; however, a few examined high-speed turning plain carbon steel. This paper seeks analyze effects materials speed Specific Energy Consumption (SEC) during dry AISI 1045 For this purpose, were experimentally measured compared with estimates predictive models developed using polynomial regression artificial neural networks. The resulting evaluated based two metrics: coefficient determination root mean square error. According results, did reach 70 % representation variability data. time associated highest lowest SEC CT5015-P10 GC4225-P25 inserts calculated. values these tools obtained at medium speed. Also, GC4225 insert was found be higher than that CT5015 tool.