Simulation tool for prediction of cutting forces and surface quality of micro-milling processes

• Published in: International journal of advanced manufacturing technology Vol. 99; no. 1-4; pp. 225 - 232
• Main Authors: Schützer, Klaus, da Silva de Luca Ramos, Luciana Wasnievski, Mewis, Jan, Tamborlin, Marcelo Octavio, Baldo, Crhistian Raffaelo
• Format: Journal Article
• Language: English
• Published: London Springer London 01-10-2018; Springer Nature B.V
• Subjects: CAE) and Design; Computer simulation; Computer-Aided Engineering (CAD; Cutting fluids; Cutting force; Cutting forces; Cutting parameters; Dynamic models; Engineering; Impact factors; Industrial and Production Engineering; Mechanical Engineering; Media Management; Milling (machining); Model accuracy; Original Article; Predictions; Surface properties; Workpieces; Micro-milling; Virtual machining; Surface quality; Cutting forces
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-018-2517-5

The improvement of micro-milling processes implies the application of advanced analysis and modeling techniques to derive a deeper process understanding. Because of micro-scale effects, monitoring, and measurement systems applied in conventional milling are in most cases not suitable for identifying optimal cutting conditions. Therefore, analytical and mechanical models have been developed in recent years to account for impact factors dominating the micro-milling errors. Within the research presented in this publication, geometric, kinematic, and dynamic models have been adjusted and dimensioned according to the dominating impact factors in micro-milling and have been consolidated to enable for a time-domain simulation. The effect of element size of discretized workpiece and tool as well as the time step size on cutting forces has been evaluated. The accuracy of predicting cutting forces has been investigated and a good agreement of measured and simulated cutting forces has been found. Finally, a mold for a micro-fluidic device has been machined virtually and experimentally to evaluate the accuracy of the integrated models in predicting the final quality of a micro-milled part in terms of surface quality parameters.