%0 Journal Article %1 Kovacic:2004:JMPT %A Kovacic, M. %A Balic, J. %A Brezocnik, M. %D 2004 %J Journal of Materials Processing Technology %K Milling algorithms, cutting forces genetic prediction programming, %P 1647--1652 %R doi:10.1016/j.jmatprotec.2004.04.318 %T Evolutionary approach for cutting forces prediction in milling %U http://www.sciencedirect.com/science/article/B6TGJ-4CHS1T2-9/2/ca7fc5b9431830386384db78e7ee233a %V 155-156 %X Knowing cutting forces is important for choosing cutting parameters for milling. Traditionally, cutting forces are calculated by equation which includes empirically measured specific cutting forces. In the article modelling of cutting forces with genetic programming is proposed, which imitates principles of living beings. Measurements have been made for two materials (aluminium alloy AlMgSi1 and steel 1.2343) and two different types of milling (conventional milling and STEP milling). For each material and type of milling parameters, tensile strength and hardness of workpiece, tool diameter, cutting depth, spindle speed, feeding and type of milling were monitored, and for each combination of milling parameters cutting forces were measured. On the basis of the experimental data, different models for cutting forces prediction were obtained by genetic programming. Research shows that genetically developed models fit the experimental data.

@article{Kovacic:2004:JMPT, abstract = {Knowing cutting forces is important for choosing cutting parameters for milling. Traditionally, cutting forces are calculated by equation which includes empirically measured specific cutting forces. In the article modelling of cutting forces with genetic programming is proposed, which imitates principles of living beings. Measurements have been made for two materials (aluminium alloy AlMgSi1 and steel 1.2343) and two different types of milling (conventional milling and STEP milling). For each material and type of milling parameters, tensile strength and hardness of workpiece, tool diameter, cutting depth, spindle speed, feeding and type of milling were monitored, and for each combination of milling parameters cutting forces were measured. On the basis of the experimental data, different models for cutting forces prediction were obtained by genetic programming. Research shows that genetically developed models fit the experimental data.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Kovacic, M. and Balic, J. and Brezocnik, M.}, biburl = {https://www.bibsonomy.org/bibtex/21d016ce14297dac41282cfb57e21faa1/brazovayeye}, doi = {doi:10.1016/j.jmatprotec.2004.04.318}, interhash = {202270fe8438368441b2174ddaa66bf7}, intrahash = {1d016ce14297dac41282cfb57e21faa1}, issn = {0924-0136}, journal = {Journal of Materials Processing Technology}, keywords = {Milling algorithms, cutting forces genetic prediction programming,}, month = {30 November}, notes = {Proceedings of the International Conference on Advances in Materials and Processing Technologies: Part 2}, owner = {wlangdon}, pages = {1647--1652}, timestamp = {2008-06-19T17:43:45.000+0200}, title = {Evolutionary approach for cutting forces prediction in milling}, url = {http://www.sciencedirect.com/science/article/B6TGJ-4CHS1T2-9/2/ca7fc5b9431830386384db78e7ee233a}, volume = {155-156}, year = 2004 }