Combined Intelligent and Adaptive Optimization in End Milling of Multi-layered 16MnCr5/316L

  • Uros ZuperlEmail author
  • Franc Cus
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 949)


In this work, a new intelligent and adaptive optimization for end milling of four-layer functionally graded steel is presented in order to maximize the machining performance, minimize the production costs, and maximize the metal removal rate and ensuring the surface quality requirements. The proposed optimization consists of intelligent modeling of cutting quantities and particle swarm optimization (PSO) algorithm. Particle swarm optimization method is employed in real time to find optimum cutting conditions considering the real tool wear. Adaptive neural inference system is used to predict the tool flank wear timely on the basis of estimated cutting forces. Cutting forces and finally surface roughness were estimated during machining by using artificial neural networks (ANNs). The experimental results show that the proposed approach found an optimal solution of cutting conditions which improved the metal removal rate and improved the machining performance for 24% compared to conventional machining with off-line optimized parameters.


Adaptive optimization Cutting conditions Ball-end milling Multi-layered metal material Flank wear PSO ANFIS Neural networks 


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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Faculty of Mechanical EngineeringUniversity of MariborMariborSlovenia

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