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Investigations into high-speed rough and finish end-milling of hardened EN24 steel for implementation of control strategies

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Abstract

High-speed end-milling is used for production of variety of parts, dies, and molds made of hardened EN24 steel which are widely used in power and transport industries. Since desired productivity and quality are important in these industries, different strategies are needed for rough and finish end-milling operations. In this paper, a framework is presented for integrating different requirements of high-speed end-milling. In flat end-milling experiments, slots are machined in hardened EN24 steel using single insert cutter under different sets of cutting parameters for roughing and finishing operations. For rough end-milling, the responses such as material removal volume, tool wear and cutting forces are measured with respect to cutting time. A response surface is developed to predict material removal volume and a set of cutting parameters is selected for a given range of material removal volume using differential evolution (DE) algorithm till the tool wear reaches certain value. The experimental data is also used to develop Bayesian-based artificial neural network (ANN) model. Using this ANN model, reference values for cutting force and cutting time are generated for rough end-milling. Similarly, DE is used to predict a set of cutting parameters for a given range of surface roughness using response surface model. The reference cutting force is obtained for finish end-milling using ANN model. These reference values are useful in the monitoring and implementation of control strategy for the high-speed end-milling operations.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, 600036, India

    S. Saikumar & M. S. Shunmugam

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  1. S. Saikumar
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  2. M. S. Shunmugam
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Correspondence to M. S. Shunmugam.

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Saikumar, S., Shunmugam, M.S. Investigations into high-speed rough and finish end-milling of hardened EN24 steel for implementation of control strategies. Int J Adv Manuf Technol 63, 391–406 (2012). https://doi.org/10.1007/s00170-012-3915-8

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  • DOI: https://doi.org/10.1007/s00170-012-3915-8

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