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Optimization of Metal Removal Rate, Surface Roughness, and Hardness Using the Taguchi Method in CNC Turning Machine

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Intelligent Data Engineering and Analytics (FICTA 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 371))

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Abstract

Due to the widespread use of automation in industrial operations and cutting machines, the manufacturing process requires high reliability modeling methods to predict output during operation. In this study, Taguchi method and regression analysis have been executed to investigate the influence of some machining parameters like cutting speed, feed rate, and depth of cut on the surface coarseness, material removal rate, and hardness in CNC machining of steel AISI 1025. Different experiments were carried out using L25 by (CNC) machining. Analysis of variance (ANOVA) was applied to evaluate the impact of machining parameters on coarseness material removal rate and hardness. The results of analysis indicate that the feed rate, cutting speed, and depth of cut were the dominant parameters affecting on surface roughness (Ra), hardness, and metal removal rate (MRR), respectively; in addition, the experimental values and predicted value are very close each others.

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Authors and Affiliations

  1. Department of Medical Devices Technology Engineering, College of Engineering Technology, National University of Science and Technology, Dhi Qar, Iraq

    Zahraa N. Abdul Hussain & Mohammed Jameel Alsalhy

Authors
  1. Zahraa N. Abdul Hussain
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  2. Mohammed Jameel Alsalhy
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Corresponding author

Correspondence to Zahraa N. Abdul Hussain .

Editor information

Editors and Affiliations

  1. Department of Electronics Engineering, Faculty of Engineering and Technology (UNSIET), Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India

    Vikrant Bhateja

  2. Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Fiona Carroll

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    João Manuel R. S. Tavares

  4. Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Sandeep Singh Sengar

  5. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Peter Peer

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Hussain, Z.N.A., Alsalhy, M.J. (2023). Optimization of Metal Removal Rate, Surface Roughness, and Hardness Using the Taguchi Method in CNC Turning Machine. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_35

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