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Modelling and simultaneous optimization of environmental, economic, and technological factors in machining

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Abstract

In the current era, manufacturing industries are facing multifaceted challenges related to increasing environmental awareness, decreasing economic gains, and technology obsolesce. These challenges become more apparent during the machining of difficult-to-machine materials due to high tool wear rates, high cutting forces, undesirable surface quality, high tool replacement costs, and a stagnating productivity. The developed approach aims at improving environmental, economic, and technological factors by optimizing four performance characteristics–energy demand, surface roughness, tool wear, and material removal rate during the milling of H13 tool steel by using an integrated artificial neural network and genetic algorithm. The proposed methodology provides Pareto solutions for minimum energy demand, surface roughness, & tool wear, and maximum material removal rate. The novelty of this work lies in generating Pareto fronts for analyzing conflicting responses, and determining preferred solutions without sacrificing environmental, technological, and economic considerations, simultaneously. The present work will be significant to practitioners in adopting better management strategies and simultaneously dealing with these challenges. The potential of the research lies in directly integrating the proposed optimization module with the machine tool system to serve as an online tool for machine tool process optimization.

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Data availability

The datasets generated and/or analysed during the current study are available upon reasonable request from the corresponding author.

Abbreviations

AMGA:

Archive-based micro-genetic algorithm

ANN:

Artificial neural network

CNC:

Computer numerical control

DFA:

Desirability function approach

ED:

Energy demand

GA:

Genetic algorithm

GRA:

Grey relational analysis

HRC:

Hardness Rockwell C

LDPS:

Linear decreasing particle swarm

AHP:

Analytic hierarchy process

LM:

Levenberg Marquardt

MAPE:

Mean absolute prediction error

MQL:

Minimum quantity lubrication

MRR:

Material removal rate

PCA:

Principal component analysis

Ra :

Average surface roughness

Rz :

Mean roughness depth

RSM:

Response surface method

TW:

Tool wear

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Authors and Affiliations

  1. Birla Institute of Technology and Science Pilani, Pilani Campus, Pilani, 333031, India

    Kuldip Singh Sangwan, Rishi Kumar, Vijaypal Poonia & Rakhee Kulshrestha

  2. Technische Universität Braunschweig–Institute of Machine Tools and Production Technology (IWF), Langer Kamp 19B, 38106, Brunswick, Germany

    Christoph Herrmann

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  1. Kuldip Singh Sangwan
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Sangwan, K.S., Kumar, R., Herrmann, C. et al. Modelling and simultaneous optimization of environmental, economic, and technological factors in machining. Int J Interact Des Manuf 18, 859–877 (2024). https://doi.org/10.1007/s12008-023-01569-1

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